chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# SkillOpt Environment Variables
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# Copy this file to .env and fill in your values.
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# Usage: set -a; source .env; set +a
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# ── Azure OpenAI (required for openai_chat backend) ──────────────────
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export AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
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export AZURE_OPENAI_API_VERSION=2024-12-01-preview
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# Authentication: choose one method
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||||
# Option 1: API Key
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export AZURE_OPENAI_API_KEY=
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# Option 2: Azure CLI (no API key needed, recommended on Azure VMs)
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# export AZURE_OPENAI_AUTH_MODE=azure_cli
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# Option 3: Managed Identity
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# export AZURE_OPENAI_AUTH_MODE=managed_identity
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# export AZURE_OPENAI_MANAGED_IDENTITY_CLIENT_ID=your-client-id
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# ── OpenAI-compatible endpoints ──────────────────────────────────────
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# Set AUTH_MODE to openai_compatible and reuse AZURE_OPENAI_ENDPOINT / _API_KEY.
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# The plain OpenAI client is used; no Azure auth, no api-version header.
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# export AZURE_OPENAI_ENDPOINT=https://api.openai.com/v1
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# export AZURE_OPENAI_API_KEY=sk-...
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# export AZURE_OPENAI_AUTH_MODE=openai_compatible
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# ── Anthropic / Claude (for claude_chat backend) ─────────────────────
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# export ANTHROPIC_API_KEY=sk-ant-...
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# ── Qwen Local Model (for qwen_chat backend) ────────────────────────
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# export QWEN_CHAT_BASE_URL=http://localhost:8000/v1
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# export QWEN_CHAT_MODEL=Qwen/Qwen3.5-4B
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# ── MiniMax (for minimax_chat backend) ──────────────────────────────
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# export MINIMAX_BASE_URL=https://api.minimax.io/v1
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# export MINIMAX_API_KEY=...
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# export MINIMAX_MODEL=MiniMax-M2.7
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+66
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__pycache__/
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*.pyc
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*.egg-info/
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build/
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dist/
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site/
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|
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data/*
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!data/README.md
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!data/searchqa_id_split/
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!data/searchqa_id_split/**
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!data/livemathematicianbench_id_split/
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!data/livemathematicianbench_id_split/**
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!data/docvqa_id_split/
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!data/docvqa_id_split/**
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!data/officeqa_id_split/
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!data/officeqa_id_split/**
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!data/spreadsheetbench_id_split/
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!data/spreadsheetbench_id_split/**
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!data/alfworld_path_split/
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!data/alfworld_path_split/**
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outputs/
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logs/
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external/
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# SkillOpt-Sleep runtime state (staging proposals, config, diagnostics, cron logs)
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.skillopt-sleep/
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# SkillOpt-Sleep handoff-backend round data (prompts/answers derived from transcripts)
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.skillopt-sleep-handoff/
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.skillopt-sleep-handoff.night*.done/
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|
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/BabyVision/
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/MMRB/
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/SpreadsheetBench/
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/dl4ir-searchQA/
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|
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configs/local/
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configs/**/*.local.yaml
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*.local.md
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*.secret.md
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*.bak
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|
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.env
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.secrets/
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.codex_azure*/
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# Internal docs (not for open-source release)
|
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docs/ablation_plan.md
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docs/ablation_paper_tables.md
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docs/ablation_paper_tables.html
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docs/experiment_commands.md
|
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docs/slow_update_flowchart.md
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docs/session_memory.md
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docs/harness_fresh_machine_handoff.md
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docs/harness_monitoring_memory.md
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docs/harness_reproduction_secrets.secret.md
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docs/reflact_conda_env_export.yml
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docs/reflact_overview.html
|
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docs/render_ablation_paper_tables.py
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docs/让*
|
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.gradio/
|
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.venv
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|
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# Local experiment launchers — contain machine-specific endpoints/identities, never commit
|
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tests/run_*.sh
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tests/launch_*.py
|
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*.launch.log
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+113
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# Changelog
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All notable changes to SkillOpt are documented here. This project adheres to
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[Semantic Versioning](https://semver.org/) and the format is based on
|
||||
[Keep a Changelog](https://keepachangelog.com/).
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|
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## [Unreleased]
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|
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### Added
|
||||
- **Handoff backend** (`--backend handoff`) for SkillOpt-Sleep — runs the
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sleep cycle with no model subprocess or API key: the engine writes each
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pending model call to `PROMPTS.md`/`pending.json` (exit code 3) and the
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user's own agent session answers into `answers/<id>.md`; re-running the
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same command resumes statelessly from the answers (typically 3–6 rounds
|
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per night). Mined tasks are pinned per night so answering sessions cannot
|
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shift the task set. Ships a `/skillopt-sleep-handoff` Claude Code command
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that automates the loop with fresh-context subagents to protect the
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held-out gate.
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|
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## [0.2.0] — 2026-07-02
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|
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The headline of this release is **SkillOpt-Sleep**: a nightly offline
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self-evolution engine that harvests a coding agent's real session
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transcripts, mines recurring tasks, replays them offline, and consolidates
|
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short-term experience into long-term memory and skills — all behind the same
|
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held-out validation gate that keeps SkillOpt training honest. It ships as a
|
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decoupled top-level package (`skillopt_sleep/`, zero dependency on the
|
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research code) and as the new `skillopt-sleep` CLI.
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|
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### Added
|
||||
- **SkillOpt-Sleep engine** — nightly offline self-evolution cycle
|
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(harvest → mine → replay → consolidate) behind a validation gate, exposed
|
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as the `skillopt-sleep` console script and `python -m skillopt_sleep`.
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- Multi-objective reward (accuracy / tokens / latency) with user preferences.
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- Multi-rollout contrastive reflection under a token/time budget.
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- Experience replay + controllable dream rollouts (opt-in).
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- Slow-update long-term memory field (runs even with the gate off).
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- 3-way train/val/test split with `gate_mode on|off`.
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- Verifier-discipline validation gate, with a stress-test suite
|
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(thanks @Tanmay9223, #87).
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- **Cross-tool backends & plugin shells** for Claude Code, Codex, Copilot,
|
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Devin, and OpenClaw:
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- Codex Desktop transcript harvesting, skill-first Codex integration, and a
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reviewed task-file flow (thanks @Kirchberg, #48, #49, #60).
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- GitHub Copilot backend (`CopilotCliBackend`) + research-engine MCP plugin
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||||
(thanks @Dongbumlee, #50).
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- Devin plugin: MCP server + ATIF-v1.7 harvest (thanks @xerxes-y, #88).
|
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- OpenClaw shell for SkillOpt-Sleep (thanks @Elzlxx, #59).
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- **SearchQA** split materialization helper and fail-fast on systemic rollout
|
||||
failures, with a `searchqa` install extra (thanks @summerview1997,
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||||
#63, #64, #65).
|
||||
- WebUI environment loading and backend preflight (thanks @summerview1997, #63).
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|
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### Changed
|
||||
- Decoupled the Sleep engine into a standalone top-level `skillopt_sleep/`
|
||||
package with zero dependency on the research code.
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||||
- Made `EnvAdapter.reflect` a shared default so reflect kwargs are no longer
|
||||
dropped (thanks @imshunsuke, #44).
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||||
- English-only pass across the engine, plugins, and docs.
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|
||||
### Fixed
|
||||
- Windows robustness for the Claude/Codex backends, plus a hardened JSON
|
||||
fallback path (thanks @Yif-Yang, #79).
|
||||
- Reject prose pseudo-JSON wrapped in single quotes/backticks (#82).
|
||||
- Surface Codex auth/model/version failures instead of silently scoring 0
|
||||
(thanks @dmmdea, #92).
|
||||
- Redact secrets before persisting cycle diagnostics.
|
||||
- Configure the `qwen_chat`/`minimax` backends so local LLM endpoints work
|
||||
(thanks @imrehg, #85).
|
||||
- Forward the Qwen target timeout and gate `enable_thinking` for vLLM targets
|
||||
(thanks @mvanhorn, #40).
|
||||
- Make `--bare` conditional on `ANTHROPIC_API_KEY` (#68), add a
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||||
`SKILLOPT_SLEEP_PYTHON` override with a lookback-hours first-run fallback
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||||
(#74), and fix ALFWorld gamefile paths relative to `ALFWORLD_DATA`.
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### Packaging
|
||||
- Bump `skillopt`, `skillopt.__version__`, and `skillopt_sleep.__version__`
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to `0.2.0`.
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||||
- Restore `skillopt_webui` to the built wheel (it was dropped when the
|
||||
`packages.find` include list was made explicit).
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||||
- Add the `searchqa` extra and include `json_repair` in the `claude`, `qwen`,
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||||
and `all` extras.
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||||
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||||
### Acknowledgements 🙏
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v0.2.0 landed thanks to our community contributors — thank you!
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||||
|
||||
- @Kirchberg — Codex Desktop harvesting, skill-first Codex integration,
|
||||
reviewed task-file flow (#48, #49, #60)
|
||||
- @Dongbumlee — GitHub Copilot backend + research-engine MCP plugin (#50)
|
||||
- @summerview1997 — SearchQA materialization, rollout fail-fast, WebUI
|
||||
preflight (#63, #64, #65)
|
||||
- @xerxes-y — Devin plugin: MCP server + ATIF-v1.7 harvest (#88)
|
||||
- @Elzlxx — OpenClaw shell for SkillOpt-Sleep (#59)
|
||||
- @imshunsuke — shared `EnvAdapter.reflect` default + docs fixes (#43, #44)
|
||||
- @mvanhorn — Qwen timeout forwarding + `enable_thinking` gating (#40)
|
||||
- @dmmdea — surface Codex auth/model/version failures (#92)
|
||||
- @Tanmay9223 — verifier-discipline stress test (#87)
|
||||
- @imrehg — `configure_qwen_chat` for local LLM endpoints (#85)
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||||
- @samuelgoofus-boop — community contributions
|
||||
|
||||
Special thanks to @Yif-Yang for driving the SkillOpt-Sleep engine.
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||||
|
||||
**Full changelog:** https://github.com/microsoft/SkillOpt/compare/v0.1.0...v0.2.0
|
||||
|
||||
## [0.1.0] — 2026-06-02
|
||||
|
||||
Initial public release: the full training loop (rollout → reflect →
|
||||
aggregate → select → update → evaluate), multi-backend support
|
||||
(OpenAI / Azure / Claude / Qwen / MiniMax), six built-in benchmarks, and the
|
||||
WebUI dashboard.
|
||||
|
||||
[0.2.0]: https://github.com/microsoft/SkillOpt/releases/tag/v0.2.0
|
||||
[0.1.0]: https://github.com/microsoft/SkillOpt/releases/tag/v0.1.0
|
||||
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# Contributing to SkillOpt
|
||||
|
||||
Thank you for your interest in contributing! SkillOpt welcomes contributions of all kinds.
|
||||
|
||||
## Getting Started
|
||||
|
||||
```bash
|
||||
git clone https://github.com/microsoft/SkillOpt.git
|
||||
cd SkillOpt
|
||||
pip install -e ".[dev]"
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||||
```
|
||||
|
||||
## How to Contribute
|
||||
|
||||
### 🐛 Bug Reports
|
||||
Open a GitHub issue with reproduction steps, expected/actual behavior, and your config file (remove API keys).
|
||||
|
||||
### 🔧 Add a Benchmark
|
||||
See the [guide](docs/guide/new-benchmark.md) and use the scaffold at `skillopt/envs/_template/`.
|
||||
|
||||
### 🤖 Add a Model Backend
|
||||
See the [guide](docs/guide/new-backend.md).
|
||||
|
||||
### 📝 Improve Documentation
|
||||
```bash
|
||||
pip install -e ".[docs]"
|
||||
mkdocs serve # Preview at http://localhost:8000
|
||||
```
|
||||
|
||||
## Pull Request Process
|
||||
|
||||
1. Fork the repo and create a feature branch
|
||||
2. Make changes and test with an existing benchmark
|
||||
3. Submit a PR with a clear description
|
||||
4. Ensure CI passes
|
||||
|
||||
## Code Style
|
||||
- Follow existing patterns in the codebase
|
||||
- Use type hints for function signatures
|
||||
- Keep docstrings concise
|
||||
|
||||
## License
|
||||
By contributing, you agree your contributions are licensed under the [MIT License](LICENSE).
|
||||
@@ -0,0 +1,21 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2026 Microsoft Corporation
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
@@ -0,0 +1,107 @@
|
||||
# SkillOpt: Executive Strategy for Self-Evolving Agent Skills
|
||||
|
||||
*Train agent skills like you train neural networks — with epochs, (mini-)batchsize, learning rates, and validation gates — but without touching model weights.*
|
||||
|
||||
[](https://microsoft.github.io/SkillOpt/) [](https://arxiv.org/abs/2605.23904) [](https://youtu.be/JUBMDTCiM0M) [](https://pypi.org/project/skillopt/) [](https://www.python.org/) [](LICENSE)
|
||||
|
||||
<p align="center">
|
||||
<a href="https://trendshift.io/repositories/38498?utm_source=trendshift-badge&utm_medium=badge&utm_campaign=badge-trendshift-38498" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/trendshift/repositories/38498/daily?language=Python" alt="microsoft%2FSkillOpt | Trendshift" width="250" height="55"/></a>
|
||||
<a href="https://trendshift.io/repositories/38498?utm_source=trendshift-badge&utm_medium=badge&utm_campaign=badge-trendshift-38498" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/trendshift/repositories/38498/weekly?language=Python" alt="microsoft%2FSkillOpt | Trendshift" width="250" height="55"/></a>
|
||||
</p>
|
||||
|
||||
> 📖 **For installation, data preparation, training/eval commands, the full configuration reference, and framework internals, see the [Documentation & Reproduction Guide](https://microsoft.github.io/SkillOpt/docs/guideline.html)** (rendered on GitHub Pages).
|
||||
|
||||
---
|
||||
|
||||
## News 🔥🔥🔥
|
||||
- **[2026-07-02]** 🚀 **SkillOpt [v0.2.0](https://github.com/microsoft/SkillOpt/releases/tag/v0.2.0) is out on [PyPI](https://pypi.org/project/skillopt/)!** Headline feature: **SkillOpt-Sleep**, a nightly offline self-evolution engine (harvest → mine → replay → consolidate, all behind a held-out validation gate) with multi-objective reward, experience replay + dream rollouts, and long-term memory — now shipped as the `skillopt-sleep` CLI. This release also adds cross-tool backends and plugin shells for **Claude, Codex, Copilot, Devin, and OpenClaw**, SearchQA split materialization, Windows robustness, and hardened JSON parsing. See the [release notes](https://github.com/microsoft/SkillOpt/releases/tag/v0.2.0) for the full changelog and contributor acknowledgements.
|
||||
- **[2026-06-15]** 😴 **SkillOpt-Sleep (preview)** — a nightly offline self-evolution companion for local coding agents (Claude Code / Codex / Copilot): review past sessions, replay recurring tasks, and consolidate validated skills behind a held-out gate. See **[`docs/sleep/README.md`](docs/sleep/README.md)** for what it is, how to use it, and results.
|
||||
- **[2026-06-03]** 🎉 **[gbrain](https://github.com/garrytan/gbrain), [gbrain-evals](https://github.com/garrytan/gbrain-evals/blob/main/docs/benchmarks/2026-06-03-skillopt.md), and [darwin-skill](https://github.com/alchaincyf/darwin-skill) have all integrated SkillOpt.**
|
||||
- **[2026-06-02]** 🎉 **SkillOpt [v0.1.0](https://github.com/microsoft/SkillOpt/releases/tag/v0.1.0) is now available on [PyPI](https://pypi.org/project/skillopt/)!** Install with `pip install skillopt`. This initial release includes the full training loop (rollout → reflect → aggregate → select → update → evaluate), multi-backend support (OpenAI / Azure / Claude / Qwen / MiniMax), six built-in benchmarks, and WebUI dashboard.
|
||||
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
Modern agent skills are usually hand-crafted, generated one-shot by a strong
|
||||
LLM, or evolved through loosely controlled self-revision — none of which
|
||||
behaves like a deep-learning optimizer for the skill itself, and none of
|
||||
which reliably improves over its starting point under feedback.
|
||||
|
||||
**SkillOpt treats the skill document as the trainable state of a frozen
|
||||
agent**, and trains it with the discipline that makes weight-space
|
||||
optimization reproducible. A separate optimizer model turns scored rollouts
|
||||
into bounded add / delete / replace edits on a single skill document; a
|
||||
candidate edit is accepted only when it strictly improves a held-out
|
||||
validation score. A textual learning-rate budget, a rejected-edit buffer,
|
||||
and an epoch-wise slow / meta update make skill training stable while
|
||||
adding **zero inference-time model calls** at deployment.
|
||||
|
||||
The deployed artifact is a compact `best_skill.md` (typically 300–2,000
|
||||
tokens) that runs against the unchanged target model. Across **six
|
||||
benchmarks, seven target models, and three execution harnesses** (direct
|
||||
chat, Codex CLI, Claude Code CLI), SkillOpt is best or tied-best on **all
|
||||
52 evaluated (model, benchmark, harness) cells** and on GPT-5.5 lifts the
|
||||
average no-skill accuracy by **+23.5 points in direct chat, +24.8 inside
|
||||
the Codex agentic loop, and +19.1 inside Claude Code**. Optimized skill
|
||||
artifacts transfer across model scales, between Codex and Claude Code
|
||||
harnesses, and to nearby benchmarks without further optimization.
|
||||
|
||||
For the full method, ablations, and per-cell results see the [paper](https://arxiv.org/abs/2605.23904); for a visual walkthrough of the loop see the [project page](https://microsoft.github.io/SkillOpt/); for deeper API / backend / benchmark docs see [`docs/`](docs/).
|
||||
|
||||
## 🎬 Demo Video
|
||||
|
||||
https://github.com/user-attachments/assets/eb12d3bc-371c-467f-904d-91b61f339ed7
|
||||
|
||||
<p align="center">
|
||||
<a href="https://youtu.be/JUBMDTCiM0M"><b>▶ Watch the full demo on YouTube</b></a>
|
||||
</p>
|
||||
|
||||
---
|
||||
|
||||
## Extensibility & WebUI
|
||||
|
||||
### Adding a new backend
|
||||
|
||||
A backend = a chat / exec target (e.g. `openai_chat`, `claude_chat`,
|
||||
`qwen_chat`, `minimax_chat`, `codex_exec`, `claude_code_exec`). See
|
||||
[`docs/guide/new-backend.md`](docs/guide/new-backend.md) for the full
|
||||
contract; in short you add a `skillopt/model/<name>_backend.py` module,
|
||||
register it in `skillopt/model/common.py` + `backend_config.py`, and wire
|
||||
it through the router in `skillopt/model/__init__.py`. `qwen_backend.py`
|
||||
and `minimax_backend.py` are good templates.
|
||||
|
||||
### Adding a new benchmark
|
||||
|
||||
A benchmark = a `skillopt/envs/<name>/` package with a `dataloader.py`, a
|
||||
`rollout.py`, and an `initial.md` seed skill. See
|
||||
[`docs/guide/new-benchmark.md`](docs/guide/new-benchmark.md) for the full
|
||||
contract; the simplest reference is `skillopt/envs/searchqa/`.
|
||||
|
||||
### WebUI
|
||||
|
||||
Launch the monitoring dashboard (optional):
|
||||
|
||||
```bash
|
||||
pip install -e ".[webui]"
|
||||
python -m skillopt_webui.app
|
||||
```
|
||||
|
||||
| Flag | Default | Description |
|
||||
|---|---|---|
|
||||
| `--port` | 7860 | Server port |
|
||||
| `--host` | `0.0.0.0` | Bind address |
|
||||
| `--share` | off | Create a public Gradio share link |
|
||||
|
||||
---
|
||||
|
||||
## Citation
|
||||
|
||||
```bibtex
|
||||
@article{yang2026skillopt,
|
||||
title={Skillopt: Executive strategy for self-evolving agent skills},
|
||||
author={Yang, Yifan and Gong, Ziyang and Huang, Weiquan and Yang, Qihao and Zhou, Ziwei and Huang, Zisu and Li, Yan and Gao, Xuemei and Dai, Qi and Liu, Bei and others},
|
||||
journal={arXiv preprint arXiv:2605.23904},
|
||||
year={2026}
|
||||
}
|
||||
```
|
||||
@@ -0,0 +1,7 @@
|
||||
# WeHub 来源说明
|
||||
|
||||
- 原始项目：`microsoft/SkillOpt`
|
||||
- 原始仓库：https://github.com/microsoft/SkillOpt
|
||||
- 导入方式：上游默认分支的最新快照
|
||||
- 原作者、版权和许可证信息以原始仓库及本仓库 LICENSE 为准
|
||||
- 本文件仅用于记录来源，不代表 WeHub 是原项目作者
|
||||
+14
@@ -0,0 +1,14 @@
|
||||
<!-- BEGIN MICROSOFT SECURITY.MD V1.0.0 BLOCK -->
|
||||
|
||||
## Security
|
||||
|
||||
Microsoft takes the security of our software products and services seriously, which
|
||||
includes all source code repositories in our GitHub organizations.
|
||||
|
||||
**Please do not report security vulnerabilities through public GitHub issues.**
|
||||
|
||||
For security reporting information, locations, contact information, and policies,
|
||||
please review the latest guidance for Microsoft repositories at
|
||||
[https://aka.ms/SECURITY.md](https://aka.ms/SECURITY.md).
|
||||
|
||||
<!-- END MICROSOFT SECURITY.MD BLOCK -->
|
||||
@@ -0,0 +1,79 @@
|
||||
# Paper-aligned SkillOpt reference skills (GPT-5.5)
|
||||
|
||||
This folder provides a subset of the paper's main Table 1 GPT-5.5 optimized
|
||||
skills as reference artifacts — one `gpt5.5_skill.md` per currently included
|
||||
benchmark. You can plug them into `scripts/eval_only.py` to evaluate the
|
||||
provided skills on a given split without re-running the training loop.
|
||||
|
||||
> These are checkpoints associated with the paper, not a general-purpose
|
||||
> tool. They're here so you can verify the reported numbers and use the
|
||||
> skills as portable artifacts. If you want to *train* your own skill,
|
||||
> use `scripts/train.py` per the top-level README.
|
||||
>
|
||||
> This is the first artifact batch. We plan to continue uploading the
|
||||
> remaining optimized skills and benchmark split manifests as they are
|
||||
> cleaned and verified.
|
||||
|
||||
## What's here
|
||||
|
||||
| Benchmark | Skill artifact | Matching config |
|
||||
|---|---|---|
|
||||
| SearchQA | `ckpt/searchqa/gpt5.5_skill.md` | `configs/searchqa/default.yaml` |
|
||||
| ALFWorld | `ckpt/alfworld/gpt5.5_skill.md` | `configs/alfworld/default.yaml` |
|
||||
| DocVQA | `ckpt/docvqa/gpt5.5_skill.md` | `configs/docvqa/default.yaml` |
|
||||
| LiveMathematicianBench | `ckpt/livemath/gpt5.5_skill.md` | `configs/livemathematicianbench/default.yaml` |
|
||||
| OfficeQA | `ckpt/officeqa/gpt5.5_skill.md` | `configs/officeqa/default.yaml` |
|
||||
| SpreadsheetBench | `ckpt/spreadsheetbench/gpt5.5_skill.md` | `configs/spreadsheetbench/default.yaml` |
|
||||
|
||||
Each file is a plain Markdown skill document (~2k–13k chars). It contains a
|
||||
protected `SLOW_UPDATE` section at the end that holds epoch-wise
|
||||
longitudinal guidance — that's expected, not a formatting issue.
|
||||
|
||||
## How to evaluate a provided skill
|
||||
|
||||
`scripts/eval_only.py` runs a single skill against a data split without
|
||||
invoking the optimizer. Example for SearchQA against the test split:
|
||||
|
||||
```bash
|
||||
python scripts/eval_only.py \
|
||||
--config configs/searchqa/default.yaml \
|
||||
--skill ckpt/searchqa/gpt5.5_skill.md \
|
||||
--split valid_unseen \
|
||||
--split_dir data/searchqa_id_split \
|
||||
--azure_openai_endpoint https://your-resource.openai.azure.com/ \
|
||||
--target_model gpt-5.5
|
||||
```
|
||||
|
||||
Substitute the benchmark, config, skill path, and `--split_dir` to evaluate
|
||||
any of the other five. `--split valid_unseen` is the test split, `valid_seen`
|
||||
is the selection / validation split, `train` is the training split, and
|
||||
`all` runs all three.
|
||||
|
||||
## On comparing to the paper numbers
|
||||
|
||||
To compare against the paper-reported cells, use the same dataset split and
|
||||
scorer. SearchQA's split is checked in at `data/searchqa_id_split/` (400
|
||||
train / 200 selection / 1400 test). For the other benchmarks, point
|
||||
`--split_dir` at your own materialized split; the loader is deterministic
|
||||
from `split_seed` (default `42`) + `split_ratio` (default `2:1:7`) when
|
||||
`split_mode: ratio` is used, so a given `data_path` + seed reproduces
|
||||
across machines. Explicit per-benchmark split manifests are being prepared
|
||||
for upload — see issues #14 and #21.
|
||||
|
||||
## Why force-accept vs. gated slow-update matters
|
||||
|
||||
These `ckpt/` skills were produced with the gated slow-update semantics
|
||||
described in paper Section 3.6:
|
||||
|
||||
```yaml
|
||||
optimizer:
|
||||
slow_update_gate_with_selection: true
|
||||
```
|
||||
|
||||
Current `main` defaults to `false` (force-accept mode), a newer
|
||||
post-submission behavior where the slow-update guidance is written into
|
||||
`current_skill` and `best_skill` unconditionally at the epoch boundary. If
|
||||
you re-train with the current default, you may produce a *different*
|
||||
`best_skill.md` than the one checked in here. Both modes are supported;
|
||||
see the top-level README's "Configuration -> Slow-update acceptance mode"
|
||||
section.
|
||||
@@ -0,0 +1,113 @@
|
||||
# ALFWorld Embodied Agent Skill
|
||||
|
||||
## Overview
|
||||
This skill guides agents operating in the ALFWorld text-based embodied environment.
|
||||
The agent must complete household tasks by navigating rooms, interacting with objects,
|
||||
and using appliances. Actions must be chosen from the admissible action list provided
|
||||
at each step.
|
||||
|
||||
**Output format**: Always output `<think>...</think>` for reasoning, then `<action>...</action>` for the chosen action.
|
||||
|
||||
---
|
||||
|
||||
## Task Types
|
||||
|
||||
| Type | Goal | Key Steps |
|
||||
|------|------|-----------|
|
||||
| Pick & Place | Put object X in/on receptacle Y | Find X -> take X -> go to Y -> put X in/on Y |
|
||||
| Pick Two & Place | Put two instances of X in/on Y | Find X1 -> take -> place -> find X2 -> take -> place |
|
||||
|
||||
### Pick Two Object Bookkeeping
|
||||
For `pick_two_obj_and_place`, choose one destination receptacle instance once it is opened/usable, and remember it as the target. Both object instances should be placed into that same remembered receptacle. After placing the first object, do not remove it again; if the second object was already seen, return directly to its remembered location rather than searching randomly. If the two objects are accidentally split across different receptacles, consolidate them into the chosen target receptacle.
|
||||
| Examine in Light | Examine object X under desklamp | Find X -> take X -> find desklamp -> use desklamp |
|
||||
|
||||
| Examine in Light detail | Final interaction | While holding X where a desklamp is visible, use the desklamp; do not try to place X on the lamp first. |
|
||||
| Clean & Place | Clean object X and put in/on Y | Find X -> take X -> go to sink -> clean X -> go to Y -> put X |
|
||||
| Heat & Place | Heat object X and put in/on Y | Find X -> take X -> go to microwave -> heat X -> go to Y -> put X |
|
||||
| Cool & Place | Cool object X and put in/on Y | Find X -> take X -> go to fridge -> cool X -> go to Y -> put X |
|
||||
|
||||
---
|
||||
|
||||
## General Principles
|
||||
|
||||
1. **Decompose the task**: Parse the goal into ordered sub-goals (locate, acquire, transform, deliver). Complete each before moving to the next.
|
||||
2. **Systematic exploration**: Search each surface and container exactly once before revisiting. Open closed containers (drawers, cabinets, fridge) before judging them empty.
|
||||
|
||||
- Prioritize semantically likely locations first, then broaden systematically: food in fridges/on countertops or dining tables; dishes/utensils/cookware on countertops, dining tables, stoveburners, cabinets, or drawers; office/bedroom items on desks, shelves, dressers, sidetables, or in drawers; newspapers on coffeetables, sidetables, sofas, or tvstands; toiletries/cleaning items near sinks, bathroom counters, shelves, carts, or cabinets.
|
||||
|
||||
- For portable kitchen targets such as bread, mugs, cups, plates, bowls, and utensils, check broad exposed surfaces early: after one or two empty countertops, try dining tables or other open surfaces before opening many cabinets/drawers. For small office/bedroom targets, alternate drawers with exposed desks, shelves, sidetables, and dressers rather than exhausting drawers first.
|
||||
|
||||
- Keep a persistent **searched set** of receptacle instances, e.g. `drawer 1`, `shelf 3`, `countertop 2`. Once an observation shows no needed target object there, mark it searched and do not call it “unexplored” later.
|
||||
- If all locations in the current preferred class are searched, **broaden to any unvisited admissible `go to ...` location** instead of restarting the same sequence. Search broadly across surfaces, furniture, containers, and appliances when relevant.
|
||||
- If a visible object is itself an openable/container-like object, such as a box, and opening/examining it is admissible, inspect it before leaving the area.
|
||||
3. **Grab immediately**: When a required object is visible and reachable, take it right away before moving elsewhere.
|
||||
|
||||
- Pick up only the exact requested object type. Similar or related objects, such as a cup when the task asks for a mug, a spoon when it asks for a knife, or a pot when it asks for a pan, are distractors; leave them in place and mark that location searched for the target.
|
||||
4. **Transform before placing**: If the task requires cleaning, heating, or cooling, perform the state change at the appropriate appliance before heading to the final destination.
|
||||
|
||||
- Do not repeatedly revisit the sink, microwave, fridge, or final destination before holding the target object. If you find the appliance early, remember its location, then resume searching unvisited object locations until the target object is acquired.
|
||||
|
||||
- Use direct admissible appliance/tool commands immediately when available, such as `clean X with sinkbasin`, `heat X with microwave`, `cool X with fridge`, or `use desklamp`. Do not waste steps opening, closing, toggling, or examining the appliance unless the needed action is unavailable or opening is required for searching/placing.
|
||||
5. **Direct delivery**: Once holding the transformed (or untransformed) goal object, navigate straight to the target receptacle and place it.
|
||||
|
||||
- Remember known destination receptacles and return directly to the same instance after pickup/transformation. If the destination is also a semantically likely source location, check/open it early rather than only after exhaustive search: food may already be in the fridge, utensils may be on the diningtable, newspapers may be on/near the sofa, and a target drawer can be opened early for pick-two tasks. If the object starts at the destination but needs cleaning/heating/cooling, take it out, transform it, then return to that same instance and place it back.
|
||||
6. **Track progress**: Maintain an internal count of how many objects still need to be found and placed. Only stop searching when the count reaches zero.
|
||||
7. **Avoid loops**: Never repeat the same action more than twice in a row. If stuck, move to a different unexplored location.
|
||||
8. **Only choose admissible actions**: Always pick an action from the admissible action list. Do not invent actions.
|
||||
|
||||
---
|
||||
|
||||
## Common Mistakes to Avoid
|
||||
|
||||
- **Revisiting searched locations**: Keep track of which surfaces/containers have been checked; do not re-examine them.
|
||||
- **Ignoring visible objects**: If the target object appears in the observation, pick it up immediately.
|
||||
- **Skipping state changes**: Do not place an object at the destination without first cleaning/heating/cooling it when required.
|
||||
- **Premature termination**: Do not stop the episode until all goal conditions are verified as met.
|
||||
- **Action loops**: Repeatedly toggling or examining the same object wastes steps. Move on to new locations instead.
|
||||
|
||||
### Hard Search-Loop Recovery
|
||||
|
||||
- **Exact-instance lockout before pickup**: once a receptacle/surface instance has been observed and does not contain the target object, do not go back to that exact instance while still searching for the object. A phase change, such as holding the object or needing final delivery, is the only reason to return.
|
||||
- **Fast broadening threshold**: after 3-4 misses in the same receptacle class, switch to a different likely class or any unvisited admissible location instead of continuing or restarting that class, unless the target has already been seen there.
|
||||
- **No search reset by recency**: do not say a location is "unsearched" merely because it was not in the last few observations. The searched set is global for the whole episode.
|
||||
- **Finite-class exhaustion**: if all visible instances of a small class have been checked once, such as all stoveburners, diningtables, countertops, or shelves, mark that class exhausted for object search and do not start a second pass. Remember a usable destination instance, then search different receptacle classes.
|
||||
- **Unvisited beats likely-but-searched**: after several misses, prefer any admissible unvisited `go to`, `open`, or `examine` target over revisiting a semantically likely but already-searched location.
|
||||
- **Destination surfaces before pickup**: if the destination receptacle is also a likely object location, inspect each instance at most once before pickup. If it lacks the object, remember it as the final destination but stop using it as a search target until the object has been transformed and is ready to place.
|
||||
- **Kitchen item fallback**: for cookware and dishware, after checking obvious burners/tables/counters once, broaden to unsearched cabinets, drawers, shelves, sinkbasins, and other kitchen storage/surfaces rather than cycling among the obvious locations.
|
||||
|
||||
### Strict Search Ledger Action Filter
|
||||
|
||||
Before every empty-handed search action, apply this hard filter:
|
||||
|
||||
1. If a required target object is visible, take it immediately.
|
||||
2. Otherwise choose an exact receptacle/surface/container instance whose contents have not yet been observed in the current object-search phase.
|
||||
3. Reject any `go to`, `examine`, or `open` action for an exact instance already observed to lack the target, even if it is semantically likely, nearby, recently mentioned, or the final destination type.
|
||||
4. If all likely instances are rejected by the ledger, broaden to any unvisited admissible location/class instead of restarting from instance 1 of a searched class.
|
||||
|
||||
The searched ledger survives inventory checks, appliance visits, placing the first object in a pick-two task, and putting down an irrelevant inspected object/container. These events are not permission to rescan shelves, drawers, cabinets, tables, counters, or destination receptacles from the beginning.
|
||||
|
||||
### Destination-as-Source Lockout
|
||||
|
||||
When the final receptacle type is also a plausible source location, inspect each visible destination instance at most once before pickup. After it lacks the target, remember a usable destination instance and lock that exact instance out of object search until you are holding the required object ready for delivery. Do not alternate between destination instances and other searched source instances while still empty-handed.
|
||||
|
||||
### Pick-Two Phase Memory
|
||||
|
||||
After placing the first object in a pick-two task, do not begin a fresh room/class search. If another required instance was previously seen, return directly to that remembered source location for the second pickup. If no second instance is remembered, continue from the existing unsearched-location ledger rather than revisiting locations already checked before the first placement.
|
||||
|
||||
<!-- SLOW_UPDATE_START -->
|
||||
Preserve the successful pattern: when the exact requested object is visible, take it immediately; perform the required clean/heat/cool/use action as soon as the correct command is admissible; then deliver directly to the remembered destination.
|
||||
|
||||
Treat tool locations as tools, not repeated search targets. If a sinkbasin, fridge, microwave, desklamp, or destination receptacle has already been checked and does not contain the target while you are empty-handed, remember it for later but do not revisit it until you are holding the required object or ready to place/use it.
|
||||
|
||||
Use a next-unsearched-instance pointer for every numbered class. If you leave cabinets, drawers, shelves, countertops, or stoveburners and later return to that class, resume at the lowest exact instance not yet observed; never restart at instance 1 and never revisit an instance already observed to lack the target.
|
||||
|
||||
For pan-to-stoveburner tasks, search in a step-efficient order: make one quick pass over stoveburners only to find a pan or remember an empty destination, then leave stoveburners until delivery. Next check countertops/islands and sinkbasins. Then prioritize cabinets in numeric order, opening each closed cabinet and observing its contents, before low-yield drawers. Do not abandon cabinet search to revisit searched stoveburners, countertops, or drawers.
|
||||
|
||||
For kettle/teapot clean-and-place tasks, after checking obvious countertops/islands, check stoveburners and sinkbasins once, then cabinets in numeric order. If several cabinets are empty, continue to the next unsearched cabinet or broaden to unvisited shelves/carts/dining tables; do not return to already searched countertops. Remember one open/empty cabinet as the final destination, but do not keep using searched cabinets as search targets.
|
||||
|
||||
For dishsponge clean-and-place tasks, check sinkbasin and nearby countertops once, then search unvisited cabinets, drawers, shelves, carts, and other storage/surfaces. Because the sink is needed for cleaning, remember it after the first visit; do not go back to the sink while empty-handed just because the sponge is likely near it. Because shelf is the destination, remember a usable shelf after inspecting it once; after a shelf lacks the sponge, search only unvisited shelves or other unvisited locations until the sponge is found.
|
||||
|
||||
When the step budget is running and you are still empty-handed, prefer any unvisited admissible location over any searched likely location. A location being semantically likely, useful later, or recently mentioned is never a reason to rescan it before acquisition.
|
||||
|
||||
Do not let the broadening threshold cause class restarts. Broadening means move to a different unvisited class or continue at the next unsearched instance of a promising storage class; it never means cycling back through exact instances already observed.
|
||||
<!-- SLOW_UPDATE_END -->
|
||||
@@ -0,0 +1,26 @@
|
||||
# DocVQA Skill
|
||||
|
||||
## Visual Evidence Discipline
|
||||
- Read the document carefully before answering.
|
||||
- Prefer the smallest exact text span that answers the question.
|
||||
|
||||
- For questions asking for a value, count, page number, date, or graph reading, return only the requested value span; omit nearby labels, category names, units, or explanatory words unless the question explicitly asks for them.
|
||||
- When several nearby strings look similar, choose the one whose surrounding labels or layout best match the question.
|
||||
|
||||
## Exact Answer Discipline
|
||||
- Copy names, numbers, and dates exactly from the document whenever possible.
|
||||
|
||||
- Preserve the document's exact spelling and punctuation for names and quoted phrases; do not substitute similar letters or change straight/curly quotes, spacing, or parentheses when the visible text provides them.
|
||||
- Prefer direct extraction over paraphrase.
|
||||
- Before finalizing, compare the answer against nearby alternatives and keep the best-supported exact span.
|
||||
|
||||
## Structured Layout Lookup
|
||||
- For tables, first find the row or entry named in the question, then read the value under the requested column, header, date, or category; answer with that cell only.
|
||||
- For forms, receipts, or labeled fields, locate the exact role, party, or field label mentioned in the question, then copy the filled-in value from the same line, box, block, or immediately adjacent field.
|
||||
- For table-of-contents, indexed, numbered, or bulleted lists, match the requested title, entry, or point number, then follow the same line or list item to the associated value; do not take a nearby value from another item.
|
||||
|
||||
## Anchored Handwriting / Nearby Text
|
||||
- For handwritten or list/table questions with an anchor term, first locate the anchor, then inspect the immediately adjacent text in the same row, column, or nearby margin. If legible, provide the best-supported nearby span rather than leaving the answer blank.
|
||||
|
||||
<!-- SLOW_UPDATE_START -->
|
||||
<!-- SLOW_UPDATE_END -->
|
||||
@@ -0,0 +1,35 @@
|
||||
# Live Mathematical MCQ Heuristics
|
||||
|
||||
## Option Comparison
|
||||
|
||||
### Meta-Options About Stronger Results
|
||||
- Treat options of the form “one of the remaining options is correct, but a stronger result can be proven” as serious candidates, especially when the question asks for the strongest statement.
|
||||
- If a concrete option is true but your theorem or derivation gives a strictly stronger conclusion not exactly listed, choose the meta-option rather than the weaker concrete statement.
|
||||
- When options are nested by strength, rank them explicitly before answering: e.g. finite-time blowup is stronger than merely “not globally bounded”; positive stable growth is stronger than ordinary unboundedness; sharper constants, rates, exceptional-set bounds, endpoint inclusion, or full equivalences are stronger than weaker asymptotic versions.
|
||||
- Compare all options before committing. The correct choice is often the strongest statement justified by the question, while nearby distractors are weaker, overstrong, or miss an equality case.
|
||||
- Track exact quantifiers such as "there exists", "for every", "if and only if", and "exactly when".
|
||||
|
||||
## Theorem-Level Precision
|
||||
|
||||
- Do not add converse, realization, or classification claims unless the theorem explicitly proves them. Phrases such as “conversely,” “every such parameter occurs,” “if and only if,” or “exactly all” add strength beyond a one-way implication.
|
||||
- Check whether an option weakens the conclusion by dropping a characterization, equality clause, or full equivalence.
|
||||
- Check whether an option overstates the theorem by upgrading regularity, removing scale restrictions, or changing an existential statement into a universal one.
|
||||
|
||||
## Hypotheses
|
||||
|
||||
### Exact Conditions and Thresholds
|
||||
- For biconditional/equivalence questions, reject conditions that are merely necessary or merely sufficient. A broader condition, such as congruence modulo a divisor instead of modulo the full modulus, is usually weaker and not equivalent unless the domain collapses the extra cases.
|
||||
- For threshold conditions, verify the exact sign and endpoint: distinguish \(\mu_0\) from \(-\mu_0\), \(<\) from \(\le\), and whether the equality case belongs to the positive, zero, or negative parameter regime.
|
||||
- When options differ by “for every” vs “for sufficiently large,” local vs global domains, strict vs non-strict inequalities, or dependence of constants, rank them by logical strength and match the sharpest justified version.
|
||||
- Verify the hypotheses and domain carefully. Distractors often keep the theorem shape but alter the required assumptions.
|
||||
- Pay close attention to equality cases, extremal conditions, and whether a result applies to the full family or only a restricted subfamily.
|
||||
|
||||
## Final Answer
|
||||
- Output the final answer as the single option label only.
|
||||
|
||||
## Exact Scope and Quantitative Wording
|
||||
- Distinguish global conclusions from localized or completed ones. Equivalence after localization, completion, or at each prime/scale is usually weaker than an unqualified equivalence.
|
||||
- In estimate-heavy options, compare every quantitative detail: exponent, derivative index range, constants and their parameter dependence, log factors, additive terms, one-sided vs two-sided notation, and pointwise vs uniform convergence.
|
||||
|
||||
<!-- SLOW_UPDATE_START -->
|
||||
<!-- SLOW_UPDATE_END -->
|
||||
@@ -0,0 +1,50 @@
|
||||
# OfficeQA Skill
|
||||
|
||||
## Retrieval Discipline
|
||||
|
||||
- When an external official time-series observation is needed, prefer the source's series/data-download/table page once identified. If exact-date or guessed-value searches return empty results, stop repeating them; broaden to the official series name/code plus `data` or `download` and use the table values.
|
||||
|
||||
- Treat provided/oracle parsed pages as primary evidence: if they contain the relevant table and period, extract directly from them before searching elsewhere; search only for missing continuation pages, missing periods, or an official actual value not present.
|
||||
- Start by narrowing to the most likely candidate file before reading long passages.
|
||||
- Prefer targeted search terms that name the exact entity, period, measure, or table concept from the question.
|
||||
- After a promising match, read only a small surrounding span and verify it matches the requested year, basis, and unit.
|
||||
|
||||
- If the requested date range extends beyond the provided/oracle page, first enumerate the required periods and verify that every period is present in evidence. Do not compute from a partial ledger or fill missing periods from memory; retrieve continuation pages, adjacent issues, or a later issue of the same table that contains the missing dates/revisions.
|
||||
|
||||
## Evidence Discipline
|
||||
- Extract the exact value from the retrieved text before doing any arithmetic.
|
||||
- Keep track of each operand's period, unit, and semantic role so nearby proxy values are not mixed in.
|
||||
|
||||
- For Treasury financing narratives, label each amount by transaction role before calculating: offered amount, tenders/subscriptions received, tenders accepted, competitive/noncompetitive accepted, foreign or Government-account exchange tenders, refunding, and **new cash** are not interchangeable.
|
||||
- When converting currencies or scales, make a direction ledger first: source table unit, source currency, exchange-rate orientation (foreign currency per U.S. dollar means divide by the rate; U.S. dollars per foreign unit means multiply), and requested final unit.
|
||||
|
||||
- For tables, align values by row label and exact column header, not proximity alone; watch for continued or unlabeled columns, footnotes, adjacent amount-versus-percent columns, fiscal-year versus calendar-year sections, and repeated month rows under different year blocks.
|
||||
- If the question asks for a transformed or derived quantity, compute only after confirming every operand.
|
||||
|
||||
- For derived comparisons, preserve the direction and sign implied by the wording: “change from A to B” means B minus A; “former than latter” means former minus latter; “share accounted for by X” means X divided by the stated total; paired “gap” questions require computing each within-row difference before ranking.
|
||||
- For statistical, regression, correlation, and growth-rate questions, write a formula ledger before calculating: confirm the exact series/endpoints, ordered vector, elapsed intervals, and requested convention such as continuously compounded rate, CAGR, Pearson correlation, or OLS index/year choice.
|
||||
- For multi-stage questions where one table determines the period/entity used in another lookup, freeze that derived key with evidence first, then retrieve the second measure only for that exact month/year/reporting date/entity.
|
||||
|
||||
- For inclusive time-series ranges, make a period-by-period ledger covering every requested month/year exactly once, preserving calendar versus fiscal basis, end-of-month or end-of-fiscal-month status, source units, and any specified adjustments.
|
||||
|
||||
- For statistical transforms over time-series windows, confirm endpoint inclusion/exclusion exactly as worded, use consecutive time indices for trend regressions when appropriate, sort values before medians, and for logarithmic growth use ln(final/initial) before converting to the requested percentage format.
|
||||
|
||||
## Final Answer Discipline
|
||||
|
||||
- Before finalizing, enforce the requested unit and format: convert thousands/millions/billions or full nominal dollars as needed, then apply no-comma, fixed-decimal, whole-number, or nearest-tenth/thousandth formatting exactly as asked.
|
||||
- Return the final answer only after one last consistency check against the retrieved evidence.
|
||||
- Copy the final answer from a checked value, not from an unverified intermediate guess.
|
||||
|
||||
## Statistical and Time-Series Calculation Checks
|
||||
|
||||
- Before computing any statistic, write the intended formula and denominator convention. If the prompt explicitly says **population standard deviation**, divide by `n`; if it says **sample**, divide by `n-1`; for a z-score comparing one observation against a small set of comparison months/periods and no population convention is stated, estimate dispersion with the **sample** standard deviation of the comparison set. Do not round intermediate operands, weighted averages, logs, exchange-rate conversions, or standard deviations before the final requested rounding.
|
||||
- For long inclusive ranges, first enumerate the expected count of observations and the first/last period, then verify the ledger has exactly that count. Exclude totals, cumulative-to-date columns, comparable-period columns, estimates, and extra latest-month columns outside the requested calendar or fiscal range.
|
||||
- When a page contains multiple nearby sections with similar labels, use only the section whose title and row label match the requested measure exactly; do not compute from the first visible table if the requested measure/table title is absent or only partially shown.
|
||||
- For Treasury security quotations, obey the table's quote basis. If the table states that price decimals are 32nds, convert quotes such as `99.27` as `99 + 27/32`, not as decimal `99.27`. If a task asks for smoothing, averaging, or forecasting in a target currency using period-specific exchange rates, convert each period's observation to the target currency first unless the prompt explicitly says to compute in the source currency and convert only the final result.
|
||||
|
||||
## Stricter Final Formatting
|
||||
|
||||
- Match any requested output template exactly. Unless the prompt explicitly asks for unit words or explanatory text, return only the numeric value or requested list; do not append words such as `million`, `dollars`, `percent`, or `percentage points`. Include symbols/commas only when the prompt requests currency-formatted output or the answer format clearly requires them.
|
||||
|
||||
<!-- SLOW_UPDATE_START -->
|
||||
<!-- SLOW_UPDATE_END -->
|
||||
@@ -0,0 +1,71 @@
|
||||
# Question Answering Skill
|
||||
|
||||
(No learned rules yet. Rules will be added through the reflection process.)
|
||||
|
||||
## Concise Answer Normalization
|
||||
- Prefer the shortest unambiguous answer that directly satisfies the question. Do not include generic descriptors, legal suffixes, or expanded formal names unless the question specifically asks for the full official name or the descriptor is necessary to identify the entity.
|
||||
|
||||
- If the answer appears inside a longer descriptive phrase, strip words that merely repeat the clue's requested type or modifiers already stated in the clue. For short-answer trivia, return the distinctive core entity or headword rather than role titles, product flavor adjectives, or place/facility designators, even when those words are part of a fuller official phrase, unless the full official name is explicitly requested.
|
||||
- For place/name-etymology questions asking for “the name” or “the word” that means something, answer the distinctive name/word itself rather than a larger phrase with a generic type label.
|
||||
|
||||
- For natural geographic features, preserve conventional feature designators such as “Lake,” “River,” “Bay,” “Gorge,” “Mount,” or “Island” when they are part of the proper name or match the requested feature type. Do not shorten “Lake Okeechobee,” “Tampa Bay,” or “Olduvai Gorge” to an ambiguous base name merely to be concise.
|
||||
- For companies, brands, and organizations, answer the common distinctive name when sufficient; omit additions such as “Company,” “Corporation,” “Inc.,” etc. unless explicitly required.
|
||||
|
||||
- Preserve the answer surface form supported by the strongest evidence when exact variants differ: spelling, capitalization, punctuation, and word order can matter. Do not substitute an equivalent official/common variant such as an alternate spelling or inverted institution name if a direct title/snippet/answer field gives the expected form.
|
||||
|
||||
- When copying titles or quoted names, preserve ordinary ASCII punctuation from the evidence, especially straight apostrophes (`'`). Do not replace them with typographic curly quotes/apostrophes unless that exact stylized form is explicitly shown as the supported answer.
|
||||
|
||||
- For nicknames, epithets, saints, and quoted titles, copy the supported surface form exactly, including spacing, capitalization, and conventional abbreviations such as “St.” Do not normalize a stylized or quoted form into a lowercase dictionary word or an expanded spelling when the clue/evidence points to the stylized answer.
|
||||
|
||||
- For person answers in trivia or crossword-style clues, prefer the conventional supported name. Use just a surname, first name, or saint/regnal name only when the clue/source clearly expects that short form; otherwise use the canonical full personal name from the strongest evidence or answer field, especially when a lone given name would be ambiguous.
|
||||
- Return the grammatical base form expected by the clue. Do not add a plural `s` merely because a crossword source pluralizes a shared name or category; if the clue lists people sharing a first name, answer the singular given name.
|
||||
|
||||
- For common-noun category answers, default to the singular dictionary headword in trivia/crossword-style clues, even if the clue uses plural words like “these,” “those,” “places,” or “items” for grammar. Use a plural only when the term is inherently plural or an answer field/source clearly gives a plural phrase.
|
||||
|
||||
- For common-noun clues about things being replaced, used in place of, or substituted by another system/item, answer the broad headword for the thing replaced unless a narrowing modifier is required by the clue or answer field. Do not add adjectives such as “letter,” “regular,” or “standard” merely because they appear in explanatory context.
|
||||
- For fill-in-the-blank or definitional clues using words like “this” or “that,” provide a standalone noun phrase. Avoid context-dependent pronouns or possessives from the source text; use a natural article such as “the” when needed (e.g., answer “the highest point,” not “its highest point”).
|
||||
|
||||
## Context-Grounded Evidence Matching
|
||||
- Start by identifying the most distinctive terms in the question: proper names, dates, titles, quoted phrases, unusual words, roles, relationships, and category descriptors.
|
||||
- Prioritize passages or document titles where several distinctive clue terms occur together, especially if the wording directly repeats or closely paraphrases the question.
|
||||
- Treat document titles as useful evidence: the answer is often named in a title while the snippet confirms the clue facts.
|
||||
|
||||
- Do not assume the document title itself is the answer. If the requested type differs from the title entity, use the title as context and extract the matching typed entity from the snippet or clue relationship.
|
||||
|
||||
- For “known as,” “called,” “defined as,” or category/type clues, choose the canonical term explicitly used in the strongest matching title/snippet or scraped answer field rather than inventing a related derivative or near-synonym from the clue wording. When multiple plausible candidates appear, prefer the candidate whose evidence directly states the requested relationship and repeats the most distinctive clue facts.
|
||||
- Ignore noisy results that only match generic words; prefer evidence that directly connects the clue facts to one specific entity.
|
||||
|
||||
## Clue Interpretation and Answer Type
|
||||
- For Jeopardy-style wording such as “this man,” “this group,” “this film,” “this country,” “this system,” “he,” or “his wife,” infer the expected answer type before choosing the answer.
|
||||
- Use that expected type to validate candidates: answer with the concise person, place, title, organization, object, term, or phrase requested by the clue.
|
||||
|
||||
- Treat modifiers attached to the requested type as hard filters, not background flavor: constraints like dates, “largest,” “2-letter-named,” “1978 remake,” “hot dog brand,” “dual throne,” or “on this company’s board” must all fit the candidate before you answer.
|
||||
- For clues centered on creative works such as books, films, plays, songs, poems, or other media, first determine whether the clue asks for the work itself, its creator, a performer or cast member, a character, a quotation source, or a setting. Verbs such as “wrote,” “directed,” “stars,” “played,” and “set in,” plus pronouns like “he” or “her,” usually determine the target.
|
||||
|
||||
- For fill-in-style clues with placeholders such as “this,” “these,” or “one of these,” substitute each candidate back into the clue and choose the concise answer that makes the full phrase, title, or fact read correctly.
|
||||
- For terse clues that are just examples or names separated by commas, slashes, or “or,” infer the shared category, class, or synonym that links them, then answer with that concise common term.
|
||||
|
||||
- For crossword-style clues, treat parenthetical numbers or stated letter counts as hard constraints on the answer length, and omit generic labels that would violate them. In dual-definition clues using wording like “X, or what Y does,” choose the single word that satisfies both senses and preserve the required inflected form.
|
||||
- If the clue references an unavailable image or link with wording like “seen here,” “pictured,” or parenthetical visual hints, rely on the textual clues and context to infer the answer; do not treat the missing image as necessary evidence.
|
||||
- If multiple snippets support the same entity, use that corroboration to choose the canonical/common form of the answer.
|
||||
|
||||
## Trivia / Jeopardy Snippet Formats
|
||||
- Retrieved trivia snippets may contain the clue and answer in scraped formats such as `CATEGORY | clue | answer`, `clue. ANSWER`, or labels like `right:`.
|
||||
- When the question text matches the clue in such a snippet, extract the answer field or adjacent answer name, not the category or the whole clue sentence.
|
||||
|
||||
## Common Clue Traps
|
||||
- Watch for inverse relationships: if the clue says “His third wife was Jiang Qing,” the requested answer is the husband, not Jiang Qing.
|
||||
|
||||
- More generally, preserve relation direction in clues: “A is evidence of this B,” “A is related to this language,” or “home to these characters” asks for the target of the relationship, not the entity already named in the clue.
|
||||
|
||||
- When a clue says examples, models, breeds, members, or items “include,” “like,” or “such as” named entities, treat those names as evidence for the requested parent class or entity. Answer the encompassing brand, animal, category, place, or term requested by “this,” not one of the examples already given.
|
||||
- If the question gives the start of a quotation or phrase, answer with the exact missing continuation from the context.
|
||||
|
||||
- For song, poem, nursery-rhyme, or quotation clues, first decide whether the question asks for a missing word or phrase from the quote or for the associated creator, performer, or work; use pronouns and answer-type signals to choose the right target.
|
||||
- When a clue asks for a constrained form such as a first name, abbreviation, acronym, or lyric word, return that exact form rather than the fuller person, title, or explanation; preserve conventional punctuation or spelling when it is part of the requested form.
|
||||
- If the clue contains wordplay, quotation marks, or puns, treat them as hints, but answer with the real entity supported by the evidence.
|
||||
|
||||
- If a clue includes a quoted title, quoted narration or lyric, named event, slogan, or other distinctive phrase but asks for an associated “this” entity, treat the quote or name as evidence to identify the requested person, work, place, group, category, source, or term; do not return the quoted anchor unless the clue explicitly asks for it.
|
||||
|
||||
<!-- SLOW_UPDATE_START -->
|
||||
<!-- SLOW_UPDATE_END -->
|
||||
@@ -0,0 +1,133 @@
|
||||
# Spreadsheet Manipulation Skill (xlsx)
|
||||
|
||||
## Overview
|
||||
This skill guides agents in manipulating Excel (.xlsx) spreadsheets using Python.
|
||||
|
||||
**Primary libraries**: `openpyxl` (structure-preserving read/write), `pandas` (data transformation).
|
||||
Never use any other third-party libraries.
|
||||
|
||||
---
|
||||
|
||||
## Common Workflow
|
||||
|
||||
1. **Explore** the input file: list sheets, inspect headers, check dimensions.
|
||||
|
||||
- Inspect actual workbook data beyond the preview, including nearby rows/columns, sample outputs, formulas, labels, headers, and any reference/example sheets such as `Output`, `Manual Result`, or `Desired...` tabs.
|
||||
|
||||
- Treat existing filled cells in the requested output area or adjacent example tables as semantic examples for edge cases and expected formats, but still recompute and write the complete requested target range.
|
||||
- Scan the used range for complete header groups, not just row 1. Tables may start in later rows/columns, have title rows above them, or have multiple source/result tables on the same sheet; use nearby labels and the requested output range to distinguish sources from destinations.
|
||||
- Locate tables, fields, and target ranges by header text, nearby labels, and surrounding nonblank structure rather than fixed coordinates. Build header maps from actual cells when useful, e.g. `{str(cell.value).strip(): cell.column}`.
|
||||
2. **Write `solution.py`** with `INPUT_PATH` and `OUTPUT_PATH` defined at the top.
|
||||
3. **Execute** `python solution.py` and verify the output file was created.
|
||||
4. **Confirm** the target cells/range contain the expected values.
|
||||
|
||||
---
|
||||
|
||||
## Library Selection
|
||||
|
||||
| Use case | Library |
|
||||
|----------|---------|
|
||||
| Preserve formulas, formatting, named ranges | `openpyxl` |
|
||||
| Bulk data transformation, aggregation, sorting | `pandas` → write back with `openpyxl` |
|
||||
| Simple cell read/write | `openpyxl` |
|
||||
|
||||
**Warning**: `pandas.to_excel()` silently destroys existing formulas and named ranges.
|
||||
When writing back to a spreadsheet that contains formulas, always use `openpyxl.save()`.
|
||||
|
||||
**Formula evaluation caution**: `openpyxl` can write formulas but does **not** calculate them or update cached results. If the requested output will be checked as cell values, compute the result in Python and write literal values unless the user explicitly requires live formulas. When existing formulas are inputs to your logic, load a second workbook with `data_only=True` to read cached values while saving changes through the normal workbook:
|
||||
|
||||
```python
|
||||
wb = openpyxl.load_workbook(INPUT_PATH)
|
||||
wb_values = openpyxl.load_workbook(INPUT_PATH, data_only=True)
|
||||
ws = wb["Sheet1"]
|
||||
ws_values = wb_values["Sheet1"]
|
||||
```
|
||||
|
||||
Treat wording such as “write/fix a formula,” “SUMIFS/COUNTIFS,” “VBA,” or “macro” as a description of the spreadsheet logic unless the deliverable explicitly requires live formula text, an `.xlsm`, or a preserved VBA project. For normal `.xlsx` outputs, implement the equivalent logic in Python/openpyxl and write the computed final values to the requested cells so verification does not depend on Excel recalculation or macros.
|
||||
|
||||
When the user provides an existing or broken formula, use it as a semantic specification: honor its referenced lookup ranges, criteria ranges, return ranges, aggregation intent, and error-handling behavior, then write the resulting values rather than guessing different source columns or leaving unevaluated formulas.
|
||||
|
||||
---
|
||||
|
||||
## solution.py Template
|
||||
|
||||
```python
|
||||
import openpyxl
|
||||
import pandas as pd
|
||||
|
||||
INPUT_PATH = "..." # set to the actual input path
|
||||
OUTPUT_PATH = "..." # set to the actual output path
|
||||
|
||||
wb = openpyxl.load_workbook(INPUT_PATH)
|
||||
ws = wb.active # or wb["SheetName"]
|
||||
|
||||
# --- perform manipulation ---
|
||||
|
||||
wb.save(OUTPUT_PATH)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Output Requirements
|
||||
|
||||
- Save the result to `OUTPUT_PATH`.
|
||||
- Do not hardcode row counts or column letters — iterate over actual rows in the workbook.
|
||||
- Preserve sheets and cells not mentioned in the instruction.
|
||||
|
||||
## Matching and Target Range Hygiene
|
||||
|
||||
- Choose the comparison operator from the instruction and examples: use `startswith` for “begins with”, substring search for “contains/search/occurrence”, and exact normalized equality only when a whole-cell match is implied.
|
||||
- Create small helper functions for comparisons and numeric parsing. Normalize text by trimming, collapsing repeated spaces/NBSPs, and casefolding; when names or labels have punctuation/spacing inconsistencies, consider punctuation-insensitive keys. Parse numeric text after removing commas/currency symbols while preserving signs and decimal points; skip `None`/blank and booleans for numeric tests, and handle placeholders such as `"-"`, `"$"`, `"$0"`, blanks, and numeric zero deliberately.
|
||||
- Normalize date keys deliberately: handle `datetime`/`date` objects, Excel serial numbers, and date-like strings, then compare at the granularity implied by the task, such as exact date, month, month/year, fiscal period, or year. For workday/date-window logic, compute the range in Python and exclude weekends/holidays as specified.
|
||||
|
||||
- For monthly or period summary grids, canonicalize period labels from all sources: sheet names, title text, row/column headers, text months such as `March`, and actual date cells. Match summaries by normalized period plus the other stated criteria rather than by fixed month offsets or existing formulas.
|
||||
- For date ranges and rolling windows, infer endpoint inclusivity from wording and examples. Phrases like `X to Y`, `through`, and `up to`, or examples such as `2 to 5` meaning `4 days`, usually require inclusive boundary handling.
|
||||
- For time extraction or time-threshold logic, parse `datetime`, `time`, Excel serial/fractional times, and time-like strings into real Python `time`/`datetime` values. Write real time values with an Excel `number_format` such as `hh:mm:ss AM/PM`; do not write text substrings when the result should behave as a time.
|
||||
- For joins, deduplication, grouping, interval lookups, lookup grids, and ordered outputs, build explicit normalized keys, including composite keys when the task refers to multiple fields. Preserve original source order within each group unless sorting is explicitly requested.
|
||||
- For outputs that depend on other rows or lookup grids, make a first pass to build normalized dictionaries/groups/range structures, then a second pass to write results. Avoid nested full-sheet scans per row; split delimited tokens and ignore empty tokens, and treat error literals such as `#N/A` as meaningful sentinel values when the task refers to them.
|
||||
|
||||
- For lookups, filters, joins, and label/header matching, normalize comparison keys consistently: trim whitespace, skip blanks explicitly, use case-insensitive text matching when appropriate, and treat numeric-looking IDs consistently (`330`, `330.0`, and `"330"`). Keep numeric outputs numeric; use `number_format` for display formatting instead of converting numbers to strings unless text is explicitly required.
|
||||
- When replacing a generated output area, clear only the instructed target range before writing new results so stale values/formulas do not remain. Preserve formatting, column widths, borders, formulas, and unrelated cells unless the instruction explicitly asks to change them.
|
||||
|
||||
- If the instruction includes formatting changes, apply them exactly after writing values and only to the requested cells/range. Use `openpyxl` styles for fills, alignment, fonts, borders, and number formats; convert hex colors to ARGB when needed, for example `#FFC000` → `FFFFC000`. For “format as text,” set `number_format = '@'` and write string values when the expected cell values are text.
|
||||
|
||||
- When the instruction names a destination range or columns, write derived results directly there. Do not insert rows/columns, relocate the source table, or sort/delete source records unless that structural change is explicitly requested.
|
||||
- For filtered lists, summaries, and aggregations, first collect all source records/results in memory, preserving the required order, then write from the first output row and clear leftover cells below the new results in the target columns. When adding rows, copy style/alignment/number format from an existing template row when appropriate; when deleting rows, delete from bottom to top to avoid row-index shifts.
|
||||
- Preserve intended blanks as empty cells (`None`) rather than placeholder text or `0` unless the task specifies otherwise.
|
||||
|
||||
- For numeric aggregation, crosstab, SUMIFS-like, and INDEX/MATCH-style summary outputs, infer missing-match behavior from table semantics and examples: numeric summary grids usually require literal `0` for no matching records, while filtered lists or “show only once” outputs usually require blanks (`None`).
|
||||
- For blank-sensitive logic such as “if input is blank, output blank,” evaluate the driving input with `data_only=True` when it may itself be a formula, and write `None` for truly blank outputs rather than relying on a new formula returning `""`.
|
||||
|
||||
## Robustness for Simple Fill Tasks
|
||||
|
||||
- Prefer simple, auditable row/column loops over complex workbook XML parsing unless the task truly requires unsupported workbook internals. Before returning, run the script once to catch syntax/indentation errors and verify that representative target rows were actually written.
|
||||
|
||||
<!-- SLOW_UPDATE_START -->
|
||||
When the user asks for a formula, macro, VBA code, or a fix to an Excel formula, still deliver the completed workbook state: compute the intended results in Python and write literal final values into the requested cells. Do not write formula strings unless the task explicitly says the output must contain live formulas.
|
||||
|
||||
After writing, reload or inspect the saved workbook and verify that every requested/evaluated target cell contains a non-formula literal where a value is expected. If a target cell is still `None` unexpectedly, fix the script before finishing.
|
||||
|
||||
Use existing formulas in the workbook as examples/specifications, not as output. If a cell contains a reference formula such as `=A25` or an INDEX/MATCH/SUMIFS pattern, parse what source cells/ranges/criteria it refers to, compute those results yourself, and overwrite the destination with the referenced or calculated value.
|
||||
|
||||
For blank-sensitive formula tasks, compute the branch explicitly: if the driving source cell is truly blank, write `None`; otherwise write the actual result such as `0`, `1`, a category label, or a lookup value. Never rely on `IF(...,"",...)` formulas to be recalculated later.
|
||||
|
||||
For lookup/category tasks, locate both the input rows and the lookup table by headers and nearby labels. Support exact keys, numeric-looking keys, and interval/range tables; then fill every destination row that has a driving input, not just the first visible example.
|
||||
|
||||
For “every nth row” or OFFSET-style tasks, infer the source column, first source row, and step from the provided examples or formulas, then copy the actual source values into the requested output range as literals.
|
||||
|
||||
For schedule/calendar fill tasks, build a cycle-day-to-periods mapping from the schedule/template area first, then fill the daily rows across all requested class columns based on each row’s cycle day. Preserve repeated/double periods exactly as shown by the template; do not leave formulas in the schedule cells.
|
||||
|
||||
For INDEX/MATCH problems where the first row works but subsequent rows fail, treat row labels, column/year headers, region/type criteria, and expense/category labels as a multi-key lookup. Fill the whole result matrix with values from the source data table, using cached `data_only` values when source cells are formulas.
|
||||
|
||||
For multi-step macro/VBA-style requests, implement every stated operation in the workbook, not just the first deletion/filtering step. Re-read the numbered requirements before saving and verify later computed columns, totals, and derived fields as well as the obvious filtered rows.
|
||||
|
||||
When a target range includes special rows such as `Total`, `Grand Total`, `min`, `max`, constraints, headers, or blank separators, do not apply ordinary row logic blindly to those rows. Compute totals as aggregates when indicated, and leave constraint/header/blank cells untouched unless explicitly requested.
|
||||
|
||||
For residual-balancing tasks, identify data rows separately from min/max constraint rows. Add positive residuals from unit 1 toward unit 5 without exceeding max values; subtract negative residuals from unit 5 toward unit 1 without going below min values; update only the unit cells in actual data rows.
|
||||
|
||||
For time-threshold rows, decide per row whether it is a normal data row or a summary row. Normal rows use the before/after threshold rule; summary rows should aggregate the computed normal-row results if the workbook labels or examples indicate a total.
|
||||
|
||||
Keep scripts simple enough to run cleanly. Avoid unnecessary dynamic code generation and fragile f-strings with regex expressions inside them. Always execute the final `solution.py`; fix any syntax, indentation, or runtime error, then verify representative target cells.
|
||||
|
||||
If workbook cells contain arbitrary sample text that could be sensitive or trigger content filters, do not quote large raw cell contents in your response. Process them locally in Python with neutral variable names and output only the completed script/workbook changes.
|
||||
<!-- SLOW_UPDATE_END -->
|
||||
@@ -0,0 +1,103 @@
|
||||
# SkillOpt default configuration — base for all environments.
|
||||
# Environment configs should inherit via: _base_: default.yaml
|
||||
|
||||
model:
|
||||
backend: azure_openai
|
||||
optimizer: gpt-5.5
|
||||
target: gpt-5.5
|
||||
optimizer_backend: openai_chat
|
||||
target_backend: openai_chat
|
||||
reasoning_effort: medium
|
||||
rewrite_reasoning_effort: ""
|
||||
rewrite_max_completion_tokens: 64000
|
||||
codex_exec_path: codex
|
||||
codex_exec_sandbox: workspace-write
|
||||
codex_exec_profile: ""
|
||||
codex_exec_full_auto: false
|
||||
codex_exec_reasoning_effort: none
|
||||
codex_exec_use_sdk: auto
|
||||
codex_exec_network_access: false
|
||||
codex_exec_web_search: false
|
||||
codex_exec_approval_policy: never
|
||||
claude_code_exec_path: claude
|
||||
claude_code_exec_profile: ""
|
||||
claude_code_exec_use_sdk: auto
|
||||
claude_code_exec_effort: medium
|
||||
claude_code_exec_max_thinking_tokens: 16384
|
||||
codex_trace_to_optimizer: true
|
||||
azure_openai_endpoint: "" # e.g. "https://your-resource.openai.azure.com/"
|
||||
azure_openai_api_version: "2024-12-01-preview"
|
||||
azure_openai_api_key: "" # Fill locally if you do not export AZURE_OPENAI_API_KEY
|
||||
azure_openai_auth_mode: "" # empty → fall back to AZURE_OPENAI_AUTH_MODE env (default "azure_cli")
|
||||
azure_openai_ad_scope: "https://cognitiveservices.azure.com/.default"
|
||||
azure_openai_managed_identity_client_id: ""
|
||||
optimizer_azure_openai_endpoint: "" # e.g. "https://your-resource.openai.azure.com/"
|
||||
optimizer_azure_openai_api_version: "2024-12-01-preview"
|
||||
optimizer_azure_openai_api_key: ""
|
||||
optimizer_azure_openai_auth_mode: "" # empty → fall back to OPTIMIZER_AZURE_OPENAI_AUTH_MODE env, then shared
|
||||
optimizer_azure_openai_ad_scope: "https://cognitiveservices.azure.com/.default"
|
||||
optimizer_azure_openai_managed_identity_client_id: ""
|
||||
target_azure_openai_endpoint: "" # e.g. "https://your-resource.openai.azure.com/"
|
||||
target_azure_openai_api_version: "2024-12-01-preview"
|
||||
target_azure_openai_api_key: ""
|
||||
target_azure_openai_auth_mode: "" # empty → fall back to TARGET_AZURE_OPENAI_AUTH_MODE env, then shared
|
||||
target_azure_openai_ad_scope: "https://cognitiveservices.azure.com/.default"
|
||||
target_azure_openai_managed_identity_client_id: ""
|
||||
|
||||
# MiniMax backend settings (minimax_chat target)
|
||||
minimax_base_url: "" # https://api.minimax.io/v1 if blank
|
||||
minimax_api_key: ""
|
||||
minimax_model: "MiniMax-M2.7"
|
||||
minimax_temperature: "0.7"
|
||||
minimax_max_tokens: "8000"
|
||||
minimax_enable_thinking: "false"
|
||||
optimizer_minimax_base_url: "" # per-role override
|
||||
target_minimax_base_url: "" # per-role override
|
||||
optimizer_minimax_api_key: ""
|
||||
target_minimax_api_key: ""
|
||||
|
||||
train:
|
||||
num_epochs: 4
|
||||
train_size: 0 # 0 = derive from dataset split when available
|
||||
batch_size: 40
|
||||
accumulation: 1
|
||||
seed: 42
|
||||
|
||||
gradient:
|
||||
minibatch_size: 8
|
||||
merge_batch_size: 8
|
||||
analyst_workers: 16
|
||||
max_analyst_rounds: 3
|
||||
failure_only: false
|
||||
|
||||
optimizer:
|
||||
learning_rate: 4 # max edits per step (edit_budget)
|
||||
min_learning_rate: 2 # min edits for decay schedulers
|
||||
lr_scheduler: cosine # constant / linear / cosine / autonomous
|
||||
lr_control_mode: fixed # fixed / autonomous / none
|
||||
skill_update_mode: patch # patch / rewrite_from_suggestions / full_rewrite_minibatch
|
||||
use_slow_update: true
|
||||
slow_update_samples: 20
|
||||
slow_update_gate_with_selection: false
|
||||
longitudinal_pair_policy: mixed # mixed / changed / unchanged
|
||||
use_meta_skill: true
|
||||
use_skill_aware_reflection: false # EmbodiSkill: split failures into SKILL_DEFECT (edit body) vs EXECUTION_LAPSE (protected appendix)
|
||||
skill_aware_appendix_source: both # both = success+failure emit appendix notes; failure_only = only EXECUTION_LAPSE (paper-faithful)
|
||||
skill_aware_consolidate_threshold: 0 # 0 = off; >0 = LLM-consolidate the appendix when its note count exceeds N
|
||||
|
||||
evaluation:
|
||||
use_gate: true
|
||||
sel_env_num: 0
|
||||
test_env_num: 0
|
||||
eval_test: true
|
||||
|
||||
env:
|
||||
name: ""
|
||||
skill_init: ""
|
||||
split_mode: ratio # ratio = build deterministic split from data_path; split_dir = use pre-split train/val/test
|
||||
split_seed: 42
|
||||
split_dir: ""
|
||||
data_path: ""
|
||||
split_output_dir: ""
|
||||
exec_timeout: 120 # per target model/code-agent call timeout in seconds
|
||||
out_root: ""
|
||||
@@ -0,0 +1,29 @@
|
||||
_base_: ../_base_/default.yaml
|
||||
|
||||
train:
|
||||
train_size: 0
|
||||
accumulation: 1
|
||||
|
||||
gradient:
|
||||
minibatch_size: 8
|
||||
merge_batch_size: 8
|
||||
|
||||
optimizer:
|
||||
learning_rate: 4
|
||||
|
||||
evaluation:
|
||||
sel_env_num: 0
|
||||
test_env_num: 0
|
||||
|
||||
env:
|
||||
name: alfworld
|
||||
skill_init: skillopt/envs/alfworld/skills/initial.md
|
||||
split_mode: split_dir
|
||||
split_dir: data/alfworld_path_split
|
||||
data_path: ""
|
||||
split_output_dir: ""
|
||||
max_steps: 50
|
||||
max_completion_tokens: 16384
|
||||
workers: 8
|
||||
max_api_workers: 8
|
||||
limit: 0
|
||||
@@ -0,0 +1,28 @@
|
||||
_base_: ../_base_/default.yaml
|
||||
|
||||
model:
|
||||
reasoning_effort: medium
|
||||
|
||||
train:
|
||||
batch_size: 40
|
||||
accumulation: 1
|
||||
|
||||
gradient:
|
||||
minibatch_size: 8
|
||||
merge_batch_size: 8
|
||||
|
||||
optimizer:
|
||||
learning_rate: 4
|
||||
|
||||
env:
|
||||
name: docvqa
|
||||
skill_init: skillopt/envs/docvqa/skills/initial.md
|
||||
split_mode: split_dir
|
||||
split_dir: data/docvqa/splits
|
||||
data_path: ""
|
||||
split_output_dir: ""
|
||||
max_turns: 1
|
||||
max_completion_tokens: 16384
|
||||
workers: 16
|
||||
image_detail: auto
|
||||
limit: 0
|
||||
@@ -0,0 +1,47 @@
|
||||
# ─────────────────────────────────────────────────────────────────────────────
|
||||
# Feature: soft / mixed validation-gate metric (community-contributed, PR #25)
|
||||
# ─────────────────────────────────────────────────────────────────────────────
|
||||
#
|
||||
# This is NOT a default SkillOpt setting and was NOT used to produce the
|
||||
# numbers reported in the paper. It is provided as a reference for users
|
||||
# who encounter a specific scenario where the default `hard` gate is too
|
||||
# coarse to drive training.
|
||||
#
|
||||
# When to consider this:
|
||||
# - You are running on a custom environment.
|
||||
# - Your held-out *selection* split has very few items (e.g. ≤ ~10).
|
||||
# - Your reward function is continuous / partial-credit (e.g. F1, BLEU,
|
||||
# soft match) rather than purely binary 0/1.
|
||||
#
|
||||
# Symptom this addresses:
|
||||
# With a small selection split + continuous rewards, candidate skills
|
||||
# often improve per-item soft scores (e.g. 0.06 → 0.26 on one item) but
|
||||
# never flip the discrete hard outcome. The default `hard` gate then
|
||||
# rejects every candidate and training stalls. Switching the gate to
|
||||
# `soft` or `mixed` lets these partial improvements be accepted.
|
||||
#
|
||||
# When NOT to use this:
|
||||
# - When reproducing the paper. The paper-reported numbers were obtained
|
||||
# under the default `hard` gate.
|
||||
# - When your selection split is large (dozens+ items) and / or your
|
||||
# reward is already binary — `hard` is the more conservative choice
|
||||
# and matches the design described in the paper.
|
||||
#
|
||||
# To use: inherit your env config from this file, e.g.
|
||||
# _base_: ../features/soft_gate.yaml
|
||||
# or copy the `evaluation:` block below into your config.
|
||||
# ─────────────────────────────────────────────────────────────────────────────
|
||||
|
||||
_base_: ../_base_/default.yaml
|
||||
|
||||
evaluation:
|
||||
# Three options:
|
||||
# 'hard' — default; exact-match accuracy. Use this to reproduce the paper.
|
||||
# 'soft' — per-item soft / partial-credit score (recommended for the
|
||||
# small-split + continuous-reward scenario described above).
|
||||
# 'mixed' — weighted average: (1 - w) * hard + w * soft, with `w` set by
|
||||
# `gate_mixed_weight` below.
|
||||
gate_metric: soft
|
||||
|
||||
# Only used when gate_metric == 'mixed'. Ignored otherwise.
|
||||
gate_mixed_weight: 0.5
|
||||
@@ -0,0 +1,22 @@
|
||||
_base_: ../_base_/default.yaml
|
||||
|
||||
train:
|
||||
train_size: 0
|
||||
batch_size: 40
|
||||
accumulation: 1
|
||||
|
||||
env:
|
||||
name: livemathematicianbench
|
||||
skill_init: skillopt/envs/livemathematicianbench/skills/initial.md
|
||||
split_mode: split_dir
|
||||
split_dir: data/livemathematicianbench_split
|
||||
data_path: ""
|
||||
split_output_dir: ""
|
||||
max_turns: 1
|
||||
max_completion_tokens: 16384
|
||||
exec_timeout: 300
|
||||
workers: 64
|
||||
limit: 0
|
||||
shuffle_choices: true
|
||||
use_theorem: false
|
||||
use_sketch: false
|
||||
@@ -0,0 +1,34 @@
|
||||
_base_: ../_base_/default.yaml
|
||||
|
||||
model:
|
||||
reasoning_effort: medium
|
||||
|
||||
train:
|
||||
batch_size: 40
|
||||
accumulation: 1
|
||||
|
||||
gradient:
|
||||
minibatch_size: 8
|
||||
merge_batch_size: 8
|
||||
|
||||
optimizer:
|
||||
learning_rate: 4
|
||||
|
||||
env:
|
||||
name: officeqa
|
||||
skill_init: skillopt/envs/officeqa/skills/initial.md
|
||||
split_mode: split_dir
|
||||
split_dir: data/officeqa_split
|
||||
data_dirs:
|
||||
- data/officeqa_docs_official
|
||||
workers: 4
|
||||
max_tool_turns: 24
|
||||
max_completion_tokens: 16384
|
||||
search_mode: offline
|
||||
max_queries_per_turn: 4
|
||||
search_api_url: http://apisix.westus2.cloudapp.azure.com/search_tool/search
|
||||
search_auth_env: OFFICEQA_CUSTOM_SEARCH_AUTH
|
||||
search_provider: duckduckgo
|
||||
search_max_num_results: 4
|
||||
search_timeout_seconds: 20
|
||||
limit: 0
|
||||
@@ -0,0 +1,32 @@
|
||||
_base_: ../_base_/default.yaml
|
||||
|
||||
model:
|
||||
reasoning_effort: medium
|
||||
|
||||
train:
|
||||
train_size: 400
|
||||
batch_size: 40
|
||||
accumulation: 1
|
||||
|
||||
gradient:
|
||||
minibatch_size: 8
|
||||
merge_batch_size: 8
|
||||
|
||||
optimizer:
|
||||
learning_rate: 4
|
||||
|
||||
evaluation:
|
||||
sel_env_num: 0
|
||||
test_env_num: 0
|
||||
|
||||
env:
|
||||
name: searchqa
|
||||
skill_init: skillopt/envs/searchqa/skills/initial.md
|
||||
split_mode: split_dir
|
||||
split_dir: data/searchqa_split
|
||||
data_path: ""
|
||||
split_output_dir: ""
|
||||
max_turns: 1
|
||||
max_completion_tokens: 16384
|
||||
workers: 24
|
||||
limit: 0
|
||||
@@ -0,0 +1,34 @@
|
||||
_base_: ../_base_/default.yaml
|
||||
|
||||
model:
|
||||
reasoning_effort: medium
|
||||
|
||||
train:
|
||||
train_size: 80
|
||||
batch_size: 40
|
||||
accumulation: 1
|
||||
|
||||
gradient:
|
||||
minibatch_size: 8
|
||||
merge_batch_size: 8
|
||||
|
||||
optimizer:
|
||||
learning_rate: 4
|
||||
|
||||
evaluation:
|
||||
sel_env_num: 0
|
||||
test_env_num: 0
|
||||
|
||||
env:
|
||||
name: spreadsheetbench
|
||||
skill_init: skillopt/envs/spreadsheetbench/skills/initial.md
|
||||
split_mode: split_dir
|
||||
split_dir: data/spreadsheetbench_split
|
||||
data_path: ""
|
||||
split_output_dir: ""
|
||||
data_root: data/spreadsheetbench_verified_400
|
||||
mode: multi
|
||||
max_turns: 30
|
||||
max_completion_tokens: 16384
|
||||
exec_timeout: 600
|
||||
workers: 24
|
||||
+237
@@ -0,0 +1,237 @@
|
||||
# Data Manifests
|
||||
|
||||
This directory releases lightweight split manifests for the SkillOpt paper
|
||||
splits. These manifests are not full runnable benchmark payloads. To evaluate a
|
||||
benchmark, first materialize the full examples from the raw data source when
|
||||
needed, then point `--split_dir` at the split directory listed below.
|
||||
|
||||
In this README, "coverage" describes which part of the upstream benchmark the
|
||||
manifest references. It does not mean the released manifest directory contains
|
||||
the full runnable examples.
|
||||
|
||||
## Layout
|
||||
|
||||
Every released manifest directory uses the same file layout:
|
||||
|
||||
```text
|
||||
data/<benchmark>_<manifest_type>/
|
||||
|-- split_manifest.json
|
||||
|-- train/items.json
|
||||
|-- val/items.json
|
||||
`-- test/items.json
|
||||
```
|
||||
|
||||
`split_manifest.json` records source metadata, split counts, and item fields.
|
||||
Each `items.json` contains only stable IDs or source-path hints.
|
||||
|
||||
## Released Splits
|
||||
|
||||
| Manifest directory | Benchmark | Counts | Coverage | Raw data source | `split_dir` |
|
||||
|---|---|---:|---|---|---|
|
||||
| `searchqa_id_split/` | SearchQA | 400 / 200 / 1400 | Official HF dataset IDs | [lucadiliello/searchqa](https://huggingface.co/datasets/lucadiliello/searchqa) | `data/searchqa_split` |
|
||||
| `livemathematicianbench_id_split/` | LiveMathematicianBench | 35 / 18 / 124 | Four official monthly files | [LiveMathematicianBench/LiveMathematicianBench](https://huggingface.co/datasets/LiveMathematicianBench/LiveMathematicianBench) | `data/livemathematicianbench_split` |
|
||||
| `docvqa_id_split/` | DocVQA | 107 / 53 / 374 | 10% subset of validation | [lmms-lab/DocVQA](https://huggingface.co/datasets/lmms-lab/DocVQA) | `data/docvqa/splits` |
|
||||
| `officeqa_id_split/` | OfficeQA | 50 / 24 / 172 | OfficeQA Full | [databricks/officeqa](https://huggingface.co/datasets/databricks/officeqa) | `data/officeqa_split` |
|
||||
| `spreadsheetbench_id_split/` | SpreadsheetBench | 80 / 40 / 280 | SpreadsheetBench Verified 400 | [KAKA22/SpreadsheetBench](https://huggingface.co/datasets/KAKA22/SpreadsheetBench) | `data/spreadsheetbench_split` |
|
||||
| `alfworld_path_split/` | ALFWorld | 39 / 18 / 134 | ALFWorld `json_2.1.1` paths | [alfworld/alfworld](https://github.com/alfworld/alfworld) | `data/alfworld_path_split` |
|
||||
|
||||
Counts are ordered as train / val / test.
|
||||
|
||||
## Direct Use
|
||||
|
||||
Only `alfworld_path_split/` can be used directly as `--split_dir` from this
|
||||
release, because the ALFWorld loader reads `gamefile` and `task_type` from the
|
||||
split items.
|
||||
|
||||
This does not mean the ALFWorld raw data is included. You still need to
|
||||
download ALFWorld separately with `alfworld-download` and set `$ALFWORLD_DATA`
|
||||
to the data root containing `json_2.1.1`.
|
||||
|
||||
The other manifest directories are lookup manifests. They intentionally omit
|
||||
full example fields such as questions, answers, contexts, images, or task
|
||||
instructions. Materialize those benchmarks into the `split_dir` paths listed
|
||||
above before running SkillOpt.
|
||||
|
||||
## Lookup Keys
|
||||
|
||||
The manifests are sufficient to locate the corresponding raw examples after
|
||||
the raw data has been downloaded or otherwise made available:
|
||||
|
||||
| Benchmark | Manifest lookup key |
|
||||
|---|---|
|
||||
| SearchQA | Match `items.json[].id` to the `key` field in `lucadiliello/searchqa`. |
|
||||
| LiveMathematicianBench | Open `source_file`, then match `no`; the manifest `id` is `<month>:<no>`. |
|
||||
| DocVQA | Match `questionId` within the official DocVQA `validation` split; `image_path` records the expected local image path. |
|
||||
| OfficeQA | Match `uid` in `officeqa_full.csv`; `source_files` and `source_docs` identify the supporting document. |
|
||||
| SpreadsheetBench | Match `id`; `spreadsheet_path` identifies the referenced spreadsheet directory. |
|
||||
| ALFWorld | Resolve `gamefile` relative to `$ALFWORLD_DATA`. |
|
||||
|
||||
## Manifest Item Examples
|
||||
|
||||
SearchQA:
|
||||
|
||||
```json
|
||||
{
|
||||
"id": "221c83e6630f4e7983da48fa28da1882"
|
||||
}
|
||||
```
|
||||
|
||||
LiveMathematicianBench:
|
||||
|
||||
```json
|
||||
{
|
||||
"id": "202602:22",
|
||||
"month": "202602",
|
||||
"no": 22,
|
||||
"paper_link": "http://arxiv.org/abs/2602.10700v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
}
|
||||
```
|
||||
|
||||
DocVQA:
|
||||
|
||||
```json
|
||||
{
|
||||
"id": "50877",
|
||||
"questionId": "50877",
|
||||
"docId": "14724",
|
||||
"image_path": "data/docvqa_images/q50877_d14724.png",
|
||||
"source_split": "validation"
|
||||
}
|
||||
```
|
||||
|
||||
OfficeQA:
|
||||
|
||||
```json
|
||||
{
|
||||
"id": "UID0002",
|
||||
"uid": "UID0002",
|
||||
"category": "easy",
|
||||
"source_files": "treasury_bulletin_1944_01.txt"
|
||||
}
|
||||
```
|
||||
|
||||
SpreadsheetBench:
|
||||
|
||||
```json
|
||||
{
|
||||
"id": "32438",
|
||||
"spreadsheet_path": "spreadsheet/32438",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
}
|
||||
```
|
||||
|
||||
ALFWorld:
|
||||
|
||||
```json
|
||||
{
|
||||
"id": "train:0000",
|
||||
"gamefile": "json_2.1.1/train/.../game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
}
|
||||
```
|
||||
|
||||
## Benchmark Notes
|
||||
|
||||
### SearchQA
|
||||
|
||||
`searchqa_id_split/` is an ID-only manifest. Each released `id` exactly matches
|
||||
the `key` field in `lucadiliello/searchqa`.
|
||||
|
||||
To materialize the runnable SearchQA split used by
|
||||
`configs/searchqa/default.yaml`, install the optional dependency and run:
|
||||
|
||||
```bash
|
||||
python -m pip install 'skillopt[searchqa]'
|
||||
python scripts/materialize_searchqa.py
|
||||
```
|
||||
|
||||
This writes full examples to:
|
||||
|
||||
```text
|
||||
data/searchqa_split
|
||||
```
|
||||
|
||||
Materialized examples must include the fields consumed by the SearchQA
|
||||
environment, including:
|
||||
|
||||
```text
|
||||
question
|
||||
context
|
||||
answers
|
||||
```
|
||||
|
||||
### LiveMathematicianBench
|
||||
|
||||
`livemathematicianbench_id_split/` was generated from these raw files:
|
||||
|
||||
```text
|
||||
data/202511/qa_202511_final.json
|
||||
data/202512/qa_202512_final.json
|
||||
data/202601/qa_202601_final.json
|
||||
data/202602/qa_202602_final.json
|
||||
```
|
||||
|
||||
The manifest stores IDs in the loader format:
|
||||
|
||||
```text
|
||||
<month>:<no>
|
||||
```
|
||||
|
||||
Materialized examples must include:
|
||||
|
||||
```text
|
||||
question
|
||||
choices
|
||||
correct_choice
|
||||
theorem_type
|
||||
theorem
|
||||
sketch
|
||||
paper_link
|
||||
```
|
||||
|
||||
### DocVQA
|
||||
|
||||
`docvqa_id_split/` records `docvqa_validation_10pct`: a 10% subset sampled from
|
||||
the official DocVQA `validation` split.
|
||||
|
||||
```text
|
||||
source_split: validation
|
||||
docvqa_validation_10pct: train=107, val=53, test=374
|
||||
```
|
||||
|
||||
Each manifest item contains question/document IDs plus image location metadata.
|
||||
Materialized examples must provide `question`, `answer` or `ground_truth`, and
|
||||
an `image_path` that resolves locally.
|
||||
|
||||
### OfficeQA
|
||||
|
||||
`officeqa_id_split/` records the split over OfficeQA Full
|
||||
(`officeqa_full.csv`). The official OfficeQA CSVs are gated on Hugging Face, so
|
||||
materialization requires authorized access.
|
||||
|
||||
Each manifest item contains `uid`, `category`, `source_files`, and
|
||||
`source_docs` hints. Materialized examples must include `question` and
|
||||
`ground_truth` or `answer`.
|
||||
|
||||
### SpreadsheetBench
|
||||
|
||||
`spreadsheetbench_id_split/` records the split over SpreadsheetBench Verified
|
||||
400, from `spreadsheetbench_verified_400.tar.gz`.
|
||||
|
||||
Each manifest item contains task identity metadata such as `id`,
|
||||
`spreadsheet_path`, and `instruction_type`. Materialization must also place the
|
||||
referenced spreadsheet directories at:
|
||||
|
||||
```text
|
||||
data/spreadsheetbench_verified_400
|
||||
```
|
||||
|
||||
### ALFWorld
|
||||
|
||||
`alfworld_path_split/` records `gamefile` paths relative to `$ALFWORLD_DATA`.
|
||||
The source payload is `json_2.1.1`, which must be downloaded separately with
|
||||
`alfworld-download`.
|
||||
|
||||
This manifest can be used directly as `--split_dir` after `$ALFWORLD_DATA`
|
||||
points to the local ALFWorld data root containing `json_2.1.1`.
|
||||
@@ -0,0 +1,29 @@
|
||||
{
|
||||
"benchmark": "ALFWorld",
|
||||
"manifest_type": "path_split",
|
||||
"source_repo": "alfworld/alfworld",
|
||||
"source_repo_type": "repository",
|
||||
"source_url": "https://github.com/alfworld/alfworld",
|
||||
"source_file": "json_2.1.1",
|
||||
"source_method": "generated by alfworld-download",
|
||||
"source_split_files": [
|
||||
"split_train.json",
|
||||
"split_val.json",
|
||||
"split_test.json"
|
||||
],
|
||||
"counts": {
|
||||
"train": 39,
|
||||
"val": 18,
|
||||
"test": 134
|
||||
},
|
||||
"item_fields": [
|
||||
"id",
|
||||
"gamefile",
|
||||
"task_type"
|
||||
],
|
||||
"path_root": "$ALFWORLD_DATA",
|
||||
"notes": [
|
||||
"This is a path manifest, not the ALFWorld game payload.",
|
||||
"The gamefile field is relative to ALFWORLD_DATA and must be expanded before direct use as split_dir data."
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,672 @@
|
||||
[
|
||||
{
|
||||
"id": "test:0000",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-AlarmClock-None-DeskLamp-308/trial_T20190908_222917_366542/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0001",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-AlarmClock-None-DeskLamp-308/trial_T20190908_222933_607649/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0002",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-AlarmClock-None-DeskLamp-308/trial_T20190908_222951_616606/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0003",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-Book-None-DeskLamp-308/trial_T20190908_020029_636862/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0004",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-Book-None-DeskLamp-308/trial_T20190908_020048_814402/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0005",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-Book-None-DeskLamp-308/trial_T20190908_144951_587345/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0006",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-Bowl-None-DeskLamp-308/trial_T20190907_133919_856963/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0007",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-Bowl-None-DeskLamp-308/trial_T20190907_133935_066606/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0008",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-Bowl-None-DeskLamp-308/trial_T20190907_133953_562557/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0009",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-CD-None-DeskLamp-308/trial_T20190908_141942_810052/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0010",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-CD-None-DeskLamp-308/trial_T20190908_141958_463362/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0011",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-CD-None-DeskLamp-308/trial_T20190908_142046_281296/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0012",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-Mug-None-DeskLamp-308/trial_T20190908_161733_213242/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0013",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-Mug-None-DeskLamp-308/trial_T20190908_201421_021646/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0014",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-Mug-None-DeskLamp-308/trial_T20190908_201444_037645/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0015",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-Pencil-None-DeskLamp-308/trial_T20190908_220545_153480/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0016",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-Pencil-None-DeskLamp-308/trial_T20190908_220604_010430/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0017",
|
||||
"gamefile": "json_2.1.1/valid_unseen/look_at_obj_in_light-Pencil-None-DeskLamp-308/trial_T20190908_220656_510400/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "test:0018",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-Mug-None-Desk-308/trial_T20190908_125200_737896/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0019",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-Mug-None-Desk-308/trial_T20190909_203041_433487/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0020",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-Mug-None-Desk-308/trial_T20190909_210238_431966/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0021",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-Pencil-None-Shelf-308/trial_T20190908_121952_610012/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0022",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-Pencil-None-Shelf-308/trial_T20190908_122024_052056/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0023",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-Pencil-None-Shelf-308/trial_T20190908_122154_042763/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0024",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-PepperShaker-None-Drawer-10/trial_T20190906_184021_215264/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0025",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-PepperShaker-None-Drawer-10/trial_T20190918_154326_823501/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0026",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-PepperShaker-None-Drawer-10/trial_T20190918_154424_844749/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0027",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-SaltShaker-None-Cabinet-10/trial_T20190906_191429_743650/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0028",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-SaltShaker-None-Cabinet-10/trial_T20190906_191445_723170/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0029",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-SaltShaker-None-Cabinet-10/trial_T20190906_191501_563086/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0030",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-SaltShaker-None-Drawer-10/trial_T20190909_021613_077537/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0031",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-SaltShaker-None-Drawer-10/trial_T20190909_021650_880235/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0032",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-SaltShaker-None-Drawer-10/trial_T20190909_021728_339782/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0033",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-SoapBottle-None-Toilet-424/trial_T20190907_004321_405868/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0034",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-SoapBottle-None-Toilet-424/trial_T20190907_004351_281384/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0035",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-SoapBottle-None-Toilet-424/trial_T20190907_004404_604165/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0036",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-Vase-None-Safe-219/trial_T20190908_205204_244321/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0037",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-Vase-None-Safe-219/trial_T20190908_205221_748352/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0038",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-Vase-None-Safe-219/trial_T20190908_205246_776817/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0039",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-Watch-None-Safe-219/trial_T20190907_074524_006355/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0040",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-Watch-None-Safe-219/trial_T20190907_074556_124850/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0041",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_and_place_simple-Watch-None-Safe-219/trial_T20190907_074643_810052/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "test:0042",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Bowl-None-Cabinet-10/trial_T20190909_061130_844814/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0043",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Bowl-None-Cabinet-10/trial_T20190909_061158_110530/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0044",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Bowl-None-Cabinet-10/trial_T20190909_061232_368489/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0045",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Cloth-None-Cabinet-424/trial_T20190908_022321_380927/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0046",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Cloth-None-Cabinet-424/trial_T20190908_022436_073995/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0047",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Cloth-None-CounterTop-424/trial_T20190908_100632_546757/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0048",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Cloth-None-CounterTop-424/trial_T20190908_114340_674467/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0049",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Egg-None-Microwave-10/trial_T20190909_120554_888709/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0050",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Egg-None-Microwave-10/trial_T20190909_120632_691361/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0051",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Egg-None-Microwave-10/trial_T20190909_120712_273910/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0052",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Knife-None-CounterTop-10/trial_T20190909_110347_624008/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0053",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Knife-None-CounterTop-10/trial_T20190909_110445_675754/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0054",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Knife-None-CounterTop-10/trial_T20190909_110531_148235/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0055",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Mug-None-CoffeeMachine-10/trial_T20190907_221208_560499/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0056",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Mug-None-CoffeeMachine-10/trial_T20190907_221300_362511/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0057",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Mug-None-CoffeeMachine-10/trial_T20190907_221355_558505/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0058",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Pan-None-CounterTop-10/trial_T20190908_032434_013084/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0059",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Pan-None-CounterTop-10/trial_T20190908_032518_891433/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0060",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Pan-None-CounterTop-10/trial_T20190908_032543_712058/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0061",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Plate-None-CounterTop-10/trial_T20190908_213356_017769/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0062",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Plate-None-CounterTop-10/trial_T20190908_213420_728917/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0063",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Plate-None-CounterTop-10/trial_T20190908_213533_897289/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0064",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-SoapBar-None-Cabinet-424/trial_T20190908_214926_337906/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0065",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-SoapBar-None-Cabinet-424/trial_T20190908_214946_567644/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0066",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-SoapBar-None-Cabinet-424/trial_T20190908_215019_162873/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0067",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-SoapBar-None-CounterTop-424/trial_T20190907_074045_109439/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0068",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-SoapBar-None-CounterTop-424/trial_T20190907_074106_050405/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0069",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-SoapBar-None-CounterTop-424/trial_T20190907_074124_966890/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0070",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Spatula-None-Drawer-10/trial_T20190907_080730_211959/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0071",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Spatula-None-Drawer-10/trial_T20190907_080800_275989/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0072",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_clean_then_place_in_recep-Spatula-None-Drawer-10/trial_T20190907_080825_222432/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0073",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Bread-None-CounterTop-10/trial_T20190908_091747_866951/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0074",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Bread-None-CounterTop-10/trial_T20190908_091811_414150/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0075",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Bread-None-CounterTop-10/trial_T20190908_091835_825830/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0076",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Lettuce-None-CounterTop-10/trial_T20190909_123133_763972/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0077",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Lettuce-None-CounterTop-10/trial_T20190909_174807_646433/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0078",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Lettuce-None-CounterTop-10/trial_T20190909_174840_771703/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0079",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Mug-None-Cabinet-10/trial_T20190909_121559_082363/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0080",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Mug-None-Cabinet-10/trial_T20190909_121635_622676/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0081",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Mug-None-Cabinet-10/trial_T20190909_121710_650938/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0082",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Mug-None-CoffeeMachine-10/trial_T20190907_183715_299073/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0083",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Mug-None-CoffeeMachine-10/trial_T20190907_183807_477267/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0084",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Mug-None-CoffeeMachine-10/trial_T20190907_183853_958104/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0085",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Pan-None-CounterTop-10/trial_T20190908_114545_244903/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0086",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Pan-None-CounterTop-10/trial_T20190908_114622_738670/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0087",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Pan-None-CounterTop-10/trial_T20190908_114656_768805/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0088",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Potato-None-Microwave-10/trial_T20190907_033157_424297/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0089",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Potato-None-Microwave-10/trial_T20190907_033228_194678/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0090",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Potato-None-Microwave-10/trial_T20190907_033306_962974/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0091",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Tomato-None-Microwave-10/trial_T20190909_102608_318800/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0092",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Tomato-None-Microwave-10/trial_T20190909_102644_926781/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0093",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_cool_then_place_in_recep-Tomato-None-Microwave-10/trial_T20190909_102710_795182/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0094",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Apple-None-Fridge-10/trial_T20190906_182259_116320/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0095",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Apple-None-Fridge-10/trial_T20190906_182353_418140/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0096",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Apple-None-Fridge-10/trial_T20190906_182435_622538/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0097",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Apple-None-GarbageCan-10/trial_T20190908_145050_918567/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0098",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Apple-None-GarbageCan-10/trial_T20190908_145143_820541/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0099",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Apple-None-GarbageCan-10/trial_T20190908_145356_918528/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0100",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Cup-None-Cabinet-10/trial_T20190907_083346_800823/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0101",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Cup-None-Cabinet-10/trial_T20190907_083429_887065/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0102",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Cup-None-Cabinet-10/trial_T20190907_083507_594820/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0103",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Egg-None-GarbageCan-10/trial_T20190908_113432_673307/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0104",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Egg-None-GarbageCan-10/trial_T20190908_113523_123938/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0105",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Egg-None-GarbageCan-10/trial_T20190908_113610_425142/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0106",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Mug-None-Cabinet-10/trial_T20190909_021100_341887/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0107",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Mug-None-Cabinet-10/trial_T20190909_021200_669381/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0108",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Mug-None-Cabinet-10/trial_T20190909_021247_306737/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0109",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Mug-None-CoffeeMachine-10/trial_T20190907_171806_406231/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0110",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Mug-None-CoffeeMachine-10/trial_T20190907_171850_960211/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0111",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Mug-None-CoffeeMachine-10/trial_T20190907_171933_349922/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0112",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Potato-None-GarbageCan-10/trial_T20190907_161745_664033/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0113",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Potato-None-GarbageCan-10/trial_T20190907_161853_945788/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0114",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Tomato-None-GarbageCan-10/trial_T20190908_225046_020282/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0115",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Tomato-None-GarbageCan-10/trial_T20190908_225359_617900/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0116",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_heat_then_place_in_recep-Tomato-None-GarbageCan-10/trial_T20190908_225453_272533/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "test:0117",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-CD-None-Safe-308/trial_T20190907_050942_897916/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0118",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-CD-None-Safe-308/trial_T20190907_051013_060265/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0119",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-CD-None-Safe-308/trial_T20190907_051056_585414/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0120",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-KeyChain-None-Safe-219/trial_T20190909_011803_423115/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0121",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-KeyChain-None-Safe-219/trial_T20190909_012027_782483/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0122",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-PepperShaker-None-Drawer-10/trial_T20190908_010306_215435/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0123",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-PepperShaker-None-Drawer-10/trial_T20190912_221016_460197/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0124",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-PepperShaker-None-Drawer-10/trial_T20190912_221141_608117/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0125",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-Pillow-None-Sofa-219/trial_T20190907_163240_345855/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0126",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-Pillow-None-Sofa-219/trial_T20190907_163327_486300/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0127",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-Pillow-None-Sofa-219/trial_T20190907_163408_914117/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0128",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-SoapBar-None-Cabinet-424/trial_T20190909_081720_491733/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0129",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-SoapBar-None-Cabinet-424/trial_T20190909_081746_857594/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0130",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-SoapBar-None-GarbageCan-424/trial_T20190909_064053_839817/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0131",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-SoapBar-None-GarbageCan-424/trial_T20190909_064221_368939/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0132",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-SoapBar-None-GarbageCan-424/trial_T20190909_064309_357168/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "test:0133",
|
||||
"gamefile": "json_2.1.1/valid_unseen/pick_two_obj_and_place-ToiletPaper-None-Cabinet-424/trial_T20190906_202926_527010/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,197 @@
|
||||
[
|
||||
{
|
||||
"id": "train:0000",
|
||||
"gamefile": "json_2.1.1/train/look_at_obj_in_light-AlarmClock-None-DeskLamp-305/trial_T20190908_082736_108723/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "train:0001",
|
||||
"gamefile": "json_2.1.1/train/look_at_obj_in_light-CD-None-DeskLamp-304/trial_T20190907_185649_782438/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "train:0002",
|
||||
"gamefile": "json_2.1.1/train/look_at_obj_in_light-CD-None-DeskLamp-320/trial_T20190907_224439_174735/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "train:0003",
|
||||
"gamefile": "json_2.1.1/train/look_at_obj_in_light-Pillow-None-DeskLamp-316/trial_T20190908_232421_645610/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "train:0004",
|
||||
"gamefile": "json_2.1.1/train/look_at_obj_in_light-Statue-None-DeskLamp-319/trial_T20190907_035546_167548/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "train:0005",
|
||||
"gamefile": "json_2.1.1/train/pick_and_place_simple-CellPhone-None-Shelf-313/trial_T20190908_123725_452958/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "train:0006",
|
||||
"gamefile": "json_2.1.1/train/pick_and_place_simple-Newspaper-None-Sofa-211/trial_T20190906_175004_203092/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "train:0007",
|
||||
"gamefile": "json_2.1.1/train/pick_and_place_simple-Pencil-None-Desk-302/trial_T20190908_032836_462632/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "train:0008",
|
||||
"gamefile": "json_2.1.1/train/pick_and_place_simple-SoapBar-None-GarbageCan-416/trial_T20190908_020839_714699/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "train:0009",
|
||||
"gamefile": "json_2.1.1/train/pick_and_place_simple-Statue-None-CoffeeTable-222/trial_T20190907_131249_788749/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "train:0010",
|
||||
"gamefile": "json_2.1.1/train/pick_and_place_simple-ToiletPaper-None-ToiletPaperHanger-406/trial_T20190908_122807_136741/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "train:0011",
|
||||
"gamefile": "json_2.1.1/train/pick_and_place_simple-ToiletPaper-None-ToiletPaperHanger-415/trial_T20190908_050443_333939/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "train:0012",
|
||||
"gamefile": "json_2.1.1/train/pick_clean_then_place_in_recep-Apple-None-DiningTable-4/trial_T20190908_104413_450768/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0013",
|
||||
"gamefile": "json_2.1.1/train/pick_clean_then_place_in_recep-DishSponge-None-Shelf-20/trial_T20190907_222429_992578/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0014",
|
||||
"gamefile": "json_2.1.1/train/pick_clean_then_place_in_recep-DishSponge-None-Shelf-401/trial_T20190908_072225_397518/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0015",
|
||||
"gamefile": "json_2.1.1/train/pick_clean_then_place_in_recep-Kettle-None-Cabinet-2/trial_T20190909_043103_418752/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0016",
|
||||
"gamefile": "json_2.1.1/train/pick_clean_then_place_in_recep-Knife-None-Drawer-22/trial_T20190907_224827_746945/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0017",
|
||||
"gamefile": "json_2.1.1/train/pick_clean_then_place_in_recep-Lettuce-None-DiningTable-20/trial_T20190906_191148_519826/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0018",
|
||||
"gamefile": "json_2.1.1/train/pick_clean_then_place_in_recep-Lettuce-None-Fridge-13/trial_T20190908_203022_601787/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0019",
|
||||
"gamefile": "json_2.1.1/train/pick_clean_then_place_in_recep-Plate-None-Fridge-5/trial_T20190909_112954_869911/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0020",
|
||||
"gamefile": "json_2.1.1/train/pick_clean_then_place_in_recep-Spoon-None-DiningTable-18/trial_T20190909_102159_277894/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0021",
|
||||
"gamefile": "json_2.1.1/train/pick_cool_then_place_in_recep-Bread-None-CounterTop-1/trial_T20190908_212439_711334/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0022",
|
||||
"gamefile": "json_2.1.1/train/pick_cool_then_place_in_recep-Bread-None-CounterTop-15/trial_T20190909_085448_256298/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0023",
|
||||
"gamefile": "json_2.1.1/train/pick_cool_then_place_in_recep-Bread-None-CounterTop-16/trial_T20190908_143948_082471/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0024",
|
||||
"gamefile": "json_2.1.1/train/pick_cool_then_place_in_recep-Pan-None-StoveBurner-27/trial_T20190906_212619_469871/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0025",
|
||||
"gamefile": "json_2.1.1/train/pick_cool_then_place_in_recep-Plate-None-DiningTable-17/trial_T20190909_122939_032098/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0026",
|
||||
"gamefile": "json_2.1.1/train/pick_cool_then_place_in_recep-Pot-None-CounterTop-1/trial_T20190909_124252_504581/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0027",
|
||||
"gamefile": "json_2.1.1/train/pick_heat_then_place_in_recep-Apple-None-Fridge-20/trial_T20190908_013911_274341/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0028",
|
||||
"gamefile": "json_2.1.1/train/pick_heat_then_place_in_recep-Egg-None-CounterTop-12/trial_T20190908_215527_416490/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0029",
|
||||
"gamefile": "json_2.1.1/train/pick_heat_then_place_in_recep-Mug-None-CoffeeMachine-1/trial_T20190907_222924_821086/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0030",
|
||||
"gamefile": "json_2.1.1/train/pick_heat_then_place_in_recep-Mug-None-CoffeeMachine-28/trial_T20190908_062730_537428/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0031",
|
||||
"gamefile": "json_2.1.1/train/pick_heat_then_place_in_recep-Plate-None-Cabinet-13/trial_T20190907_062749_759882/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0032",
|
||||
"gamefile": "json_2.1.1/train/pick_heat_then_place_in_recep-Potato-None-Fridge-2/trial_T20190909_030845_198194/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0033",
|
||||
"gamefile": "json_2.1.1/train/pick_heat_then_place_in_recep-Tomato-None-CounterTop-26/trial_T20190907_005525_499114/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "train:0034",
|
||||
"gamefile": "json_2.1.1/train/pick_two_obj_and_place-CD-None-Drawer-319/trial_T20190907_145515_348252/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "train:0035",
|
||||
"gamefile": "json_2.1.1/train/pick_two_obj_and_place-Candle-None-Drawer-427/trial_T20190909_043917_251333/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "train:0036",
|
||||
"gamefile": "json_2.1.1/train/pick_two_obj_and_place-KeyChain-None-ArmChair-222/trial_T20190909_100312_677332/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "train:0037",
|
||||
"gamefile": "json_2.1.1/train/pick_two_obj_and_place-Newspaper-None-Sofa-212/trial_T20190908_112632_208041/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "train:0038",
|
||||
"gamefile": "json_2.1.1/train/pick_two_obj_and_place-SaltShaker-None-SideTable-21/trial_T20190909_041626_844806/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,92 @@
|
||||
[
|
||||
{
|
||||
"id": "val:0000",
|
||||
"gamefile": "json_2.1.1/valid_seen/look_at_obj_in_light-AlarmClock-None-DeskLamp-323/trial_T20190909_044715_250790/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "val:0001",
|
||||
"gamefile": "json_2.1.1/valid_seen/look_at_obj_in_light-Bowl-None-DeskLamp-301/trial_T20190909_150719_492274/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "val:0002",
|
||||
"gamefile": "json_2.1.1/valid_seen/look_at_obj_in_light-Pillow-None-DeskLamp-323/trial_T20190908_053153_077977/game.tw-pddl",
|
||||
"task_type": "look_at_obj_in_light"
|
||||
},
|
||||
{
|
||||
"id": "val:0003",
|
||||
"gamefile": "json_2.1.1/valid_seen/pick_and_place_simple-Mug-None-SideTable-329/trial_T20190909_032318_169393/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "val:0004",
|
||||
"gamefile": "json_2.1.1/valid_seen/pick_and_place_simple-Mug-None-SideTable-329/trial_T20190909_032340_274147/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "val:0005",
|
||||
"gamefile": "json_2.1.1/valid_seen/pick_and_place_simple-Pencil-None-Desk-310/trial_T20190909_113054_894334/game.tw-pddl",
|
||||
"task_type": "pick_and_place_simple"
|
||||
},
|
||||
{
|
||||
"id": "val:0006",
|
||||
"gamefile": "json_2.1.1/valid_seen/pick_clean_then_place_in_recep-ButterKnife-None-Drawer-30/trial_T20190908_052007_212776/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "val:0007",
|
||||
"gamefile": "json_2.1.1/valid_seen/pick_clean_then_place_in_recep-ButterKnife-None-Drawer-8/trial_T20190909_124425_112757/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "val:0008",
|
||||
"gamefile": "json_2.1.1/valid_seen/pick_clean_then_place_in_recep-SoapBar-None-Cabinet-402/trial_T20190908_055221_984342/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "val:0009",
|
||||
"gamefile": "json_2.1.1/valid_seen/pick_clean_then_place_in_recep-SoapBar-None-Toilet-410/trial_T20190906_201106_979461/game.tw-pddl",
|
||||
"task_type": "pick_clean_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "val:0010",
|
||||
"gamefile": "json_2.1.1/valid_seen/pick_cool_then_place_in_recep-Apple-None-Microwave-19/trial_T20190906_210937_878489/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "val:0011",
|
||||
"gamefile": "json_2.1.1/valid_seen/pick_cool_then_place_in_recep-Plate-None-CounterTop-1/trial_T20190906_205324_559361/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "val:0012",
|
||||
"gamefile": "json_2.1.1/valid_seen/pick_cool_then_place_in_recep-Tomato-None-Microwave-18/trial_T20190909_012524_159092/game.tw-pddl",
|
||||
"task_type": "pick_cool_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "val:0013",
|
||||
"gamefile": "json_2.1.1/valid_seen/pick_heat_then_place_in_recep-Apple-None-DiningTable-26/trial_T20190907_060234_011675/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "val:0014",
|
||||
"gamefile": "json_2.1.1/valid_seen/pick_heat_then_place_in_recep-Tomato-None-Fridge-15/trial_T20190909_020200_054379/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "val:0015",
|
||||
"gamefile": "json_2.1.1/valid_seen/pick_heat_then_place_in_recep-Tomato-None-Fridge-23/trial_T20190909_082320_103350/game.tw-pddl",
|
||||
"task_type": "pick_heat_then_place_in_recep"
|
||||
},
|
||||
{
|
||||
"id": "val:0016",
|
||||
"gamefile": "json_2.1.1/valid_seen/pick_two_obj_and_place-Book-None-Desk-313/trial_T20190908_125930_920681/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
},
|
||||
{
|
||||
"id": "val:0017",
|
||||
"gamefile": "json_2.1.1/valid_seen/pick_two_obj_and_place-CreditCard-None-Safe-323/trial_T20190907_001129_214240/game.tw-pddl",
|
||||
"task_type": "pick_two_obj_and_place"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,36 @@
|
||||
{
|
||||
"benchmark": "DocVQA",
|
||||
"manifest_type": "id_split",
|
||||
"source_repo": "lmms-lab/DocVQA",
|
||||
"source_repo_type": "dataset",
|
||||
"source_url": "https://huggingface.co/datasets/lmms-lab/DocVQA",
|
||||
"source_revision": "539088ef8a8ada01ac8e2e6d4e372586748a265e",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"source_split_name": "docvqa_validation_10pct",
|
||||
"split_method": "10% subset sampled from the DocVQA validation split",
|
||||
"counts": {
|
||||
"train": 107,
|
||||
"val": 53,
|
||||
"test": 374
|
||||
},
|
||||
"item_fields": [
|
||||
"id",
|
||||
"questionId",
|
||||
"docId",
|
||||
"image_path",
|
||||
"ucsf_document_id",
|
||||
"ucsf_document_page_no",
|
||||
"topic",
|
||||
"source_dataset",
|
||||
"source_config",
|
||||
"source_split",
|
||||
"sample_seed"
|
||||
],
|
||||
"notes": [
|
||||
"This is a split manifest, not the full DocVQA payload.",
|
||||
"Materialize full CSV rows and image files before evaluation.",
|
||||
"This manifest corresponds to docvqa_validation_10pct.",
|
||||
"All released train/val/test items originate from a 10% subset of the official DocVQA validation split."
|
||||
]
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,691 @@
|
||||
[
|
||||
{
|
||||
"id": "62409",
|
||||
"questionId": "62409",
|
||||
"docId": "8554",
|
||||
"image_path": "data/docvqa_images/q62409_d8554.png",
|
||||
"ucsf_document_id": "pgjw0227",
|
||||
"ucsf_document_page_no": "5",
|
||||
"topic": "table/list",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "50961",
|
||||
"questionId": "50961",
|
||||
"docId": "549",
|
||||
"image_path": "data/docvqa_images/q50961_d549.png",
|
||||
"ucsf_document_id": "qtjf0226",
|
||||
"ucsf_document_page_no": "2",
|
||||
"topic": "free_text",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "46461",
|
||||
"questionId": "46461",
|
||||
"docId": "13361",
|
||||
"image_path": "data/docvqa_images/q46461_d13361.png",
|
||||
"ucsf_document_id": "ysbw0217",
|
||||
"ucsf_document_page_no": "5",
|
||||
"topic": "layout",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "3041",
|
||||
"questionId": "3041",
|
||||
"docId": "1204",
|
||||
"image_path": "data/docvqa_images/q3041_d1204.png",
|
||||
"ucsf_document_id": "xfjv0228",
|
||||
"ucsf_document_page_no": "3",
|
||||
"topic": "form",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "41716",
|
||||
"questionId": "41716",
|
||||
"docId": "11835",
|
||||
"image_path": "data/docvqa_images/q41716_d11835.png",
|
||||
"ucsf_document_id": "qjgn0226",
|
||||
"ucsf_document_page_no": "131",
|
||||
"topic": "form",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "61123",
|
||||
"questionId": "61123",
|
||||
"docId": "7374",
|
||||
"image_path": "data/docvqa_images/q61123_d7374.png",
|
||||
"ucsf_document_id": "mldg0227",
|
||||
"ucsf_document_page_no": "5",
|
||||
"topic": "layout",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "43068",
|
||||
"questionId": "43068",
|
||||
"docId": "12393",
|
||||
"image_path": "data/docvqa_images/q43068_d12393.png",
|
||||
"ucsf_document_id": "rmwn0226",
|
||||
"ucsf_document_page_no": "52",
|
||||
"topic": "figure/diagram",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "51221",
|
||||
"questionId": "51221",
|
||||
"docId": "764",
|
||||
"image_path": "data/docvqa_images/q51221_d764.png",
|
||||
"ucsf_document_id": "kzbn0226",
|
||||
"ucsf_document_page_no": "14",
|
||||
"topic": "layout",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "6397",
|
||||
"questionId": "6397",
|
||||
"docId": "2242",
|
||||
"image_path": "data/docvqa_images/q6397_d2242.png",
|
||||
"ucsf_document_id": "jkcn0000",
|
||||
"ucsf_document_page_no": "2",
|
||||
"topic": "form",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "57428",
|
||||
"questionId": "57428",
|
||||
"docId": "4779",
|
||||
"image_path": "data/docvqa_images/q57428_d4779.png",
|
||||
"ucsf_document_id": "rnbx0223",
|
||||
"ucsf_document_page_no": "208",
|
||||
"topic": "Image/Photo",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "3135",
|
||||
"questionId": "3135",
|
||||
"docId": "1221",
|
||||
"image_path": "data/docvqa_images/q3135_d1221.png",
|
||||
"ucsf_document_id": "ngph0227",
|
||||
"ucsf_document_page_no": "5",
|
||||
"topic": "layout",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "18819",
|
||||
"questionId": "18819",
|
||||
"docId": "5749",
|
||||
"image_path": "data/docvqa_images/q18819_d5749.png",
|
||||
"ucsf_document_id": "jhfd0079",
|
||||
"ucsf_document_page_no": "9",
|
||||
"topic": "form",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "15382",
|
||||
"questionId": "15382",
|
||||
"docId": "4890",
|
||||
"image_path": "data/docvqa_images/q15382_d4890.png",
|
||||
"ucsf_document_id": "kjvw0217",
|
||||
"ucsf_document_page_no": "3",
|
||||
"topic": "table/list",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "5772",
|
||||
"questionId": "5772",
|
||||
"docId": "1940",
|
||||
"image_path": "data/docvqa_images/q5772_d1940.png",
|
||||
"ucsf_document_id": "pzyw0224",
|
||||
"ucsf_document_page_no": "10",
|
||||
"topic": "form",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "49077",
|
||||
"questionId": "49077",
|
||||
"docId": "14179",
|
||||
"image_path": "data/docvqa_images/q49077_d14179.png",
|
||||
"ucsf_document_id": "nrxb0228",
|
||||
"ucsf_document_page_no": "3",
|
||||
"topic": "layout",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "58519",
|
||||
"questionId": "58519",
|
||||
"docId": "5347",
|
||||
"image_path": "data/docvqa_images/q58519_d5347.png",
|
||||
"ucsf_document_id": "sjbw0217",
|
||||
"ucsf_document_page_no": "11",
|
||||
"topic": "table/list",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "50720",
|
||||
"questionId": "50720",
|
||||
"docId": "281",
|
||||
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|
||||
"ucsf_document_id": "nrcj0037",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "56785",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "59653",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "61791",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "37229",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "60407",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "64420",
|
||||
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|
||||
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|
||||
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|
||||
"ucsf_document_id": "jnjm0223",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "47365",
|
||||
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|
||||
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|
||||
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|
||||
"ucsf_document_id": "nxym0227",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "47458",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "7621",
|
||||
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|
||||
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|
||||
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|
||||
"ucsf_document_id": "flxn0020",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "53575",
|
||||
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|
||||
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|
||||
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|
||||
"ucsf_document_id": "hsfn0020",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"ucsf_document_id": "lldj0224",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"ucsf_document_id": "xmww0217",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "1955",
|
||||
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|
||||
"docId": "892",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"ucsf_document_id": "rnbx0223",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "64306",
|
||||
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|
||||
"docId": "10149",
|
||||
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|
||||
"ucsf_document_id": "lpjm0223",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "64887",
|
||||
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|
||||
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|
||||
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|
||||
"ucsf_document_id": "szpg0227",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "58680",
|
||||
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|
||||
"docId": "5545",
|
||||
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|
||||
"ucsf_document_id": "hhwh0078",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "5287",
|
||||
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|
||||
"docId": "1785",
|
||||
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|
||||
"ucsf_document_id": "mtnh0227",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "55471",
|
||||
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|
||||
"docId": "4340",
|
||||
"image_path": "data/docvqa_images/q55471_d4340.png",
|
||||
"ucsf_document_id": "fsgj0223",
|
||||
"ucsf_document_page_no": "96",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"id": "53095",
|
||||
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|
||||
"docId": "296",
|
||||
"image_path": "data/docvqa_images/q53095_d296.png",
|
||||
"ucsf_document_id": "qhxj0037",
|
||||
"ucsf_document_page_no": "3",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"id": "53726",
|
||||
"questionId": "53726",
|
||||
"docId": "2008",
|
||||
"image_path": "data/docvqa_images/q53726_d2008.png",
|
||||
"ucsf_document_id": "hhnf0094",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "57321",
|
||||
"questionId": "57321",
|
||||
"docId": "4722",
|
||||
"image_path": "data/docvqa_images/q57321_d4722.png",
|
||||
"ucsf_document_id": "xybx0223",
|
||||
"ucsf_document_page_no": "32",
|
||||
"topic": "table/list",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
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|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "26659",
|
||||
"questionId": "26659",
|
||||
"docId": "7470",
|
||||
"image_path": "data/docvqa_images/q26659_d7470.png",
|
||||
"ucsf_document_id": "lhmg0227",
|
||||
"ucsf_document_page_no": "1",
|
||||
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|
||||
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|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "38920",
|
||||
"questionId": "38920",
|
||||
"docId": "11157",
|
||||
"image_path": "data/docvqa_images/q38920_d11157.png",
|
||||
"ucsf_document_id": "klnf0227",
|
||||
"ucsf_document_page_no": "1",
|
||||
"topic": "table/list|layout",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "50837",
|
||||
"questionId": "50837",
|
||||
"docId": "14742",
|
||||
"image_path": "data/docvqa_images/q50837_d14742.png",
|
||||
"ucsf_document_id": "ysmc0228",
|
||||
"ucsf_document_page_no": "4",
|
||||
"topic": "table/list",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "59615",
|
||||
"questionId": "59615",
|
||||
"docId": "6569",
|
||||
"image_path": "data/docvqa_images/q59615_d6569.png",
|
||||
"ucsf_document_id": "hnnp0227",
|
||||
"ucsf_document_page_no": "45",
|
||||
"topic": "handwritten|table/list|layout",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
},
|
||||
{
|
||||
"id": "58687",
|
||||
"questionId": "58687",
|
||||
"docId": "5545",
|
||||
"image_path": "data/docvqa_images/q58687_d5545.png",
|
||||
"ucsf_document_id": "hhwh0078",
|
||||
"ucsf_document_page_no": "1",
|
||||
"topic": "table/list",
|
||||
"source_dataset": "lmms-lab/DocVQA",
|
||||
"source_config": "DocVQA",
|
||||
"source_split": "validation",
|
||||
"sample_seed": "full_validation_5349"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,34 @@
|
||||
{
|
||||
"benchmark": "LiveMathematicianBench",
|
||||
"manifest_type": "id_split",
|
||||
"source_repo": "LiveMathematicianBench/LiveMathematicianBench",
|
||||
"source_repo_type": "dataset",
|
||||
"source_url": "https://huggingface.co/datasets/LiveMathematicianBench/LiveMathematicianBench",
|
||||
"source_revision": "b72450f6ce96c26158d64d945a5d31ef7727be41",
|
||||
"source_files": [
|
||||
"data/202511/qa_202511_final.json",
|
||||
"data/202512/qa_202512_final.json",
|
||||
"data/202601/qa_202601_final.json",
|
||||
"data/202602/qa_202602_final.json"
|
||||
],
|
||||
"split_mode": "ratio",
|
||||
"split_ratio": "2:1:7",
|
||||
"split_seed": 42,
|
||||
"counts": {
|
||||
"train": 35,
|
||||
"val": 18,
|
||||
"test": 124
|
||||
},
|
||||
"item_fields": [
|
||||
"id",
|
||||
"month",
|
||||
"no",
|
||||
"paper_link",
|
||||
"source_file"
|
||||
],
|
||||
"id_format": "<month>:<no>",
|
||||
"notes": [
|
||||
"This is an ID split manifest, not the full LiveMathematicianBench payload.",
|
||||
"Materialize full split items from the official LiveMathematicianBench raw qa_*_final.json files before evaluation."
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,870 @@
|
||||
[
|
||||
{
|
||||
"id": "202602:12",
|
||||
"month": "202602",
|
||||
"no": 12,
|
||||
"paper_link": "http://arxiv.org/abs/2602.07171v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202601:3",
|
||||
"month": "202601",
|
||||
"no": 3,
|
||||
"paper_link": "http://arxiv.org/abs/2601.01447v1",
|
||||
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|
||||
},
|
||||
{
|
||||
"id": "202511:4",
|
||||
"month": "202511",
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||||
"no": 4,
|
||||
"paper_link": "http://arxiv.org/abs/2511.23123v1",
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||||
"source_file": "data/202511/qa_202511_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202601:20",
|
||||
"month": "202601",
|
||||
"no": 20,
|
||||
"paper_link": "http://arxiv.org/abs/2601.13212v1",
|
||||
"source_file": "data/202601/qa_202601_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202601:42",
|
||||
"month": "202601",
|
||||
"no": 42,
|
||||
"paper_link": "http://arxiv.org/abs/2601.09348v1",
|
||||
"source_file": "data/202601/qa_202601_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202512:38",
|
||||
"month": "202512",
|
||||
"no": 38,
|
||||
"paper_link": "http://arxiv.org/abs/2512.19831v2",
|
||||
"source_file": "data/202512/qa_202512_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202512:4",
|
||||
"month": "202512",
|
||||
"no": 4,
|
||||
"paper_link": "http://arxiv.org/abs/2512.03141v1",
|
||||
"source_file": "data/202512/qa_202512_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:4",
|
||||
"month": "202602",
|
||||
"no": 4,
|
||||
"paper_link": "http://arxiv.org/abs/2602.14368v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202511:15",
|
||||
"month": "202511",
|
||||
"no": 15,
|
||||
"paper_link": "http://arxiv.org/abs/2511.17325v1",
|
||||
"source_file": "data/202511/qa_202511_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:32",
|
||||
"month": "202602",
|
||||
"no": 32,
|
||||
"paper_link": "http://arxiv.org/abs/2602.14817v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202512:51",
|
||||
"month": "202512",
|
||||
"no": 51,
|
||||
"paper_link": "http://arxiv.org/abs/2512.14581v1",
|
||||
"source_file": "data/202512/qa_202512_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202512:26",
|
||||
"month": "202512",
|
||||
"no": 26,
|
||||
"paper_link": "http://arxiv.org/abs/2512.19586v1",
|
||||
"source_file": "data/202512/qa_202512_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202601:13",
|
||||
"month": "202601",
|
||||
"no": 13,
|
||||
"paper_link": "http://arxiv.org/abs/2601.10017v1",
|
||||
"source_file": "data/202601/qa_202601_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:1",
|
||||
"month": "202602",
|
||||
"no": 1,
|
||||
"paper_link": "http://arxiv.org/abs/2602.23137v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202511:18",
|
||||
"month": "202511",
|
||||
"no": 18,
|
||||
"paper_link": "http://arxiv.org/abs/2511.10795v1",
|
||||
"source_file": "data/202511/qa_202511_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202512:5",
|
||||
"month": "202512",
|
||||
"no": 5,
|
||||
"paper_link": "http://arxiv.org/abs/2512.00348v1",
|
||||
"source_file": "data/202512/qa_202512_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202511:19",
|
||||
"month": "202511",
|
||||
"no": 19,
|
||||
"paper_link": "http://arxiv.org/abs/2511.06951v1",
|
||||
"source_file": "data/202511/qa_202511_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:40",
|
||||
"month": "202602",
|
||||
"no": 40,
|
||||
"paper_link": "http://arxiv.org/abs/2602.20462v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:29",
|
||||
"month": "202602",
|
||||
"no": 29,
|
||||
"paper_link": "http://arxiv.org/abs/2602.10676v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202512:35",
|
||||
"month": "202512",
|
||||
"no": 35,
|
||||
"paper_link": "http://arxiv.org/abs/2512.08840v1",
|
||||
"source_file": "data/202512/qa_202512_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202512:48",
|
||||
"month": "202512",
|
||||
"no": 48,
|
||||
"paper_link": "http://arxiv.org/abs/2512.03482v1",
|
||||
"source_file": "data/202512/qa_202512_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202512:52",
|
||||
"month": "202512",
|
||||
"no": 52,
|
||||
"paper_link": "http://arxiv.org/abs/2512.11246v1",
|
||||
"source_file": "data/202512/qa_202512_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202512:44",
|
||||
"month": "202512",
|
||||
"no": 44,
|
||||
"paper_link": "http://arxiv.org/abs/2512.10385v1",
|
||||
"source_file": "data/202512/qa_202512_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202511:28",
|
||||
"month": "202511",
|
||||
"no": 28,
|
||||
"paper_link": "http://arxiv.org/abs/2511.03812v1",
|
||||
"source_file": "data/202511/qa_202511_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202601:43",
|
||||
"month": "202601",
|
||||
"no": 43,
|
||||
"paper_link": "http://arxiv.org/abs/2601.22555v1",
|
||||
"source_file": "data/202601/qa_202601_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:9",
|
||||
"month": "202602",
|
||||
"no": 9,
|
||||
"paper_link": "http://arxiv.org/abs/2602.19882v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202512:23",
|
||||
"month": "202512",
|
||||
"no": 23,
|
||||
"paper_link": "http://arxiv.org/abs/2512.09180v1",
|
||||
"source_file": "data/202512/qa_202512_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:21",
|
||||
"month": "202602",
|
||||
"no": 21,
|
||||
"paper_link": "http://arxiv.org/abs/2602.10509v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202511:5",
|
||||
"month": "202511",
|
||||
"no": 5,
|
||||
"paper_link": "http://arxiv.org/abs/2511.20164v1",
|
||||
"source_file": "data/202511/qa_202511_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202601:35",
|
||||
"month": "202601",
|
||||
"no": 35,
|
||||
"paper_link": "http://arxiv.org/abs/2601.15606v1",
|
||||
"source_file": "data/202601/qa_202601_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:50",
|
||||
"month": "202602",
|
||||
"no": 50,
|
||||
"paper_link": "http://arxiv.org/abs/2602.05652v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202512:13",
|
||||
"month": "202512",
|
||||
"no": 13,
|
||||
"paper_link": "http://arxiv.org/abs/2512.22861v1",
|
||||
"source_file": "data/202512/qa_202512_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:49",
|
||||
"month": "202602",
|
||||
"no": 49,
|
||||
"paper_link": "http://arxiv.org/abs/2602.07167v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:18",
|
||||
"month": "202602",
|
||||
"no": 18,
|
||||
"paper_link": "http://arxiv.org/abs/2602.20124v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202601:15",
|
||||
"month": "202601",
|
||||
"no": 15,
|
||||
"paper_link": "http://arxiv.org/abs/2601.05327v1",
|
||||
"source_file": "data/202601/qa_202601_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202601:21",
|
||||
"month": "202601",
|
||||
"no": 21,
|
||||
"paper_link": "http://arxiv.org/abs/2601.04994v1",
|
||||
"source_file": "data/202601/qa_202601_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202601:32",
|
||||
"month": "202601",
|
||||
"no": 32,
|
||||
"paper_link": "http://arxiv.org/abs/2601.09183v1",
|
||||
"source_file": "data/202601/qa_202601_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:34",
|
||||
"month": "202602",
|
||||
"no": 34,
|
||||
"paper_link": "http://arxiv.org/abs/2602.21118v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:20",
|
||||
"month": "202602",
|
||||
"no": 20,
|
||||
"paper_link": "http://arxiv.org/abs/2602.16506v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:5",
|
||||
"month": "202602",
|
||||
"no": 5,
|
||||
"paper_link": "http://arxiv.org/abs/2602.09806v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202512:40",
|
||||
"month": "202512",
|
||||
"no": 40,
|
||||
"paper_link": "http://arxiv.org/abs/2512.16535v1",
|
||||
"source_file": "data/202512/qa_202512_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202511:22",
|
||||
"month": "202511",
|
||||
"no": 22,
|
||||
"paper_link": "http://arxiv.org/abs/2511.07607v2",
|
||||
"source_file": "data/202511/qa_202511_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202601:36",
|
||||
"month": "202601",
|
||||
"no": 36,
|
||||
"paper_link": "http://arxiv.org/abs/2601.12457v1",
|
||||
"source_file": "data/202601/qa_202601_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202512:49",
|
||||
"month": "202512",
|
||||
"no": 49,
|
||||
"paper_link": "http://arxiv.org/abs/2512.21565v1",
|
||||
"source_file": "data/202512/qa_202512_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202511:10",
|
||||
"month": "202511",
|
||||
"no": 10,
|
||||
"paper_link": "http://arxiv.org/abs/2511.06484v1",
|
||||
"source_file": "data/202511/qa_202511_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202601:2",
|
||||
"month": "202601",
|
||||
"no": 2,
|
||||
"paper_link": "http://arxiv.org/abs/2601.07068v4",
|
||||
"source_file": "data/202601/qa_202601_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:19",
|
||||
"month": "202602",
|
||||
"no": 19,
|
||||
"paper_link": "http://arxiv.org/abs/2602.18179v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202601:9",
|
||||
"month": "202601",
|
||||
"no": 9,
|
||||
"paper_link": "http://arxiv.org/abs/2601.17765v1",
|
||||
"source_file": "data/202601/qa_202601_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202512:6",
|
||||
"month": "202512",
|
||||
"no": 6,
|
||||
"paper_link": "http://arxiv.org/abs/2512.23079v1",
|
||||
"source_file": "data/202512/qa_202512_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202601:5",
|
||||
"month": "202601",
|
||||
"no": 5,
|
||||
"paper_link": "http://arxiv.org/abs/2601.20344v1",
|
||||
"source_file": "data/202601/qa_202601_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:14",
|
||||
"month": "202602",
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||||
@@ -0,0 +1,247 @@
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||||
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|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
{
|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,128 @@
|
||||
[
|
||||
{
|
||||
"id": "202602:8",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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{
|
||||
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|
||||
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|
||||
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|
||||
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||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202602:6",
|
||||
"month": "202602",
|
||||
"no": 6,
|
||||
"paper_link": "http://arxiv.org/abs/2602.01571v1",
|
||||
"source_file": "data/202602/qa_202602_final.json"
|
||||
},
|
||||
{
|
||||
"id": "202512:46",
|
||||
"month": "202512",
|
||||
"no": 46,
|
||||
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|
||||
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|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,27 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
"source_docs",
|
||||
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|
||||
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|
||||
"notes": [
|
||||
"This is a split manifest, not the full OfficeQA payload.",
|
||||
"The official OfficeQA CSV is gated on Hugging Face; materialization requires authorized access."
|
||||
]
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,402 @@
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||||
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||||
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|
||||
"source_docs": "https://fraser.stlouisfed.org/title/treasury-bulletin-407/december-1957-6731?page=26",
|
||||
"source_split": "val"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,21 @@
|
||||
{
|
||||
"benchmark": "SearchQA",
|
||||
"manifest_type": "id_split",
|
||||
"source_repo": "lucadiliello/searchqa",
|
||||
"source_repo_type": "dataset",
|
||||
"source_url": "https://huggingface.co/datasets/lucadiliello/searchqa",
|
||||
"source_id_field": "key",
|
||||
"counts": {
|
||||
"train": 400,
|
||||
"val": 200,
|
||||
"test": 1400
|
||||
},
|
||||
"item_fields": [
|
||||
"id"
|
||||
],
|
||||
"notes": [
|
||||
"This is a split manifest, not the full SearchQA payload.",
|
||||
"Materialize full split items from lucadiliello/searchqa before evaluation.",
|
||||
"The IDs in items.json exactly match the key field in lucadiliello/searchqa."
|
||||
]
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,602 @@
|
||||
[
|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
]
|
||||
@@ -0,0 +1,24 @@
|
||||
{
|
||||
"benchmark": "SpreadsheetBench",
|
||||
"manifest_type": "id_split",
|
||||
"source_repo": "KAKA22/SpreadsheetBench",
|
||||
"source_repo_type": "dataset",
|
||||
"source_url": "https://huggingface.co/datasets/KAKA22/SpreadsheetBench",
|
||||
"source_revision": "ab0b742b0fc95b946f212d80ac7771b5531272e4",
|
||||
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|
||||
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|
||||
"counts": {
|
||||
"train": 80,
|
||||
"val": 40,
|
||||
"test": 280
|
||||
},
|
||||
"item_fields": [
|
||||
"id",
|
||||
"spreadsheet_path",
|
||||
"instruction_type"
|
||||
],
|
||||
"notes": [
|
||||
"This is a split manifest, not the full SpreadsheetBench payload.",
|
||||
"Materialize full task JSON rows plus spreadsheet files from SpreadsheetBench Verified 400 before evaluation."
|
||||
]
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,402 @@
|
||||
[
|
||||
{
|
||||
"id": "32438",
|
||||
"spreadsheet_path": "spreadsheet/32438",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "398-14",
|
||||
"spreadsheet_path": "spreadsheet/398-14",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"id": "32255",
|
||||
"spreadsheet_path": "spreadsheet/32255",
|
||||
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|
||||
},
|
||||
{
|
||||
"id": "10747",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"id": "35742",
|
||||
"spreadsheet_path": "spreadsheet/35742",
|
||||
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|
||||
},
|
||||
{
|
||||
"id": "46121",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"id": "51090",
|
||||
"spreadsheet_path": "spreadsheet/51090",
|
||||
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|
||||
},
|
||||
{
|
||||
"id": "51249",
|
||||
"spreadsheet_path": "spreadsheet/51249",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "82-30",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"id": "56274",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"id": "57445",
|
||||
"spreadsheet_path": "spreadsheet/57445",
|
||||
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|
||||
},
|
||||
{
|
||||
"id": "46646",
|
||||
"spreadsheet_path": "spreadsheet/46646",
|
||||
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|
||||
},
|
||||
{
|
||||
"id": "105-24",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"id": "6239",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"id": "414-20",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"id": "165-23",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "262-17",
|
||||
"spreadsheet_path": "spreadsheet/262-17",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "141-20",
|
||||
"spreadsheet_path": "spreadsheet/141-20",
|
||||
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|
||||
},
|
||||
{
|
||||
"id": "52216",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"id": "22-47",
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"id": "55421",
|
||||
"spreadsheet_path": "spreadsheet/55421",
|
||||
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|
||||
},
|
||||
{
|
||||
"id": "56427",
|
||||
"spreadsheet_path": "spreadsheet/56427",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "36097",
|
||||
"spreadsheet_path": "spreadsheet/36097",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "32902",
|
||||
"spreadsheet_path": "spreadsheet/32902",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "32023",
|
||||
"spreadsheet_path": "spreadsheet/32023",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "1818",
|
||||
"spreadsheet_path": "spreadsheet/1818",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "170-13",
|
||||
"spreadsheet_path": "spreadsheet/170-13",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "66-24",
|
||||
"spreadsheet_path": "spreadsheet/66-24",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "58949",
|
||||
"spreadsheet_path": "spreadsheet/58949",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "42354",
|
||||
"spreadsheet_path": "spreadsheet/42354",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "194-19",
|
||||
"spreadsheet_path": "spreadsheet/194-19",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "31915",
|
||||
"spreadsheet_path": "spreadsheet/31915",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "58499",
|
||||
"spreadsheet_path": "spreadsheet/58499",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "45372",
|
||||
"spreadsheet_path": "spreadsheet/45372",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "11842",
|
||||
"spreadsheet_path": "spreadsheet/11842",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "57558",
|
||||
"spreadsheet_path": "spreadsheet/57558",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "472-15",
|
||||
"spreadsheet_path": "spreadsheet/472-15",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "55060",
|
||||
"spreadsheet_path": "spreadsheet/55060",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "31011",
|
||||
"spreadsheet_path": "spreadsheet/31011",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "408-39",
|
||||
"spreadsheet_path": "spreadsheet/408-39",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "54085",
|
||||
"spreadsheet_path": "spreadsheet/54085",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "39903",
|
||||
"spreadsheet_path": "spreadsheet/39903",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "48983",
|
||||
"spreadsheet_path": "spreadsheet/48983",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "108-24",
|
||||
"spreadsheet_path": "spreadsheet/108-24",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "58484",
|
||||
"spreadsheet_path": "spreadsheet/58484",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "118-50",
|
||||
"spreadsheet_path": "spreadsheet/118-50",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "10452",
|
||||
"spreadsheet_path": "spreadsheet/10452",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "39931",
|
||||
"spreadsheet_path": "spreadsheet/39931",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "3413",
|
||||
"spreadsheet_path": "spreadsheet/3413",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "247-24",
|
||||
"spreadsheet_path": "spreadsheet/247-24",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "56786",
|
||||
"spreadsheet_path": "spreadsheet/56786",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "55965",
|
||||
"spreadsheet_path": "spreadsheet/55965",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "379-36",
|
||||
"spreadsheet_path": "spreadsheet/379-36",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "58109",
|
||||
"spreadsheet_path": "spreadsheet/58109",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "433-47",
|
||||
"spreadsheet_path": "spreadsheet/433-47",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "192-22",
|
||||
"spreadsheet_path": "spreadsheet/192-22",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "49333",
|
||||
"spreadsheet_path": "spreadsheet/49333",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "493-18",
|
||||
"spreadsheet_path": "spreadsheet/493-18",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "54638",
|
||||
"spreadsheet_path": "spreadsheet/54638",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "34033",
|
||||
"spreadsheet_path": "spreadsheet/34033",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "30930",
|
||||
"spreadsheet_path": "spreadsheet/30930",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "585-41",
|
||||
"spreadsheet_path": "spreadsheet/585-41",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "32337",
|
||||
"spreadsheet_path": "spreadsheet/32337",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "55427",
|
||||
"spreadsheet_path": "spreadsheet/55427",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "263-1",
|
||||
"spreadsheet_path": "spreadsheet/263-1",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "254-34",
|
||||
"spreadsheet_path": "spreadsheet/254-34",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "57113",
|
||||
"spreadsheet_path": "spreadsheet/57113",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "57743",
|
||||
"spreadsheet_path": "spreadsheet/57743",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "43589",
|
||||
"spreadsheet_path": "spreadsheet/43589",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "250-20",
|
||||
"spreadsheet_path": "spreadsheet/250-20",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "48080",
|
||||
"spreadsheet_path": "spreadsheet/48080",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "370-43",
|
||||
"spreadsheet_path": "spreadsheet/370-43",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,202 @@
|
||||
[
|
||||
{
|
||||
"id": "45635",
|
||||
"spreadsheet_path": "spreadsheet/45635",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "560-12",
|
||||
"spreadsheet_path": "spreadsheet/560-12",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "55049",
|
||||
"spreadsheet_path": "spreadsheet/55049",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "9569",
|
||||
"spreadsheet_path": "spreadsheet/9569",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "7902",
|
||||
"spreadsheet_path": "spreadsheet/7902",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "227-40",
|
||||
"spreadsheet_path": "spreadsheet/227-40",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "463-17",
|
||||
"spreadsheet_path": "spreadsheet/463-17",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "54144",
|
||||
"spreadsheet_path": "spreadsheet/54144",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "80-42",
|
||||
"spreadsheet_path": "spreadsheet/80-42",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "2768",
|
||||
"spreadsheet_path": "spreadsheet/2768",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "37456",
|
||||
"spreadsheet_path": "spreadsheet/37456",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "12864",
|
||||
"spreadsheet_path": "spreadsheet/12864",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "55979",
|
||||
"spreadsheet_path": "spreadsheet/55979",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "48620",
|
||||
"spreadsheet_path": "spreadsheet/48620",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "48588",
|
||||
"spreadsheet_path": "spreadsheet/48588",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "395-36",
|
||||
"spreadsheet_path": "spreadsheet/395-36",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "382-10",
|
||||
"spreadsheet_path": "spreadsheet/382-10",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "59595",
|
||||
"spreadsheet_path": "spreadsheet/59595",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "53383",
|
||||
"spreadsheet_path": "spreadsheet/53383",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "48921",
|
||||
"spreadsheet_path": "spreadsheet/48921",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "416-15",
|
||||
"spreadsheet_path": "spreadsheet/416-15",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "47798",
|
||||
"spreadsheet_path": "spreadsheet/47798",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "56563",
|
||||
"spreadsheet_path": "spreadsheet/56563",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "46897",
|
||||
"spreadsheet_path": "spreadsheet/46897",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "9726",
|
||||
"spreadsheet_path": "spreadsheet/9726",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "50768",
|
||||
"spreadsheet_path": "spreadsheet/50768",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "51-12",
|
||||
"spreadsheet_path": "spreadsheet/51-12",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "31628",
|
||||
"spreadsheet_path": "spreadsheet/31628",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "39046",
|
||||
"spreadsheet_path": "spreadsheet/39046",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "8942",
|
||||
"spreadsheet_path": "spreadsheet/8942",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "48527",
|
||||
"spreadsheet_path": "spreadsheet/48527",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "59196",
|
||||
"spreadsheet_path": "spreadsheet/59196",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "6698",
|
||||
"spreadsheet_path": "spreadsheet/6698",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "43436",
|
||||
"spreadsheet_path": "spreadsheet/43436",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "38462",
|
||||
"spreadsheet_path": "spreadsheet/38462",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "402-43",
|
||||
"spreadsheet_path": "spreadsheet/402-43",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "267-18",
|
||||
"spreadsheet_path": "spreadsheet/267-18",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "37378",
|
||||
"spreadsheet_path": "spreadsheet/37378",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "53647",
|
||||
"spreadsheet_path": "spreadsheet/53647",
|
||||
"instruction_type": "Cell-Level Manipulation"
|
||||
},
|
||||
{
|
||||
"id": "142-12",
|
||||
"spreadsheet_path": "spreadsheet/142-12",
|
||||
"instruction_type": "Sheet-Level Manipulation"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,69 @@
|
||||
# Contributing to SkillOpt
|
||||
|
||||
Thank you for your interest in contributing to SkillOpt! This guide covers how to get started.
|
||||
|
||||
## Development Setup
|
||||
|
||||
```bash
|
||||
git clone https://github.com/microsoft/SkillOpt.git
|
||||
cd SkillOpt
|
||||
pip install -e ".[dev]"
|
||||
```
|
||||
|
||||
## Ways to Contribute
|
||||
|
||||
### 🐛 Bug Reports
|
||||
|
||||
Open an issue with:
|
||||
- Steps to reproduce
|
||||
- Expected vs actual behavior
|
||||
- Config file used (sanitize API keys)
|
||||
- Python version and OS
|
||||
|
||||
### 🔧 New Benchmark
|
||||
|
||||
See [Add a New Benchmark](guide/new-benchmark.md) for the implementation guide.
|
||||
|
||||
**Checklist:**
|
||||
- [ ] Data loader in `skillopt/envs/<benchmark>/loader.py`
|
||||
- [ ] Environment adapter in `skillopt/envs/<benchmark>/env.py`
|
||||
- [ ] Config file in `configs/<benchmark>/default.yaml`
|
||||
- [ ] Registration in `skillopt/envs/__init__.py`
|
||||
- [ ] Documentation page in `docs/`
|
||||
|
||||
### 🤖 New Model Backend
|
||||
|
||||
See [Add a New Model Backend](guide/new-backend.md) for the implementation guide.
|
||||
|
||||
**Checklist:**
|
||||
- [ ] Backend in `skillopt/model/<backend>.py`
|
||||
- [ ] Registration in `skillopt/model/__init__.py`
|
||||
- [ ] API key entry in `.env.example`
|
||||
- [ ] Documentation update
|
||||
|
||||
### 📝 Documentation
|
||||
|
||||
Documentation is built with MkDocs Material:
|
||||
|
||||
```bash
|
||||
pip install -e ".[docs]"
|
||||
mkdocs serve # Preview at http://localhost:8000
|
||||
```
|
||||
|
||||
## Code Style
|
||||
|
||||
- Follow existing patterns in the codebase
|
||||
- Use type hints for function signatures
|
||||
- Keep docstrings concise
|
||||
|
||||
## Pull Request Process
|
||||
|
||||
1. Fork the repository
|
||||
2. Create a feature branch: `git checkout -b feature/my-benchmark`
|
||||
3. Make your changes
|
||||
4. Test with an existing benchmark config
|
||||
5. Submit a PR with a clear description
|
||||
|
||||
## License
|
||||
|
||||
By contributing, you agree that your contributions will be licensed under the MIT License.
|
||||
@@ -0,0 +1,139 @@
|
||||
# Configuration Guide
|
||||
|
||||
SkillOpt uses YAML configuration files with a hierarchical override system.
|
||||
|
||||
## Config Structure
|
||||
|
||||
```
|
||||
configs/
|
||||
├── _base_/
|
||||
│ └── default.yaml # Global defaults
|
||||
├── searchqa/
|
||||
│ └── default.yaml # SearchQA overrides
|
||||
├── docvqa/
|
||||
│ └── default.yaml # DocVQA overrides
|
||||
└── alfworld/
|
||||
└── default.yaml # ALFWorld overrides
|
||||
```
|
||||
|
||||
Benchmark configs inherit from `_base_/default.yaml` and override specific values.
|
||||
|
||||
## Key Parameters
|
||||
|
||||
### Model
|
||||
|
||||
```yaml
|
||||
model:
|
||||
backend: azure_openai # azure_openai | openai_chat | claude_code_exec | qwen
|
||||
optimizer: gpt-5.5 # Optimizer model (for reflection)
|
||||
target: gpt-5.5 # Target model (for rollout)
|
||||
```
|
||||
|
||||
### Training
|
||||
|
||||
```yaml
|
||||
train:
|
||||
num_epochs: 4 # Number of training epochs
|
||||
batch_size: 40 # Tasks per step (batch size)
|
||||
accumulation: 1 # Gradient accumulation
|
||||
seed: 42
|
||||
```
|
||||
|
||||
### Gradient (Reflection)
|
||||
|
||||
```yaml
|
||||
gradient:
|
||||
minibatch_size: 8 # Reflect minibatch size
|
||||
analyst_workers: 16 # Parallel reflection workers
|
||||
max_analyst_rounds: 3 # Max rounds of analyst reflection
|
||||
failure_only: false # Only reflect on failures
|
||||
```
|
||||
|
||||
### Optimizer
|
||||
|
||||
```yaml
|
||||
optimizer:
|
||||
learning_rate: 4 # Max edits per step (edit budget)
|
||||
min_learning_rate: 2 # Min edits for decay schedulers
|
||||
lr_scheduler: cosine # constant | linear | cosine | autonomous
|
||||
use_slow_update: true # Momentum-like blending at epoch boundary
|
||||
slow_update_samples: 20 # Samples for slow update evaluation
|
||||
use_meta_skill: true # Cross-epoch strategy memory
|
||||
```
|
||||
|
||||
### Skill-Aware Reflection (optional, off by default)
|
||||
|
||||
EmbodiSkill-style failure routing: the failure analyst classifies each
|
||||
failure pattern as **SKILL_DEFECT** (the rule is wrong or missing → normal
|
||||
gated body edit) or **EXECUTION_LAPSE** (a valid rule exists but was not
|
||||
followed → a short reminder appended to a protected appendix region inside
|
||||
the skill that step-level edits can never modify).
|
||||
|
||||
```yaml
|
||||
optimizer:
|
||||
use_skill_aware_reflection: false # Master switch (default off = baseline-identical)
|
||||
skill_aware_appendix_source: both # both | failure_only (paper-faithful S_app)
|
||||
skill_aware_consolidate_threshold: 0 # >0: LLM-compact the appendix past N notes (experimental)
|
||||
```
|
||||
|
||||
Notes:
|
||||
|
||||
- The switch is resolved process-wide from the config
|
||||
(`configure_skill_aware_reflection`), so it applies to every benchmark
|
||||
with no per-adapter wiring.
|
||||
- `failure_only` restricts appendix notes to the failure analyst, matching
|
||||
the original S_app formulation; `both` additionally lets the success
|
||||
analyst re-emphasize existing rules.
|
||||
- Appendix notes bypass the validation gate by design and accumulate with
|
||||
order-preserving dedup; lapse-only steps (no body edits) still flush
|
||||
their notes.
|
||||
- Not supported together with `skill_update_mode=rewrite_from_suggestions`
|
||||
or the full-rewrite modes: whole-document rewrites can drop the appendix
|
||||
region.
|
||||
|
||||
### Evaluation
|
||||
|
||||
```yaml
|
||||
evaluation:
|
||||
use_gate: true # Validation gating (accept/reject updates)
|
||||
eval_test: true # Run test evaluation after training
|
||||
```
|
||||
|
||||
### Environment (Data)
|
||||
|
||||
```yaml
|
||||
env:
|
||||
name: searchqa # Benchmark name
|
||||
split_mode: ratio # ratio | split_dir
|
||||
split_ratio: "2:1:7" # train:val:test ratio
|
||||
data_path: "" # Path to dataset
|
||||
exec_timeout: 120 # Per-task timeout (seconds)
|
||||
```
|
||||
|
||||
## CLI Overrides
|
||||
|
||||
Override any config value from the command line:
|
||||
|
||||
```bash
|
||||
python scripts/train.py \
|
||||
--config configs/searchqa/default.yaml \
|
||||
optimizer.learning_rate=16 \
|
||||
optimizer.lr_scheduler=linear \
|
||||
gradient.analyst_workers=8
|
||||
```
|
||||
|
||||
## Environment Variables
|
||||
|
||||
Model credentials are loaded from environment variables:
|
||||
|
||||
| Variable | Backend | Description |
|
||||
|---|---|---|
|
||||
| `AZURE_OPENAI_ENDPOINT` | azure_openai | Azure resource endpoint |
|
||||
| `AZURE_OPENAI_API_KEY` | azure_openai | Azure API key |
|
||||
| `OPENAI_API_KEY` | openai | OpenAI API key |
|
||||
| `ANTHROPIC_API_KEY` | claude | Anthropic API key |
|
||||
| `QWEN_API_BASE` | qwen | Local Qwen vLLM endpoint |
|
||||
|
||||
## Full Reference
|
||||
|
||||
See [Configuration Reference](../reference/config.md) for the complete parameter list.
|
||||
@@ -0,0 +1,51 @@
|
||||
# Deep Learning ↔ SkillOpt Analogy
|
||||
|
||||
SkillOpt is designed around a core insight: **optimizing natural-language prompts follows the same structure as training neural networks**. This page maps every DL concept to its SkillOpt counterpart.
|
||||
|
||||
## Complete Mapping
|
||||
|
||||
| Deep Learning | SkillOpt | Description |
|
||||
|---|---|---|
|
||||
| **Model weights** | Skill document (Markdown) | The thing being optimized |
|
||||
| **Forward pass** | Rollout | Target executes tasks using current skill |
|
||||
| **Loss function** | Task evaluator | Scores task execution quality |
|
||||
| **Backpropagation** | Reflect | Optimizer analyzes failures → edit patches |
|
||||
| **Gradients** | Edit patches | Proposed changes to the skill |
|
||||
| **Gradient aggregation** | Patch aggregation | Merge similar edits |
|
||||
| **Gradient clipping** | Edit selection | Cap max edits per step |
|
||||
| **Learning rate** | `learning_rate` | Max number of edits applied per step |
|
||||
| **LR scheduler** | `lr_scheduler` | Decay schedule: cosine, linear, constant |
|
||||
| **SGD step** | Skill update | Apply selected patches to document |
|
||||
| **Validation set** | Selection split | Gate checks improvement before accepting |
|
||||
| **Early stopping** | Gate patience | Reject updates that don't improve |
|
||||
| **Training step** | Step | One rollout → reflect → update cycle |
|
||||
| **Epoch** | Epoch | Full pass with slow update + meta memory |
|
||||
| **Momentum** | Slow update | Longitudinal comparison at epoch boundary |
|
||||
| **Meta-learning** | Meta skill | Cross-epoch optimizer strategy memory |
|
||||
| **Batch size** | `batch_size` | Tasks sampled per rollout |
|
||||
| **Data parallelism** | `analyst_workers` | Parallel reflection workers |
|
||||
| **Training set** | Train split | Items used for rollout |
|
||||
| **Test set** | Test split | Held-out final evaluation |
|
||||
| **Warm-up** | (implicit) | High LR early steps explore broadly |
|
||||
| **Checkpointing** | Skill snapshots | Saved after each accepted step |
|
||||
| **Transfer learning** | Seed skill / cross-benchmark init | Start from pre-trained skill |
|
||||
|
||||
## Why This Analogy Matters
|
||||
|
||||
1. **Familiar mental model**: ML practitioners immediately understand how to tune SkillOpt
|
||||
2. **Principled hyperparameter search**: Grid search over `learning_rate` × `lr_scheduler` works just like in DL
|
||||
3. **Proven mechanisms**: Gating ≈ validation-based selection, patience ≈ early stopping, slow update ≈ momentum — all with strong theoretical motivation
|
||||
|
||||
## Hyperparameter Transfer Rules
|
||||
|
||||
From our experiments, these DL intuitions transfer well:
|
||||
|
||||
!!! success "What transfers"
|
||||
- **Cosine schedule > constant** — same as in DL, cosine annealing helps convergence
|
||||
- **Moderate LR (4-16) > very high/low** — too few edits = slow learning, too many = noisy
|
||||
- **Slow update helps** — longitudinal comparison prevents catastrophic forgetting across epochs
|
||||
- **Meta skill memory improves reflection** — optimizer benefits from cross-epoch strategy notes
|
||||
|
||||
!!! warning "What doesn't transfer"
|
||||
- **Batch size ≠ better** — larger rollout batches have diminishing returns due to API costs
|
||||
- **More epochs ≠ better** — skills converge faster than neural networks (2-4 epochs usually enough)
|
||||
@@ -0,0 +1,110 @@
|
||||
# Your First Experiment
|
||||
|
||||
This guide walks through running a complete SkillOpt training on SearchQA.
|
||||
|
||||
## 1. Choose a Benchmark
|
||||
|
||||
SkillOpt includes ready-to-use configs for several benchmarks:
|
||||
|
||||
| Benchmark | Difficulty | Typical Runtime |
|
||||
|---|---|---|
|
||||
| SearchQA | ⭐ Easy | ~30 min |
|
||||
| DocVQA | ⭐⭐ Medium | ~2 hours |
|
||||
| ALFWorld | ⭐⭐⭐ Hard | ~3 hours |
|
||||
|
||||
We'll use **SearchQA** as it's the fastest to complete.
|
||||
|
||||
## 2. Configure
|
||||
|
||||
Review the config file:
|
||||
|
||||
```bash
|
||||
cat configs/searchqa/default.yaml
|
||||
```
|
||||
|
||||
Key parameters (deep learning analogy in parentheses):
|
||||
|
||||
```yaml
|
||||
train:
|
||||
num_epochs: 4 # (epochs)
|
||||
batch_size: 40 # (batch size)
|
||||
|
||||
optimizer:
|
||||
learning_rate: 4 # (max edits per step)
|
||||
lr_scheduler: cosine # (learning rate schedule)
|
||||
use_slow_update: true # (momentum at epoch boundary)
|
||||
use_meta_skill: true # (cross-epoch optimizer memory)
|
||||
|
||||
gradient:
|
||||
analyst_workers: 16 # (parallel reflection workers)
|
||||
|
||||
evaluation:
|
||||
use_gate: true # (validation gating)
|
||||
```
|
||||
|
||||
## 3. Train
|
||||
|
||||
```bash
|
||||
python scripts/train.py --config configs/searchqa/default.yaml
|
||||
```
|
||||
|
||||
You'll see output like:
|
||||
|
||||
```
|
||||
[Step 1/8] Rollout: 20 items, 4 workers...
|
||||
[Step 1/8] Score: 0.65 → Reflect...
|
||||
[Step 1/8] 6 edit patches generated
|
||||
[Step 1/8] Selected 4 edits (lr=8, cosine → 7.7)
|
||||
[Step 1/8] Gate: val score 0.68 > 0.65 ✓ ACCEPT
|
||||
[Step 2/8] ...
|
||||
```
|
||||
|
||||
## 4. Monitor
|
||||
|
||||
Training outputs are saved to `outputs/<benchmark>/<run_id>/`:
|
||||
|
||||
```
|
||||
outputs/searchqa/2024-01-15_10-30-00/
|
||||
├── steps/
|
||||
│ ├── step_0001/
|
||||
│ │ ├── candidate_skill.md
|
||||
│ │ ├── step_record.json
|
||||
│ │ └── trajectory_digest.json
|
||||
│ └── step_0002/
|
||||
├── slow_update/
|
||||
│ └── epoch_02/
|
||||
├── meta_skill/
|
||||
│ └── epoch_02/
|
||||
├── skills/
|
||||
│ └── step_0001.md
|
||||
├── best_skill.md
|
||||
├── history.json
|
||||
└── config.yaml
|
||||
```
|
||||
|
||||
## 5. Evaluate
|
||||
|
||||
Evaluate the best skill on the test split:
|
||||
|
||||
```bash
|
||||
python scripts/eval_only.py \
|
||||
--config configs/searchqa/default.yaml \
|
||||
--skill outputs/searchqa/<run_id>/skills/best_skill.md
|
||||
```
|
||||
|
||||
## WebUI
|
||||
|
||||
Prefer a graphical interface? Launch the WebUI:
|
||||
|
||||
```bash
|
||||
pip install -e ".[webui]"
|
||||
python -m skillopt_webui.app
|
||||
```
|
||||
|
||||
Then open `http://localhost:7860` in your browser to configure parameters and launch training.
|
||||
|
||||
## Next Steps
|
||||
|
||||
- [Understand the training loop](training-loop.md)
|
||||
- [Configuration reference](../reference/config.md)
|
||||
- [Add a new benchmark](new-benchmark.md)
|
||||
@@ -0,0 +1,89 @@
|
||||
# Installation
|
||||
|
||||
## Requirements
|
||||
|
||||
- Python ≥ 3.10
|
||||
- At least one model API key (Azure OpenAI, OpenAI, Anthropic, or local Qwen)
|
||||
|
||||
## Quick Install
|
||||
|
||||
```bash
|
||||
git clone https://github.com/microsoft/SkillOpt.git
|
||||
cd SkillOpt
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
## Optional Dependencies
|
||||
|
||||
Install extras for specific benchmarks or backends:
|
||||
|
||||
=== "ALFWorld"
|
||||
|
||||
```bash
|
||||
pip install -e ".[alfworld]"
|
||||
```
|
||||
|
||||
=== "Claude Backend"
|
||||
|
||||
```bash
|
||||
pip install -e ".[claude]"
|
||||
```
|
||||
|
||||
=== "Qwen (Local)"
|
||||
|
||||
```bash
|
||||
pip install -e ".[qwen]"
|
||||
```
|
||||
|
||||
=== "WebUI"
|
||||
|
||||
```bash
|
||||
pip install -e ".[webui]"
|
||||
```
|
||||
|
||||
=== "Development"
|
||||
|
||||
```bash
|
||||
pip install -e ".[dev]"
|
||||
```
|
||||
|
||||
=== "All"
|
||||
|
||||
```bash
|
||||
pip install -e ".[alfworld,claude,qwen,webui,dev]"
|
||||
```
|
||||
|
||||
## Environment Variables
|
||||
|
||||
Copy the example `.env` file and fill in your credentials:
|
||||
|
||||
```bash
|
||||
cp .env.example .env
|
||||
```
|
||||
|
||||
Edit `.env` with your API keys:
|
||||
|
||||
```ini
|
||||
# Azure OpenAI (default backend)
|
||||
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
|
||||
AZURE_OPENAI_API_KEY=your-key
|
||||
|
||||
# Or use OpenAI directly
|
||||
OPENAI_API_KEY=sk-...
|
||||
|
||||
# Or Anthropic Claude
|
||||
ANTHROPIC_API_KEY=sk-ant-...
|
||||
```
|
||||
|
||||
!!! tip
|
||||
You only need credentials for the backend you plan to use. Azure OpenAI is the default.
|
||||
|
||||
## Verify Installation
|
||||
|
||||
```bash
|
||||
python -c "import skillopt; print('SkillOpt ready!')"
|
||||
```
|
||||
|
||||
## Next Steps
|
||||
|
||||
→ [Run your first experiment](first-experiment.md)
|
||||
@@ -0,0 +1,143 @@
|
||||
# Local Environment Smoke Tests
|
||||
|
||||
This guide describes a lightweight pattern for testing a custom SkillOpt environment before connecting it to expensive model calls or a full benchmark dataset.
|
||||
|
||||
The goal is to validate the training loop plumbing first:
|
||||
|
||||
- config loading
|
||||
- adapter construction
|
||||
- dataloader splits
|
||||
- rollout output shape
|
||||
- reflection patch shape
|
||||
- merge/rank/update control flow
|
||||
- artifact creation under `out_root`
|
||||
|
||||
Once those are stable, you can switch the same environment to real model calls and larger evaluation splits.
|
||||
|
||||
## 1. Add a tiny fixture split
|
||||
|
||||
Start with a handful of deterministic examples that cover the expected pass/fail cases for your environment. Keep them small enough that a single training step can run locally.
|
||||
|
||||
A minimal fixture item usually needs:
|
||||
|
||||
```json
|
||||
{
|
||||
"id": "example-1",
|
||||
"split": "train",
|
||||
"question": "...",
|
||||
"expected": "..."
|
||||
}
|
||||
```
|
||||
|
||||
Use the split names your adapter maps to SkillOpt phases:
|
||||
|
||||
- `train` for optimization rollouts
|
||||
- `val` or `valid_seen` for selection/gating
|
||||
- `test` or `valid_unseen` for final evaluation
|
||||
|
||||
## 2. Support an offline mock mode
|
||||
|
||||
Add a configuration flag such as `mock: true` to your adapter. In mock mode, `rollout()` should return deterministic responses without calling external model APIs.
|
||||
|
||||
This lets you verify the SkillOpt loop with a fast command such as:
|
||||
|
||||
```bash
|
||||
python scripts/train.py \
|
||||
--config configs/myenv/tiny_mock.yaml
|
||||
```
|
||||
|
||||
Mock mode should still write the same artifacts as a real run, for example:
|
||||
|
||||
- `responses.json`
|
||||
- `rollout_results.json`
|
||||
- `ranked_edits.json`
|
||||
- `candidate_skill.md`
|
||||
- `summary.json`
|
||||
|
||||
## 3. Keep the smoke config tiny
|
||||
|
||||
A CI-friendly smoke config should run a single small step:
|
||||
|
||||
```yaml
|
||||
train:
|
||||
num_epochs: 1
|
||||
train_size: 3
|
||||
batch_size: 3
|
||||
|
||||
gradient:
|
||||
minibatch_size: 1
|
||||
merge_batch_size: 2
|
||||
analyst_workers: 1
|
||||
max_analyst_rounds: 1
|
||||
|
||||
optimizer:
|
||||
learning_rate: 1
|
||||
min_learning_rate: 1
|
||||
lr_scheduler: constant
|
||||
skill_update_mode: patch
|
||||
use_slow_update: false
|
||||
|
||||
evaluation:
|
||||
use_gate: true
|
||||
sel_env_num: 2
|
||||
test_env_num: 2
|
||||
eval_test: false
|
||||
|
||||
env:
|
||||
name: myenv
|
||||
out_root: outputs/myenv_tiny_mock
|
||||
mock: true
|
||||
```
|
||||
|
||||
Prefer a mock config that runs without credentials. That makes it useful for contributors and CI.
|
||||
|
||||
## 4. Validate optimizer JSON before returning it
|
||||
|
||||
If your environment or extension asks an LLM to merge or rank skill edits, validate the returned JSON before passing it back into SkillOpt. This avoids silent fallbacks from empty, malformed, or out-of-range responses.
|
||||
|
||||
Useful checks for edit payloads:
|
||||
|
||||
- response is a JSON object
|
||||
- `edits` is a non-empty list
|
||||
- every edit is an object
|
||||
- every edit has an allowed operation
|
||||
- required fields such as `content` or `target` are present for that operation
|
||||
|
||||
Useful checks for ranking payloads:
|
||||
|
||||
- `selected_indices` exists
|
||||
- indices are integers
|
||||
- indices are unique
|
||||
- indices are within the candidate edit range
|
||||
- selected count does not exceed the edit budget
|
||||
|
||||
On failure, retry with a compact prompt that includes the schema error. If retries fail, raise an explicit error instead of silently accepting malformed output.
|
||||
|
||||
## 5. Run progressively stronger checks
|
||||
|
||||
A good development sequence is:
|
||||
|
||||
```bash
|
||||
python -m py_compile scripts/train.py skillopt/envs/myenv/adapter.py
|
||||
python scripts/train.py --config configs/myenv/tiny_mock.yaml
|
||||
python scripts/train.py --config configs/myenv/tiny.yaml
|
||||
```
|
||||
|
||||
For the real tiny run, verify that:
|
||||
|
||||
- the run completes
|
||||
- `summary.json` is written
|
||||
- `ranked_edits.json` contains the expected ranking metadata
|
||||
- any optimizer bridge log marks the response schema as valid
|
||||
- no generated files are written outside `out_root`
|
||||
|
||||
## 6. Keep custom environments isolated
|
||||
|
||||
When adding a custom environment to the registry, avoid side effects for existing benchmarks:
|
||||
|
||||
- lazy-import optional dependencies
|
||||
- install environment-specific hooks only when `cfg["env"]` matches your environment
|
||||
- keep mock behavior behind an explicit config flag
|
||||
- write generated artifacts only under `out_root`
|
||||
|
||||
This makes it easier to review and test a custom integration without affecting the built-in benchmarks.
|
||||
@@ -0,0 +1,130 @@
|
||||
# Add a New Model Backend
|
||||
|
||||
SkillOpt supports multiple LLM backends. This guide shows how to add your own.
|
||||
|
||||
## Backend Architecture
|
||||
|
||||
```
|
||||
skillopt/model/
|
||||
├── base.py # Abstract base class
|
||||
├── azure_openai.py # Azure OpenAI backend
|
||||
├── openai_model.py # Direct OpenAI backend
|
||||
├── claude.py # Anthropic Claude backend
|
||||
├── qwen.py # Local Qwen (vLLM) backend
|
||||
└── your_backend.py # Your new backend
|
||||
```
|
||||
|
||||
## Step 1: Create the Backend
|
||||
|
||||
Create `skillopt/model/your_backend.py`:
|
||||
|
||||
```python
|
||||
from skillopt.model.base import ModelBackend, ModelResponse
|
||||
|
||||
class YourBackend(ModelBackend):
|
||||
"""Your custom model backend."""
|
||||
|
||||
def __init__(self, cfg: dict):
|
||||
super().__init__(cfg)
|
||||
self.model_name = cfg.get('model_name', 'your-default-model')
|
||||
self.api_key = os.environ.get('YOUR_API_KEY', '')
|
||||
self.client = self._init_client()
|
||||
|
||||
def _init_client(self):
|
||||
"""Initialize API client."""
|
||||
# TODO: Set up your API client
|
||||
pass
|
||||
|
||||
async def generate(
|
||||
self,
|
||||
messages: list[dict],
|
||||
temperature: float = 0.7,
|
||||
max_tokens: int = 4096,
|
||||
**kwargs
|
||||
) -> ModelResponse:
|
||||
"""
|
||||
Generate a completion.
|
||||
|
||||
Args:
|
||||
messages: Chat messages [{"role": "...", "content": "..."}]
|
||||
temperature: Sampling temperature
|
||||
max_tokens: Maximum tokens in response
|
||||
|
||||
Returns:
|
||||
ModelResponse with content, usage, and metadata
|
||||
"""
|
||||
response = await self.client.chat(
|
||||
model=self.model_name,
|
||||
messages=messages,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
)
|
||||
|
||||
return ModelResponse(
|
||||
content=response.text,
|
||||
usage={
|
||||
'prompt_tokens': response.usage.input,
|
||||
'completion_tokens': response.usage.output,
|
||||
},
|
||||
model=self.model_name,
|
||||
)
|
||||
|
||||
async def generate_with_tools(
|
||||
self,
|
||||
messages: list[dict],
|
||||
tools: list[dict],
|
||||
**kwargs
|
||||
) -> ModelResponse:
|
||||
"""Generate with tool/function calling support."""
|
||||
# Optional: implement if your model supports tool use
|
||||
raise NotImplementedError("Tool use not supported")
|
||||
```
|
||||
|
||||
## Step 2: Register the Backend
|
||||
|
||||
Add to `skillopt/model/__init__.py`:
|
||||
|
||||
```python
|
||||
from .your_backend import YourBackend
|
||||
|
||||
BACKEND_REGISTRY = {
|
||||
# ... existing backends ...
|
||||
'your_backend': YourBackend,
|
||||
}
|
||||
```
|
||||
|
||||
## Step 3: Configure
|
||||
|
||||
Use your backend in any config:
|
||||
|
||||
```yaml
|
||||
model:
|
||||
backend: your_backend
|
||||
model_name: your-model-id
|
||||
temperature: 0.7
|
||||
max_tokens: 4096
|
||||
```
|
||||
|
||||
Set credentials via environment variable:
|
||||
|
||||
```bash
|
||||
export YOUR_API_KEY="your-key"
|
||||
```
|
||||
|
||||
## Required Interface
|
||||
|
||||
Your backend must implement these methods:
|
||||
|
||||
| Method | Required | Description |
|
||||
|---|---|---|
|
||||
| `generate()` | ✅ | Basic text generation |
|
||||
| `generate_with_tools()` | Optional | Tool/function calling |
|
||||
| `count_tokens()` | Optional | Token counting for context management |
|
||||
|
||||
## Tips
|
||||
|
||||
!!! tip
|
||||
- Test your backend with `python -c "from skillopt.model.your_backend import YourBackend"` first
|
||||
- Use `async` methods for all API calls — SkillOpt uses asyncio throughout
|
||||
- Implement retry logic with exponential backoff for production use
|
||||
- Add your API key to `.env.example` when submitting a PR
|
||||
@@ -0,0 +1,371 @@
|
||||
# Add a New Benchmark
|
||||
|
||||
Extend SkillOpt with your own benchmark in ~200 lines of code. We will use
|
||||
a tiny worked example, `docfaithful`, that scores a target model on
|
||||
how faithfully it answers questions grounded in a small reference doc.
|
||||
|
||||
> **Working reference.** The easiest way to copy-cargo-cult a new env is
|
||||
> to read [`skillopt/envs/officeqa/`](https://github.com/microsoft/SkillOpt/tree/main/skillopt/envs/officeqa).
|
||||
> Everything below is the same shape, simplified.
|
||||
|
||||
## What you need to build
|
||||
|
||||
To add a benchmark you implement four things:
|
||||
|
||||
1. **A `SplitDataLoader` subclass** — knows how to load train / val / test
|
||||
item dicts from disk.
|
||||
2. **A rollout helper** — runs the target model on a batch of items
|
||||
under the current skill and scores each prediction.
|
||||
3. **An `EnvAdapter` subclass** — wires the loader + rollout helper into
|
||||
SkillOpt's lifecycle (`build_*_env`, `rollout`, `reflect`,
|
||||
`get_task_types`).
|
||||
4. **A YAML config** — references your env name plus the standard
|
||||
train / optimizer / gradient knobs.
|
||||
|
||||
Then one line in `scripts/train.py`'s `_register_builtins()` makes it
|
||||
discoverable.
|
||||
|
||||
---
|
||||
|
||||
## Step 1 — Create the package
|
||||
|
||||
```bash
|
||||
mkdir -p skillopt/envs/docfaithful
|
||||
touch skillopt/envs/docfaithful/__init__.py
|
||||
```
|
||||
|
||||
## Step 2 — Implement the data loader
|
||||
|
||||
`skillopt/envs/docfaithful/dataloader.py`:
|
||||
|
||||
```python
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
from skillopt.datasets.base import SplitDataLoader
|
||||
|
||||
|
||||
def _normalize(raw: dict) -> dict:
|
||||
"""Make sure every item has an ``id``. Other keys are env-specific."""
|
||||
return {
|
||||
"id": str(raw["uid"]),
|
||||
"question": raw["question"],
|
||||
"ground_truth": raw["answer"],
|
||||
"reference_text": raw.get("reference", ""),
|
||||
"task_type": raw.get("category", "docfaithful"),
|
||||
}
|
||||
|
||||
|
||||
class DocFaithfulDataLoader(SplitDataLoader):
|
||||
"""Load DocFaithful items from JSON files inside each split dir."""
|
||||
|
||||
def load_split_items(self, split_path: str) -> list[dict]:
|
||||
# split_path is e.g. data/docfaithful_split/train/
|
||||
json_files = sorted(Path(split_path).glob("*.json"))
|
||||
if not json_files:
|
||||
raise FileNotFoundError(f"No .json file found in {split_path}")
|
||||
with json_files[0].open(encoding="utf-8") as f:
|
||||
raw = json.load(f)
|
||||
return [_normalize(item) for item in raw]
|
||||
```
|
||||
|
||||
Only `load_split_items()` is mandatory. If you also want to support
|
||||
`split_mode="ratio"` (auto-split a single raw file into train/val/test),
|
||||
override `load_raw_items(data_path)` as well — see
|
||||
`skillopt/datasets/base.py` docstrings.
|
||||
|
||||
## Step 3 — Write the rollout helper
|
||||
|
||||
`skillopt/envs/docfaithful/rollout.py`:
|
||||
|
||||
```python
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
from skillopt.model import chat_target
|
||||
|
||||
|
||||
def _score(prediction: str, ground_truth: str) -> tuple[int, float]:
|
||||
"""Trivial exact-match scorer. Replace with F1 / ROUGE / LLM-judge."""
|
||||
p = (prediction or "").strip().lower()
|
||||
g = (ground_truth or "").strip().lower()
|
||||
hard = int(p == g and bool(g))
|
||||
soft = 1.0 if hard else 0.0
|
||||
return hard, soft
|
||||
|
||||
|
||||
def _rollout_one(item: dict, skill_content: str,
|
||||
*, max_completion_tokens: int) -> dict:
|
||||
system = skill_content
|
||||
user = (
|
||||
f"Question: {item['question']}\n\n"
|
||||
f"Reference:\n{item.get('reference_text', '')}\n\n"
|
||||
"Answer:"
|
||||
)
|
||||
prediction, _usage = chat_target(
|
||||
system=system,
|
||||
user=user,
|
||||
max_completion_tokens=max_completion_tokens,
|
||||
)
|
||||
hard, soft = _score(prediction, item.get("ground_truth", ""))
|
||||
return {
|
||||
"id": str(item["id"]),
|
||||
"hard": hard,
|
||||
"soft": soft,
|
||||
"predicted_answer": prediction,
|
||||
"question": item.get("question", ""),
|
||||
"reference_text": item.get("reference_text", ""),
|
||||
"task_type": item.get("task_type", "docfaithful"),
|
||||
}
|
||||
|
||||
|
||||
def run_batch(*, items: list[dict], skill_content: str, out_root: str,
|
||||
workers: int = 4, max_completion_tokens: int = 4096) -> list[dict]:
|
||||
"""Run a batch of episodes sequentially or with a thread pool."""
|
||||
os.makedirs(out_root, exist_ok=True)
|
||||
# For brevity we go sequentially — swap in concurrent.futures.ThreadPoolExecutor
|
||||
# when network / model latency dominates.
|
||||
results = [
|
||||
_rollout_one(item, skill_content,
|
||||
max_completion_tokens=max_completion_tokens)
|
||||
for item in items
|
||||
]
|
||||
Path(out_root, "rollouts.json").write_text(
|
||||
json.dumps(results, ensure_ascii=False, indent=2)
|
||||
)
|
||||
return results
|
||||
```
|
||||
|
||||
Two design points worth flagging:
|
||||
|
||||
- **Scoring lives here, not in `EnvAdapter`.** There is no `evaluate()`
|
||||
method on the ABC. Whatever signal you put in `hard` (0/1, or a float
|
||||
in [0, 1] for smoothed reward) and `soft` (float in [0, 1]) is what
|
||||
the optimizer reads.
|
||||
- **Use `skillopt.model.chat_target`**, not raw OpenAI/Claude calls.
|
||||
That routes through whichever **chat** target backend the user
|
||||
configured (`openai_chat` / `claude_chat` / `qwen_chat` /
|
||||
`minimax_chat`) without your adapter caring. Exec-style backends
|
||||
(`codex_exec`, `claude_code_exec`) need env-specific rollout code —
|
||||
see `skillopt/envs/swebench/` for an example.
|
||||
|
||||
## Step 4 — Implement the environment adapter
|
||||
|
||||
`skillopt/envs/docfaithful/adapter.py`:
|
||||
|
||||
```python
|
||||
from __future__ import annotations
|
||||
|
||||
from skillopt.datasets.base import BatchSpec
|
||||
from skillopt.envs.base import EnvAdapter
|
||||
from skillopt.envs.docfaithful.dataloader import DocFaithfulDataLoader
|
||||
from skillopt.envs.docfaithful.rollout import run_batch
|
||||
|
||||
|
||||
class DocFaithfulAdapter(EnvAdapter):
|
||||
"""SkillOpt adapter for the DocFaithful benchmark."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
split_dir: str = "",
|
||||
data_path: str = "",
|
||||
split_mode: str = "split_dir",
|
||||
split_ratio: str = "2:1:7",
|
||||
split_seed: int = 42,
|
||||
split_output_dir: str = "",
|
||||
workers: int = 4,
|
||||
analyst_workers: int = 4,
|
||||
failure_only: bool = False,
|
||||
minibatch_size: int = 8,
|
||||
edit_budget: int = 4,
|
||||
seed: int = 42,
|
||||
limit: int = 0,
|
||||
max_completion_tokens: int = 4096,
|
||||
) -> None:
|
||||
self.workers = workers
|
||||
self.analyst_workers = analyst_workers
|
||||
self.failure_only = failure_only
|
||||
self.minibatch_size = minibatch_size
|
||||
self.edit_budget = edit_budget
|
||||
self.max_completion_tokens = int(max_completion_tokens)
|
||||
self.dataloader = DocFaithfulDataLoader(
|
||||
split_dir=split_dir,
|
||||
data_path=data_path,
|
||||
split_mode=split_mode,
|
||||
split_ratio=split_ratio,
|
||||
split_seed=split_seed,
|
||||
split_output_dir=split_output_dir,
|
||||
seed=seed,
|
||||
limit=limit,
|
||||
)
|
||||
|
||||
# ── Lifecycle ───────────────────────────────────────────────────────
|
||||
|
||||
def setup(self, cfg: dict) -> None:
|
||||
super().setup(cfg)
|
||||
self.dataloader.setup(cfg)
|
||||
|
||||
def get_dataloader(self):
|
||||
return self.dataloader
|
||||
|
||||
# ── Env construction ────────────────────────────────────────────────
|
||||
|
||||
def build_env_from_batch(self, batch: BatchSpec, **kwargs):
|
||||
# For dataset-backed envs the "manager" is just the items list.
|
||||
return list(batch.payload or [])
|
||||
|
||||
def build_train_env(self, batch_size: int, seed: int, **kwargs):
|
||||
batch = self.dataloader.build_train_batch(
|
||||
batch_size=batch_size, seed=seed, **kwargs
|
||||
)
|
||||
return self.build_env_from_batch(batch, **kwargs)
|
||||
|
||||
def build_eval_env(self, env_num: int, split: str, seed: int, **kwargs):
|
||||
batch = self.dataloader.build_eval_batch(
|
||||
env_num=env_num, split=split, seed=seed, **kwargs
|
||||
)
|
||||
return self.build_env_from_batch(batch, **kwargs)
|
||||
|
||||
# ── The rollout method (reflect is inherited) ───────────────────────
|
||||
|
||||
def rollout(self, env_manager, skill_content: str,
|
||||
out_dir: str, **kwargs) -> list[dict]:
|
||||
items: list[dict] = env_manager
|
||||
return run_batch(
|
||||
items=items,
|
||||
skill_content=skill_content,
|
||||
out_root=out_dir,
|
||||
workers=self.workers,
|
||||
max_completion_tokens=self.max_completion_tokens,
|
||||
)
|
||||
|
||||
# reflect() is inherited from EnvAdapter — it delegates to
|
||||
# run_minibatch_reflect with your analyst_error_* / analyst_success_*
|
||||
# prompts. Override it only if you need custom reflection logic.
|
||||
|
||||
def get_task_types(self) -> list[str]:
|
||||
seen: list[str] = []
|
||||
for item in (
|
||||
self.dataloader.train_items
|
||||
+ self.dataloader.val_items
|
||||
+ self.dataloader.test_items
|
||||
):
|
||||
tt = str(item.get("task_type") or "docfaithful")
|
||||
if tt not in seen:
|
||||
seen.append(tt)
|
||||
return seen or ["docfaithful"]
|
||||
```
|
||||
|
||||
### What the rollout actually does
|
||||
|
||||
Look back at `run_batch` from Step 3 — it sends each `item["question"]`
|
||||
to the target model with `skill_content` as the system prompt, scores
|
||||
the answer against `item["ground_truth"]`, and returns a list of dicts:
|
||||
|
||||
```python
|
||||
[
|
||||
{"id": "ex_001", "hard": 1, "soft": 0.92,
|
||||
"predicted_answer": "...", "question": "...",
|
||||
"reference_text": item["reference_text"]},
|
||||
{"id": "ex_002", "hard": 0, "soft": 0.13, "fail_reason": "...", ...},
|
||||
...
|
||||
]
|
||||
```
|
||||
|
||||
The trainer only requires `id`, `hard`, `soft`. The rest is preserved on
|
||||
`RolloutResult.extras` (see `skillopt/types.py`) and is what your
|
||||
`reflect()` consumes via `run_minibatch_reflect`.
|
||||
|
||||
## Step 5 — Register the adapter
|
||||
|
||||
Edit [`scripts/train.py`](https://github.com/microsoft/SkillOpt/blob/main/scripts/train.py)
|
||||
and add to `_register_builtins()`:
|
||||
|
||||
```python
|
||||
try:
|
||||
from skillopt.envs.docfaithful.adapter import DocFaithfulAdapter
|
||||
_ENV_REGISTRY["docfaithful"] = DocFaithfulAdapter
|
||||
except ImportError:
|
||||
pass # docfaithful deps not installed — skip
|
||||
```
|
||||
|
||||
There is **no `BENCHMARK_REGISTRY` dict in `skillopt/envs/__init__.py`** —
|
||||
the registry lives in `scripts/train.py` and is populated lazily so that
|
||||
optional deps don't break `--help`.
|
||||
|
||||
## Step 6 — Create the YAML config
|
||||
|
||||
`configs/docfaithful/default.yaml`:
|
||||
|
||||
```yaml
|
||||
_base_: ../_base_/default.yaml # NOTE: string, not list
|
||||
|
||||
model:
|
||||
reasoning_effort: medium
|
||||
|
||||
train:
|
||||
batch_size: 16
|
||||
accumulation: 1
|
||||
num_epochs: 4
|
||||
|
||||
gradient:
|
||||
minibatch_size: 8
|
||||
merge_batch_size: 8
|
||||
|
||||
optimizer:
|
||||
learning_rate: 4
|
||||
|
||||
env:
|
||||
name: docfaithful
|
||||
# Optional: a seed skill document. Create this file (or any markdown
|
||||
# file) yourself before the first run, or omit the key to let SkillOpt
|
||||
# start from an empty skill.
|
||||
skill_init: skillopt/envs/docfaithful/skills/initial.md
|
||||
split_mode: split_dir
|
||||
split_dir: data/docfaithful_split
|
||||
workers: 4
|
||||
max_completion_tokens: 4096
|
||||
limit: 0
|
||||
```
|
||||
|
||||
> ⚠️ `_base_` is currently parsed as a **string path**, not a list. Write
|
||||
> `_base_: ../_base_/default.yaml`, not `_base_: ['../_base_/default.yaml']`.
|
||||
> See [`skillopt/config.py`](https://github.com/microsoft/SkillOpt/blob/main/skillopt/config.py)
|
||||
> if you want to add list-form inheritance.
|
||||
|
||||
## Step 7 — Run
|
||||
|
||||
```bash
|
||||
# If you set skill_init above, create the seed skill first:
|
||||
# mkdir -p skillopt/envs/docfaithful/skills
|
||||
# echo "# DocFaithful initial skill" > skillopt/envs/docfaithful/skills/initial.md
|
||||
|
||||
python scripts/train.py --config configs/docfaithful/default.yaml
|
||||
```
|
||||
|
||||
If you get `ValueError: Unknown environment 'docfaithful'. Available: [...]`,
|
||||
you forgot Step 5.
|
||||
|
||||
If you get `TypeError: Can't instantiate abstract class DocFaithfulAdapter`,
|
||||
you forgot to implement one of the four abstract methods on `EnvAdapter`:
|
||||
`build_train_env`, `build_eval_env`, `rollout`, `get_task_types`.
|
||||
|
||||
## Tips
|
||||
|
||||
- Start with `train.batch_size: 4` and `limit: 10` while debugging.
|
||||
- The `evaluate` half lives **inside your `rollout`**, not as a separate
|
||||
method — there is no `evaluate()` in the `EnvAdapter` ABC. Score the
|
||||
prediction in `run_batch` and put the score on each result dict's
|
||||
`hard` / `soft`.
|
||||
- Noisy scoring kills the optimizer. Spend time on `run_batch`'s scoring
|
||||
before you spend time on prompts.
|
||||
- If your benchmark needs heavy optional deps (selenium, vllm, ...),
|
||||
wrap the registration block with `try / except ImportError` (Step 5)
|
||||
so people without those deps can still `--help`.
|
||||
- Copy `skillopt/envs/_template/` as a starting skeleton — it now
|
||||
implements the real abstract methods.
|
||||
@@ -0,0 +1,78 @@
|
||||
# Skill Document
|
||||
|
||||
A **skill document** is a Markdown file that serves as the "prompt weights" of your agent. SkillOpt trains this document through iterative optimization.
|
||||
|
||||
## What is a Skill Document?
|
||||
|
||||
A skill document is a structured set of instructions that tells a language model **how** to approach a specific type of task. It's analogous to learned weights in a neural network — encoding task-specific knowledge in natural language rather than floating-point parameters.
|
||||
|
||||
## Structure
|
||||
|
||||
A typical skill document contains:
|
||||
|
||||
```markdown
|
||||
# Task Strategy
|
||||
|
||||
## General Approach
|
||||
- Break complex problems into sub-steps
|
||||
- Always verify intermediate results
|
||||
|
||||
## Common Patterns
|
||||
- When you see X, try approach Y
|
||||
- Avoid Z because it leads to errors
|
||||
|
||||
## Edge Cases
|
||||
- If the input contains A, handle it specially by...
|
||||
- Watch out for B — it requires C
|
||||
|
||||
## Output Format
|
||||
- Always include reasoning before the answer
|
||||
- Format numbers with proper units
|
||||
```
|
||||
|
||||
## How It Evolves
|
||||
|
||||
During training, the skill document is modified by **edit patches**:
|
||||
|
||||
1. **Additions**: New rules or strategies discovered from failed trajectories
|
||||
2. **Modifications**: Refining existing rules that are partially correct
|
||||
3. **Deletions**: Removing rules that consistently lead to errors
|
||||
|
||||
Each edit is validated through the **gate** mechanism before being permanently accepted.
|
||||
|
||||
## Initial Skill
|
||||
|
||||
You can start training with:
|
||||
|
||||
- **Empty skill**: The system learns everything from scratch
|
||||
- **Seed skill**: Provide initial instructions to bootstrap training
|
||||
- **Pre-trained skill**: Transfer a skill from a related benchmark
|
||||
|
||||
Configure the initial skill in your YAML:
|
||||
|
||||
```yaml
|
||||
train:
|
||||
init_skill: "path/to/initial_skill.md" # or omit for empty
|
||||
```
|
||||
|
||||
## Skill Quality Metrics
|
||||
|
||||
Track your skill's evolution through:
|
||||
|
||||
- **Validation score**: Primary metric on the selection split
|
||||
- **Test score**: Final metric on held-out test data
|
||||
- **Skill length**: Total tokens in the document
|
||||
- **Edit acceptance rate**: Fraction of proposed edits that pass gating
|
||||
|
||||
## Best Practices
|
||||
|
||||
!!! tip "Tips for better skills"
|
||||
1. **Start with a seed skill** (`env.skill_init`) if you have domain knowledge — it converges faster
|
||||
2. **Use cosine LR schedule** — aggressive early exploration + careful late refinement
|
||||
3. **Enable slow update** (`use_slow_update: true`) to prevent forgetting across epochs
|
||||
4. **Enable meta skill** (`use_meta_skill: true`) so the optimizer accumulates strategy memory
|
||||
|
||||
## Next Steps
|
||||
|
||||
- [Deep Learning Analogy](dl-analogy.md)
|
||||
- [Configuration Reference](../reference/config.md)
|
||||
@@ -0,0 +1,92 @@
|
||||
# The Training Loop
|
||||
|
||||
SkillOpt's core insight: **optimizing natural-language skill documents follows the same structure as training neural networks**.
|
||||
|
||||
## Overview
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────┐
|
||||
│ Training Loop │
|
||||
│ │
|
||||
│ for epoch in epochs: │
|
||||
│ for step in steps: │
|
||||
│ 1. Rollout — Target executes tasks │
|
||||
│ 2. Reflect — Optimizer analyzes trajectories │
|
||||
│ 3. Aggregate — Hierarchical merge of patches │
|
||||
│ 4. Select — Rank & clip edits (learning rate) │
|
||||
│ 5. Update — Apply patches to skill doc │
|
||||
│ 6. Gate — Validate & accept/reject │
|
||||
│ │
|
||||
│ Epoch Boundary: │
|
||||
│ • Slow Update (longitudinal comparison & guidance) │
|
||||
│ • Meta Skill (cross-epoch strategy memory) │
|
||||
└─────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## Stage Details
|
||||
|
||||
### 1. Rollout (Forward Pass)
|
||||
|
||||
The **target** model executes tasks using the current skill document as its prompt. Each task produces a trajectory and a score.
|
||||
|
||||
```python
|
||||
# Analogy: forward pass through the network
|
||||
predictions = model(input, skill_document)
|
||||
scores = evaluate(predictions, ground_truth)
|
||||
```
|
||||
|
||||
### 2. Reflect (Backward Pass)
|
||||
|
||||
The **optimizer** model analyzes failed trajectories and produces **edit patches** — structured suggestions for improving the skill document.
|
||||
|
||||
Two modes:
|
||||
|
||||
- **Shallow**: Analyze each trajectory independently
|
||||
- **Deep**: Cross-reference multiple failures to find systemic issues
|
||||
|
||||
```python
|
||||
# Analogy: computing gradients
|
||||
gradients = loss.backward() # → edit patches
|
||||
```
|
||||
|
||||
### 3. Aggregate
|
||||
|
||||
Semantically similar edit patches are merged to avoid redundant edits.
|
||||
|
||||
### 4. Select (Gradient Clipping)
|
||||
|
||||
Edits are ranked by relevance score. The `learning_rate` parameter caps how many edits are applied per step — just like gradient clipping prevents overshooting.
|
||||
|
||||
```python
|
||||
# Analogy: gradient clipping + optimizer step size
|
||||
selected = top_k(edits, k=learning_rate)
|
||||
```
|
||||
|
||||
The `lr_scheduler` adjusts this over training:
|
||||
|
||||
- **cosine**: Start aggressive, taper smoothly
|
||||
- **linear**: Linear decay
|
||||
- **constant**: Fixed rate
|
||||
|
||||
### 5. Update (Parameter Update)
|
||||
|
||||
Selected edits are applied to the skill document, producing a new version.
|
||||
|
||||
### 6. Gate (Validation)
|
||||
|
||||
The updated skill is evaluated on a **selection split** (analogous to a validation set). The update is only accepted if performance improves.
|
||||
|
||||
## Epoch Boundary Mechanisms
|
||||
|
||||
### Slow Update
|
||||
|
||||
At the end of each epoch (starting from epoch 2), the system performs a **longitudinal comparison**: it rolls out both the previous epoch's skill and the current skill on the same samples, categorizes items as improved/regressed/persistent_fail/stable_success, then generates high-level **guidance** that is injected into the skill document. This prevents catastrophic forgetting of earlier improvements.
|
||||
|
||||
### Meta Skill
|
||||
|
||||
A **meta-skill memory** accumulates high-level strategy notes across the entire training run. At the end of each epoch, the optimizer reflects on what changed between epochs and produces a compact memory that is provided as additional context during future reflection steps.
|
||||
|
||||
## Next Steps
|
||||
|
||||
- [Understand Skill Documents](skill-document.md)
|
||||
- [DL ↔ SkillOpt analogy table](dl-analogy.md)
|
||||
+1042
File diff suppressed because it is too large
Load Diff
+170
@@ -0,0 +1,170 @@
|
||||
---
|
||||
hide:
|
||||
- navigation
|
||||
---
|
||||
|
||||
<div class="hero" markdown>
|
||||
|
||||
# SkillOpt
|
||||
|
||||
### Train Agent Skills Like Neural Networks
|
||||
|
||||
*Optimize natural-language skill documents through iterative rollout, reflection, and gated validation — with epochs, learning rates, and validation gates — without touching model weights.*
|
||||
|
||||
[Get Started :material-rocket-launch:](guide/installation.md){ .md-button .md-button--primary }
|
||||
[View on GitHub :material-github:](https://github.com/microsoft/SkillOpt){ .md-button }
|
||||
|
||||
</div>
|
||||
|
||||
---
|
||||
|
||||
## How It Works
|
||||
|
||||
<div class="pipeline-container" markdown>
|
||||
<div class="pipeline-wrapper">
|
||||
|
||||
<div class="pipeline-stage" id="stage-rollout">
|
||||
<div class="stage-icon">🎯</div>
|
||||
<div class="stage-label">Rollout</div>
|
||||
<div class="stage-desc">Target executes tasks</div>
|
||||
</div>
|
||||
|
||||
<div class="pipeline-arrow"><div class="flow-line"></div></div>
|
||||
|
||||
<div class="pipeline-stage" id="stage-reflect">
|
||||
<div class="stage-icon">🔍</div>
|
||||
<div class="stage-label">Reflect</div>
|
||||
<div class="stage-desc">Optimizer analyzes trajectories</div>
|
||||
</div>
|
||||
|
||||
<div class="pipeline-arrow"><div class="flow-line"></div></div>
|
||||
|
||||
<div class="pipeline-stage" id="stage-aggregate">
|
||||
<div class="stage-icon">🔗</div>
|
||||
<div class="stage-label">Aggregate</div>
|
||||
<div class="stage-desc">Merge edit patches</div>
|
||||
</div>
|
||||
|
||||
<div class="pipeline-arrow"><div class="flow-line"></div></div>
|
||||
|
||||
<div class="pipeline-stage" id="stage-select">
|
||||
<div class="stage-icon">✂️</div>
|
||||
<div class="stage-label">Select</div>
|
||||
<div class="stage-desc">Rank & clip edits</div>
|
||||
</div>
|
||||
|
||||
<div class="pipeline-arrow"><div class="flow-line"></div></div>
|
||||
|
||||
<div class="pipeline-stage" id="stage-update">
|
||||
<div class="stage-icon">📝</div>
|
||||
<div class="stage-label">Update</div>
|
||||
<div class="stage-desc">Apply to skill doc</div>
|
||||
</div>
|
||||
|
||||
<div class="pipeline-arrow"><div class="flow-line"></div></div>
|
||||
|
||||
<div class="pipeline-stage" id="stage-gate">
|
||||
<div class="stage-icon">🚦</div>
|
||||
<div class="stage-label">Gate</div>
|
||||
<div class="stage-desc">Validate & accept</div>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
|
||||
<div class="pipeline-epoch-bar">
|
||||
<div class="epoch-mechanism">🔄 Slow Update</div>
|
||||
<div class="epoch-mechanism">🧠 Meta Skill</div>
|
||||
<div class="epoch-label">Epoch Boundary</div>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
|
||||
---
|
||||
|
||||
## Deep Learning Analogy
|
||||
|
||||
SkillOpt brings the familiar deep-learning training paradigm to agentic prompt optimization:
|
||||
|
||||
| Deep Learning | SkillOpt |
|
||||
|---|---|
|
||||
| Model weights | Skill document (Markdown) |
|
||||
| Forward pass | Rollout (target executes tasks) |
|
||||
| Loss / gradient | Reflect (optimizer produces edit patches) |
|
||||
| Gradient clipping | Edit selection (`learning_rate` = max edits) |
|
||||
| SGD step | Patch application to skill |
|
||||
| Validation set | Gated evaluation on selection split |
|
||||
| LR schedule | `lr_scheduler`: cosine, linear, constant |
|
||||
| Epochs | Multi-epoch with slow update & meta skill memory |
|
||||
|
||||
---
|
||||
|
||||
## Supported Benchmarks
|
||||
|
||||
| Benchmark | Type | Config |
|
||||
|---|---|---|
|
||||
| **DocVQA** | Document QA | `configs/docvqa/` |
|
||||
| **ALFWorld** | Embodied AI | `configs/alfworld/` |
|
||||
| **OfficeQA** | Enterprise QA | `configs/officeqa/` |
|
||||
| **SearchQA** | Open-domain QA | `configs/searchqa/` |
|
||||
| **LiveMathBench** | Math reasoning | `configs/livemathematicianbench/` |
|
||||
| **SWEBench** | Software Engineering | `configs/swebench/` |
|
||||
| + 5 more | Various | See [docs](guide/first-experiment.md) |
|
||||
|
||||
---
|
||||
|
||||
## Quick Example
|
||||
|
||||
```bash
|
||||
# Install
|
||||
pip install -e .
|
||||
|
||||
# Configure credentials
|
||||
export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
|
||||
export AZURE_OPENAI_API_KEY="your-key"
|
||||
|
||||
# Train on SearchQA
|
||||
python scripts/train.py --config configs/searchqa/default.yaml
|
||||
|
||||
# Evaluate best skill
|
||||
python scripts/eval_only.py \
|
||||
--config configs/searchqa/default.yaml \
|
||||
--skill outputs/best_skill.md
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
<div class="grid cards" markdown>
|
||||
|
||||
- :material-book-open-variant:{ .lg .middle } **Getting Started**
|
||||
|
||||
---
|
||||
|
||||
Install SkillOpt, configure your API keys, and run your first experiment in 5 minutes.
|
||||
|
||||
[:octicons-arrow-right-24: Installation](guide/installation.md)
|
||||
|
||||
- :material-puzzle:{ .lg .middle } **Add a Benchmark**
|
||||
|
||||
---
|
||||
|
||||
Extend SkillOpt with your own benchmark in ~100 lines of code.
|
||||
|
||||
[:octicons-arrow-right-24: Extension Guide](guide/new-benchmark.md)
|
||||
|
||||
- :material-cog:{ .lg .middle } **Configuration**
|
||||
|
||||
---
|
||||
|
||||
Full reference for all hyperparameters with deep learning analogies.
|
||||
|
||||
[:octicons-arrow-right-24: Config Reference](reference/config.md)
|
||||
|
||||
- :material-monitor-dashboard:{ .lg .middle } **WebUI**
|
||||
|
||||
---
|
||||
|
||||
Configure, launch, and monitor training from your browser.
|
||||
|
||||
[:octicons-arrow-right-24: WebUI Guide](guide/first-experiment.md#webui)
|
||||
|
||||
</div>
|
||||
@@ -0,0 +1,195 @@
|
||||
# API Reference
|
||||
|
||||
This page documents the public Python API SkillOpt exposes for **extending the
|
||||
framework** with new environments / benchmarks. For ready-made adapters,
|
||||
browse [`skillopt/envs/`](https://github.com/microsoft/SkillOpt/tree/main/skillopt/envs).
|
||||
|
||||
> **Source of truth.** The classes below are real Python ABCs defined in
|
||||
> `skillopt/envs/base.py`, `skillopt/datasets/base.py`, `skillopt/types.py`,
|
||||
> and `skillopt/evaluation/gate.py`. If this page ever drifts, the code
|
||||
> wins — please open an issue.
|
||||
|
||||
---
|
||||
|
||||
## Core Classes
|
||||
|
||||
### `EnvAdapter`
|
||||
|
||||
`skillopt/envs/base.py` — abstract adapter that connects the SkillOpt
|
||||
trainer to an environment (benchmark, simulator, REST API, ...).
|
||||
Subclasses **must** implement the five abstract methods below.
|
||||
|
||||
```python
|
||||
from abc import ABC, abstractmethod
|
||||
from skillopt.datasets.base import BaseDataLoader, BatchSpec
|
||||
|
||||
class EnvAdapter(ABC):
|
||||
|
||||
# ── Lifecycle hooks (have defaults; override only if needed) ────────
|
||||
|
||||
def setup(self, cfg: dict) -> None: ...
|
||||
def get_dataloader(self) -> BaseDataLoader | None: ...
|
||||
def requires_ray(self) -> bool: ... # default False
|
||||
|
||||
# ── Abstract methods (subclasses MUST implement) ────────────────────
|
||||
|
||||
@abstractmethod
|
||||
def build_train_env(self, batch_size: int, seed: int, **kwargs):
|
||||
"""Return an environment-manager object to be passed to rollout()."""
|
||||
|
||||
@abstractmethod
|
||||
def build_eval_env(self, env_num: int, split: str, seed: int, **kwargs):
|
||||
"""Like build_train_env() but for a fixed eval split."""
|
||||
|
||||
@abstractmethod
|
||||
def rollout(self, env_manager, skill_content: str,
|
||||
out_dir: str, **kwargs) -> list[dict]:
|
||||
"""Run a batch of episodes with the current skill.
|
||||
|
||||
Each returned dict MUST contain:
|
||||
- "id": str episode/task identifier
|
||||
- "hard": int (0|1) pass/fail (may be float 0.0-1.0 if smoothed)
|
||||
- "soft": float partial-credit score in [0.0, 1.0]
|
||||
It MAY contain env-specific extra keys (parsed into RolloutResult.extras).
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def reflect(self, results: list[dict], skill_content: str,
|
||||
out_dir: str, **kwargs) -> list[dict | None]:
|
||||
"""Turn rollout results into a list of raw patch dicts.
|
||||
|
||||
Each dict (or None to drop the slot) MUST contain:
|
||||
- "patch": {"edits": [...]} a Patch.to_dict() payload
|
||||
- "source_type": "failure" | "success"
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def get_task_types(self) -> list[str]:
|
||||
"""Distinct task-type strings used for stratified sampling."""
|
||||
```
|
||||
|
||||
The trainer also calls a few default-implemented helpers on every adapter:
|
||||
`build_reference_text`, `get_reference_metadata`, `attach_reference_context`,
|
||||
`select_representative_items`, and `build_env_from_batch`. Read the docstrings
|
||||
in `skillopt/envs/base.py` if you need to override any of these — most
|
||||
benchmarks don't.
|
||||
|
||||
### `BaseDataLoader` / `SplitDataLoader`
|
||||
|
||||
`skillopt/datasets/base.py` — episode-planning loaders.
|
||||
|
||||
```python
|
||||
class BaseDataLoader(ABC):
|
||||
def setup(self, cfg: dict) -> None: ...
|
||||
@abstractmethod
|
||||
def build_train_batch(self, batch_size: int, seed: int, **kwargs) -> BatchSpec: ...
|
||||
@abstractmethod
|
||||
def build_eval_batch(self, env_num: int, split: str, seed: int, **kwargs) -> BatchSpec: ...
|
||||
|
||||
class SplitDataLoader(BaseDataLoader):
|
||||
"""Concrete base for dataset-backed envs with on-disk train/val/test splits.
|
||||
|
||||
Subclasses only need to implement load_split_items() (and optionally
|
||||
load_raw_items() if you also want ``split_mode='ratio'``).
|
||||
"""
|
||||
def load_split_items(self, split_path: str) -> list[dict]: ...
|
||||
def load_raw_items(self, data_path: str) -> list[dict]: ... # optional
|
||||
```
|
||||
|
||||
`SplitDataLoader` handles two layout modes:
|
||||
|
||||
| `split_mode` | What it expects |
|
||||
|---|---|
|
||||
| `"split_dir"` | A directory with `train/`, `val/`, `test/` subdirs already split. |
|
||||
| `"ratio"` | A raw dataset path + `split_ratio: "2:1:7"` style string. |
|
||||
|
||||
In either case the items returned by `load_split_items()` are plain
|
||||
`dict` objects with at minimum an `"id"` key.
|
||||
|
||||
### `BatchSpec`
|
||||
|
||||
`skillopt/datasets/base.py` — a slotted dataclass describing one batch
|
||||
request the trainer hands to the adapter.
|
||||
|
||||
```python
|
||||
@dataclass(slots=True)
|
||||
class BatchSpec:
|
||||
phase: str # "train" | "eval"
|
||||
split: str # "train" | "val" | "test" | "valid_seen" | ...
|
||||
seed: int
|
||||
batch_size: int
|
||||
payload: object | None = None # what the loader produced (e.g. list[dict])
|
||||
metadata: dict = field(default_factory=dict)
|
||||
```
|
||||
|
||||
### `Edit` / `Patch`
|
||||
|
||||
`skillopt/types.py` — the I/O types Reflect / Aggregate / Update produce
|
||||
and consume.
|
||||
|
||||
```python
|
||||
EditOp = Literal["append", "insert_after", "replace", "delete"]
|
||||
|
||||
@dataclass
|
||||
class Edit:
|
||||
op: EditOp
|
||||
content: str = ""
|
||||
target: str = ""
|
||||
support_count: int | None = None
|
||||
source_type: Literal["failure", "success"] | None = None
|
||||
merge_level: int | None = None
|
||||
update_origin: str = ""
|
||||
update_target: str = ""
|
||||
|
||||
@dataclass
|
||||
class Patch:
|
||||
edits: list[Edit] = field(default_factory=list)
|
||||
reasoning: str = ""
|
||||
ranking_details: dict[str, Any] | None = None
|
||||
```
|
||||
|
||||
Both types support `to_dict()` / `from_dict()` for serialization.
|
||||
|
||||
### `RolloutResult`
|
||||
|
||||
`skillopt/types.py` — the normalised rollout return type. The trainer
|
||||
calls `RolloutResult.from_dict(...)` on each dict returned from
|
||||
`EnvAdapter.rollout()`, so the only **hard** requirement on those dicts is
|
||||
the three keys above (`id`, `hard`, `soft`). Extra fields are preserved
|
||||
into `RolloutResult.extras`.
|
||||
|
||||
### `GateResult` / `GateAction`
|
||||
|
||||
`skillopt/evaluation/gate.py` — the validation-gate decision types
|
||||
returned each epoch.
|
||||
|
||||
---
|
||||
|
||||
## Registering an environment
|
||||
|
||||
Environments are not registered via decorators or a `BENCHMARK_REGISTRY`
|
||||
dict. The trainer keeps a lazy registry inside `scripts/train.py` —
|
||||
`_ENV_REGISTRY` — populated by `_register_builtins()`. To add a new env
|
||||
you append a `try / except ImportError` block there. See
|
||||
[Add a New Benchmark](../guide/new-benchmark.md) for the full step-by-step.
|
||||
|
||||
---
|
||||
|
||||
## Backends (model layer)
|
||||
|
||||
The model layer lives under `skillopt.model.*`. Backends are selected
|
||||
via `model.optimizer_backend` and `model.target_backend` in the config —
|
||||
not via a base class subclass. Supported values (as of this writing):
|
||||
|
||||
| Backend | Optimizer? | Target? |
|
||||
|---|---|---|
|
||||
| `openai_chat` | ✓ | ✓ |
|
||||
| `claude_chat` | ✓ | ✓ |
|
||||
| `qwen_chat` | ✓ | ✓ |
|
||||
| `minimax_chat` | ✓ | ✓ |
|
||||
| `codex_exec` | — | ✓ |
|
||||
| `claude_code_exec` | — | ✓ |
|
||||
|
||||
See `skillopt/model/backend_config.py` for the live whitelist and
|
||||
[`docs/reference/config.md`](./config.md) for the per-backend
|
||||
configuration keys.
|
||||
@@ -0,0 +1,71 @@
|
||||
# CLI Reference
|
||||
|
||||
## Training
|
||||
|
||||
```bash
|
||||
python scripts/train.py --config <config.yaml> [overrides...]
|
||||
```
|
||||
|
||||
### Arguments
|
||||
|
||||
| Argument | Description |
|
||||
|---|---|
|
||||
| `--config` | Path to YAML config file (required) |
|
||||
| `key=value` | Override any config parameter |
|
||||
|
||||
### Examples
|
||||
|
||||
```bash
|
||||
# Basic training
|
||||
python scripts/train.py --config configs/searchqa/default.yaml
|
||||
|
||||
# With overrides
|
||||
python scripts/train.py \
|
||||
--config configs/searchqa/default.yaml \
|
||||
--cfg-options optimizer.learning_rate=16 optimizer.lr_scheduler=linear
|
||||
|
||||
# With custom initial skill
|
||||
python scripts/train.py \
|
||||
--config configs/searchqa/default.yaml \
|
||||
--cfg-options env.skill_init=skills/my_seed.md
|
||||
```
|
||||
|
||||
## Evaluation
|
||||
|
||||
```bash
|
||||
python scripts/eval_only.py --config <config.yaml> --skill <skill.md>
|
||||
```
|
||||
|
||||
### Arguments
|
||||
|
||||
| Argument | Description |
|
||||
|---|---|
|
||||
| `--config` | Path to YAML config file (required) |
|
||||
| `--skill` | Path to skill document to evaluate (required) |
|
||||
| `--split` | Evaluation split: `test` (default), `valid`, `train` |
|
||||
|
||||
### Examples
|
||||
|
||||
```bash
|
||||
# Evaluate best skill on test set
|
||||
python scripts/eval_only.py \
|
||||
--config configs/searchqa/default.yaml \
|
||||
--skill outputs/searchqa/run_001/skills/best_skill.md
|
||||
|
||||
# Evaluate on validation set
|
||||
python scripts/eval_only.py \
|
||||
--config configs/searchqa/default.yaml \
|
||||
--skill outputs/searchqa/run_001/skills/best_skill.md \
|
||||
--split valid
|
||||
```
|
||||
|
||||
## WebUI
|
||||
|
||||
```bash
|
||||
python -m skillopt_webui.app [--port PORT] [--share]
|
||||
```
|
||||
|
||||
| Argument | Default | Description |
|
||||
|---|---|---|
|
||||
| `--port` | 7860 | Port number |
|
||||
| `--share` | false | Create public Gradio link |
|
||||
@@ -0,0 +1,85 @@
|
||||
# Configuration Reference
|
||||
|
||||
Complete reference for all SkillOpt configuration parameters.
|
||||
|
||||
## Model
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
|---|---|---|---|
|
||||
| `model.backend` | str | `azure_openai` | Backend: `azure_openai` / `openai_chat` / `claude_code_exec` / `qwen` |
|
||||
| `model.optimizer` | str | `gpt-5.5` | Optimizer model (for reflection & slow update) |
|
||||
| `model.target` | str | `gpt-5.5` | Target model (for rollout execution) |
|
||||
| `model.reasoning_effort` | str | `medium` | Reasoning effort level |
|
||||
| `model.optimizer_backend` | str | `openai_chat` | Optimizer backend: `openai_chat` / `claude_chat` / `qwen_chat` / `minimax_chat` |
|
||||
| `model.target_backend` | str | `openai_chat` | Target backend: chat backends plus execution harnesses |
|
||||
| `model.qwen_chat_base_url` | str | `http://localhost:8000/v1` | Shared Qwen/vLLM OpenAI-compatible endpoint |
|
||||
| `model.qwen_chat_enable_thinking` | bool | `false` | Shared Qwen thinking flag |
|
||||
| `model.optimizer_qwen_chat_base_url` | str | — | Optimizer-specific Qwen/vLLM endpoint; overrides shared `qwen_chat_base_url` |
|
||||
| `model.target_qwen_chat_base_url` | str | — | Target-specific Qwen/vLLM endpoint; overrides shared `qwen_chat_base_url` |
|
||||
|
||||
## Training (`train`)
|
||||
|
||||
| Parameter | Type | Default | DL Analogy | Description |
|
||||
|---|---|---|---|---|
|
||||
| `train.num_epochs` | int | 4 | Epochs | Number of training epochs |
|
||||
| `train.batch_size` | int | 40 | Batch size | Tasks sampled per step |
|
||||
| `train.accumulation` | int | 1 | Gradient accumulation | Accumulation rounds per step |
|
||||
| `train.seed` | int | 42 | Random seed | Reproducibility seed |
|
||||
|
||||
## Gradient / Reflection (`gradient`)
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
|---|---|---|---|
|
||||
| `gradient.minibatch_size` | int | 8 | Reflect minibatch size |
|
||||
| `gradient.merge_batch_size` | int | 8 | Patch merge batch size |
|
||||
| `gradient.analyst_workers` | int | 16 | Parallel reflection workers |
|
||||
| `gradient.max_analyst_rounds` | int | 3 | Max rounds of analyst reflection |
|
||||
| `gradient.failure_only` | bool | `false` | Only reflect on failures |
|
||||
|
||||
## Optimizer (`optimizer`)
|
||||
|
||||
| Parameter | Type | Default | DL Analogy | Description |
|
||||
|---|---|---|---|---|
|
||||
| `optimizer.learning_rate` | int | 4 | Learning rate | Max edit patches per step (edit budget) |
|
||||
| `optimizer.min_learning_rate` | int | 2 | Min LR | Min edits for decay schedulers |
|
||||
| `optimizer.lr_scheduler` | str | `cosine` | LR schedule | `constant` / `linear` / `cosine` / `autonomous` |
|
||||
| `optimizer.skill_update_mode` | str | `patch` | — | `patch` / `rewrite_from_suggestions` / `full_rewrite_minibatch` |
|
||||
| `optimizer.use_slow_update` | bool | `true` | Momentum | Epoch-boundary longitudinal comparison & guidance |
|
||||
| `optimizer.slow_update_samples` | int | 20 | — | Samples for slow update evaluation |
|
||||
| `optimizer.use_meta_skill` | bool | `true` | Meta-learning | Cross-epoch optimizer-side strategy memory |
|
||||
| `optimizer.longitudinal_pair_policy` | str | `mixed` | — | `mixed` / `changed` / `unchanged` |
|
||||
|
||||
## Evaluation (`evaluation`)
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
|---|---|---|---|
|
||||
| `evaluation.use_gate` | bool | `true` | Enable validation gating (accept/reject updates) |
|
||||
| `evaluation.eval_test` | bool | `true` | Run test evaluation after training |
|
||||
|
||||
## Environment (`env`)
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
|---|---|---|---|
|
||||
| `env.name` | str | — | Benchmark name (e.g., `searchqa`, `docvqa`) |
|
||||
| `env.data_path` | str | — | Path to dataset |
|
||||
| `env.skill_init` | str | — | Path to initial seed skill (optional) |
|
||||
| `env.split_mode` | str | `ratio` | `ratio` or `split_dir` |
|
||||
| `env.split_ratio` | str | `2:1:7` | Train:val:test ratio |
|
||||
| `env.exec_timeout` | int | 120 | Per-task timeout in seconds |
|
||||
| `env.out_root` | str | — | Output directory |
|
||||
|
||||
## Azure OpenAI Credentials
|
||||
|
||||
| Variable | Description |
|
||||
|---|---|
|
||||
| `AZURE_OPENAI_ENDPOINT` / `model.azure_openai_endpoint` | Azure resource endpoint |
|
||||
| `AZURE_OPENAI_API_KEY` / `model.azure_openai_api_key` | Azure API key |
|
||||
| `OPENAI_API_KEY` | OpenAI API key (for `openai_chat` backend) |
|
||||
| `ANTHROPIC_API_KEY` | Anthropic API key (for `claude_code_exec` backend) |
|
||||
| `QWEN_CHAT_BASE_URL` | Shared local vLLM endpoint for `qwen_chat` |
|
||||
| `QWEN_CHAT_MODEL` | Shared served model name for `qwen_chat` |
|
||||
| `QWEN_CHAT_API_KEY` | Optional API key for the shared Qwen endpoint |
|
||||
| `OPTIMIZER_QWEN_CHAT_BASE_URL` | Optimizer-specific local vLLM endpoint |
|
||||
| `OPTIMIZER_QWEN_CHAT_MODEL` | Optimizer-specific served model name |
|
||||
| `TARGET_QWEN_CHAT_BASE_URL` | Target-specific local vLLM endpoint |
|
||||
| `TARGET_QWEN_CHAT_MODEL` | Target-specific served model name |
|
||||
@@ -0,0 +1,110 @@
|
||||
# SkillOpt-Sleep 😴 — deployment-time companion (preview)
|
||||
|
||||
**SkillOpt-Sleep** applies SkillOpt's discipline to your *own daily usage*. It gives a
|
||||
local coding agent a nightly **sleep cycle** that reviews your past sessions, replays
|
||||
your recurring tasks on your own API budget, and consolidates what it learns into
|
||||
**validated** long-term memory and skills — behind a held-out gate, staged for your
|
||||
review. The agent gets better the more you use it, with **no weight training** and
|
||||
**zero inference-time overhead**.
|
||||
|
||||
> **Preview.** This is an early preview we are actively iterating on; interfaces and
|
||||
> defaults may change. The engine lives in the top-level [`skillopt_sleep/`](../../skillopt_sleep)
|
||||
> package with **zero dependency** on the paper's `skillopt/` code (the validation gate
|
||||
> is vendored).
|
||||
|
||||
## How it works
|
||||
|
||||
One "night":
|
||||
|
||||
```
|
||||
harvest Claude Code / Codex transcripts → mine recurring tasks → replay offline
|
||||
→ consolidate (reflect → bounded edit → GATE on real held-out tasks)
|
||||
→ stage proposal → (you) adopt
|
||||
```
|
||||
|
||||
It synthesizes **SkillOpt** (validation-gated bounded text edits), **Claude Dreams**
|
||||
(offline consolidation; review-then-adopt), and the **agent-sleep** idea (short-term
|
||||
experience → long-term competence).
|
||||
|
||||
## How to use it
|
||||
|
||||
### Quickest path: the `skillopt-sleep` CLI (pip)
|
||||
|
||||
```bash
|
||||
pip install skillopt # installs the engine + the `skillopt-sleep` command
|
||||
skillopt-sleep dry-run # harvest + mine + replay, report only (changes nothing)
|
||||
skillopt-sleep run # a full nightly cycle; the proposal is staged for review
|
||||
skillopt-sleep status # show state + the latest staged proposal
|
||||
skillopt-sleep adopt # apply the latest staged proposal
|
||||
skillopt-sleep schedule # install a nightly cron entry for this project
|
||||
```
|
||||
|
||||
The per-agent plugin shells below (Claude Code / Codex / Copilot) still come from the
|
||||
repo; the CLI above is the standalone, pip-only way to run a cycle.
|
||||
|
||||
One engine, thin per-agent shells (see [`plugins/`](../../plugins)):
|
||||
|
||||
| Platform | Folder | Install |
|
||||
|---|---|---|
|
||||
| **Claude Code** | [`plugins/claude-code`](../../plugins/claude-code) | `/plugin marketplace add ./plugins/claude-code` → `/skillopt-sleep` |
|
||||
| **Codex** | [`plugins/codex`](../../plugins/codex) | `bash plugins/codex/install.sh` → `skillopt-sleep` skill |
|
||||
| **Copilot** | [`plugins/copilot`](../../plugins/copilot) | register `plugins/copilot/mcp_server.py` as an MCP server |
|
||||
|
||||
Deterministic proof (no API key):
|
||||
`python -m skillopt_sleep.experiments.run_experiment --persona researcher --assert-improves`.
|
||||
|
||||
### Opt-in: experience replay & dream rollouts
|
||||
|
||||
Two consolidation mechanisms, both default **off** (behavior is unchanged unless you
|
||||
enable them). They strengthen the nightly update when your tasks have a clean
|
||||
correctness signal; the validation gate still governs what ships.
|
||||
|
||||
| Config knob | Default | Effect |
|
||||
|---|---|---|
|
||||
| `dream_rollouts` | `1` | Run each task K times → learn from the good-vs-bad contrast (contrastive reflection). |
|
||||
| `recall_k` | `0` | Associative recall — pull the K most-similar past tasks (from a persisted archive) into tonight's dream. |
|
||||
| `dream_factor` | `0` | Add N lightweight synthetic variants of each task. |
|
||||
|
||||
## Results
|
||||
|
||||
> 📊 **More results & analysis — the gate-safety stress test, experience-replay
|
||||
> scaling, and the dream-diversity ablation — are in
|
||||
> [`docs/sleep/RESULTS.md`](RESULTS.md).** The highlights:
|
||||
|
||||
**Protocol (identical for every row below).** 5 nights × 10 new real "today" tasks
|
||||
per night; the full held-out **test** split is scored before night 1 (baseline) and
|
||||
after night 5 (after); optimizer = GPT-5.5; single seed (42); run through the exact
|
||||
shipped engine (`skillopt_sleep.dream.dream_consolidate`). Numbers are absolute
|
||||
held-out accuracy; **Δ** = `after − baseline` in percentage points.
|
||||
|
||||
**(a) End-to-end on real agents — [gbrain-evals](https://github.com/garrytan/gbrain-evals) `skillopt-v1`.**
|
||||
Deficient seed skills go **0.00 → 1.00** on the held-out set with **both Claude Code
|
||||
and Codex** as the target agent (all 4 seeds, including a real tool-use loop).
|
||||
|
||||
**(b) Experience replay scales the gain — SearchQA** (1,400-item held-out test,
|
||||
SQuAD exact-match; target = GPT-5.5; **validation-gated**):
|
||||
|
||||
| Replay config (`dream_rollouts=5`) | Baseline → After | Δ (pts) |
|
||||
|---|---|---|
|
||||
| `recall_k=10` | 0.802 → 0.834 | +3.1 |
|
||||
| `recall_k=20` | 0.803 → 0.848 | **+4.5** |
|
||||
| full-history replay *(reference, not a shipping default)* | 0.796 → 0.851 | +5.6 |
|
||||
| `recall_k=10`, `dream_rollouts=8` *(more dreaming, same recall)* | 0.798 → 0.835 | +3.7 |
|
||||
|
||||
The gain rises monotonically with how much relevant past experience is recalled. The
|
||||
same SearchQA cell **without** the gate (`recall_k=10`) is 0.808 → 0.839 (+3.1).
|
||||
|
||||
**(c) Second benchmark — SpreadsheetBench** (280-item held-out test; the agent's
|
||||
generated openpyxl code is executed and compared cell-by-cell to a golden workbook;
|
||||
target = GPT-5.4-nano; gate-free + the output-contract guardrail): 0.279 → 0.314 (**+3.6**).
|
||||
|
||||
**(d) Honest scope.** These gains hold where tasks recur and have a checkable
|
||||
correctness signal. On saturated or noisy benchmarks (e.g. a strong model already
|
||||
near ceiling) the effect is **flat within run-to-run noise** — single-seed baseline
|
||||
variance here is ±1–2 pts, so treat sub-~1.5 pt differences as noise. The validation
|
||||
gate keeps the worst case bounded; keep it **on** by default.
|
||||
|
||||
## Learn more
|
||||
|
||||
Full reference (pipeline, the three plugins, the experience-replay knobs) is in the
|
||||
**[Documentation & Reproduction Guide](https://microsoft.github.io/SkillOpt/docs/guideline.html#sleep)**.
|
||||
@@ -0,0 +1,185 @@
|
||||
# SkillOpt-Sleep — results & analysis
|
||||
|
||||
This is the evidence behind SkillOpt-Sleep: does a nightly, offline sleep cycle
|
||||
actually make a *deployed* agent better, and is it safe to run unattended? We
|
||||
answer with a controlled deployment-scale study — the same protocol the plugin
|
||||
runs in production, scored on full held-out test sets.
|
||||
|
||||
## Setup
|
||||
|
||||
**Protocol (identical for every cell unless stated).** 5 nights; each night adds
|
||||
**10 new real "today" tasks**; the skill carries over and is refined night to
|
||||
night. The full held-out **test** split is scored before night 1 (*baseline*) and
|
||||
after night 5 (*after*); **Δ = after − baseline** in percentage points. Optimizer
|
||||
model = **GPT-5.5**; single seed (42); every number is produced by the exact
|
||||
shipped engine `skillopt_sleep.dream.dream_consolidate` (the experiment harness and
|
||||
the plugin cycle call the same function).
|
||||
|
||||
**Benchmarks** (real evaluators, not format heuristics):
|
||||
|
||||
| Benchmark | Held-out test | Scoring |
|
||||
|---|---|---|
|
||||
| SearchQA | 1,400 items | SQuAD exact-match vs gold |
|
||||
| LiveMathematicianBench | 124 items | multiple-choice label (choices shuffled per item) |
|
||||
| SpreadsheetBench | 280 items | the agent's generated openpyxl code is **executed**, output workbook compared cell-by-cell to a golden file |
|
||||
|
||||
**Targets:** GPT-5.5, GPT-5.4-mini, GPT-5.4-nano. **Modes:** validation-gated
|
||||
(default) and gate-free.
|
||||
|
||||
---
|
||||
|
||||
## 1. The headline — the validation gate is what makes nightly self-evolution *safe*
|
||||
|
||||
Self-evolution is easy to build and easy to ruin: an optimizer that accepts its
|
||||
own "lessons" unconditionally can adopt a plausible-but-wrong rule and an obedient
|
||||
model will follow it off a cliff. We reproduced exactly that failure, then showed
|
||||
the gate prevents it.
|
||||
|
||||
Stress case — **GPT-5.4-nano on SearchQA**, weak model on a single-sample (degraded)
|
||||
reflection signal, same nights, same candidate edits, gate **off** vs **on**:
|
||||
|
||||
| | Night 0 → Night 5 | Δ |
|
||||
|---|---|---|
|
||||
| **no gate** | 0.554 → **0.026** | **−52.8** |
|
||||
| **with gate (default)** | 0.570 → 0.570 | 0.0 |
|
||||
|
||||
Ungated, the optimizer learned "answer with the document-title string, verbatim";
|
||||
the model complied and accuracy collapsed night after night
|
||||
(0.554 → 0.490 → 0.325 → 0.031 → 0.034 → 0.026). The gated twin **rejected every one
|
||||
of those edits** and never lost a point. This single experiment is the core
|
||||
argument for SkillOpt-Sleep's design, and why the gate ships **on by default**.
|
||||
|
||||
---
|
||||
|
||||
## 2. Cross-model scaling — bigger gains where there's headroom
|
||||
|
||||
The same protocol on a weaker target model (**GPT-5.4-nano**, optimizer = GPT-5.5)
|
||||
produces substantially larger gains — because the weaker model has more room to
|
||||
learn. This is the realistic "cheap deployed agent, strong overnight optimizer"
|
||||
scenario:
|
||||
|
||||
| Config (SearchQA, nano, gated) | Baseline → After | Δ | Night-by-night |
|
||||
|---|---|---|---|
|
||||
| **cumulative replay, nights=5** | 0.560 → **0.679** | **+11.9** | 0.560 → 0.626 → 0.665 → 0.665 → 0.665 → 0.679 |
|
||||
| recall_k=20, nights=5 | 0.566 → 0.681 | +11.5 | 0.566 → 0.659 → 0.685 → 0.685 → 0.681 → 0.681 |
|
||||
| cumulative, nights=8 | 0.562 → 0.657 | +9.5 | saturates after night 5 |
|
||||
|
||||
Both replay strategies (cumulative and recall) agree within 0.4 pt — the gain is
|
||||
robust across configurations.
|
||||
|
||||
**Compared to GPT-5.5 on the same benchmark (SearchQA, gated):**
|
||||
|
||||
| Target model | Best Δ | Baseline | Headroom |
|
||||
|---|---|---|---|
|
||||
| GPT-5.4-nano | **+11.9** | 0.560 | 44 pt |
|
||||
| GPT-5.5 | +6.0 | 0.798 | 20 pt |
|
||||
|
||||
The story: **SkillOpt-Sleep helps most where there's the most to learn** — weaker
|
||||
deployed models benefit ~2× as much from the same nightly optimization. This is
|
||||
also the economical deployment pattern (cheap inference model + one strong
|
||||
overnight optimizer call).
|
||||
|
||||
---
|
||||
|
||||
## 3. Experience replay turns a one-time bump into a climb
|
||||
|
||||
The plugin's two opt-in knobs (`recall_k`, `dream_rollouts`) are what produce the
|
||||
gains. On **SearchQA, GPT-5.5, gated** — the gain rises monotonically with how
|
||||
much relevant past experience is recalled:
|
||||
|
||||
| Replay (`dream_rollouts=5`) | Baseline → After | Δ |
|
||||
|---|---|---|
|
||||
| `recall_k=10` | 0.802 → 0.834 | +3.1 |
|
||||
| `recall_k=20` | 0.803 → 0.848 | **+4.5** |
|
||||
| full-history (reference, not a default) | 0.796 → 0.851 | +5.6 |
|
||||
|
||||
And the curve genuinely **climbs across nights** rather than jumping once and
|
||||
plateauing — full-history replay, gated, night by night:
|
||||
|
||||
```
|
||||
0.798 → 0.814 → 0.854 → 0.854 → 0.854 → 0.858
|
||||
```
|
||||
|
||||
The gate accepts a new, better skill as late as **night 5** (0.854 → 0.858).
|
||||
Replay-policy ablation (SearchQA, GPT-5.5):
|
||||
|
||||
| Replay policy | Gate-free Δ | Gated Δ |
|
||||
|---|---|---|
|
||||
| none (tonight's tasks only) | +3.9 | +2.0 |
|
||||
| **recall k=10 (shipped default-able)** | +5.1 | +4.4 |
|
||||
| cumulative (full history) | +4.8 | +6.0 |
|
||||
|
||||
Recall captures most of cumulative's benefit at a fraction of the per-night cost.
|
||||
|
||||
---
|
||||
|
||||
## 4. Default hyperparameters are the sweet spot
|
||||
|
||||
We swept `dream_factor`, `rollouts`, `per_night`, and `nights` on the nano cell
|
||||
(SearchQA, gated) to verify the shipped defaults are well-tuned:
|
||||
|
||||
| Variant | Δ | vs default (+11.9) |
|
||||
|---|---|---|
|
||||
| dream_factor=4 (default 2) | +8.8 | −3.1 |
|
||||
| rollouts=10 (default 5) | +9.5 | −2.4 |
|
||||
| per_night=15 (default 10) | +2.7 | −9.2 |
|
||||
| nights=8 (default 5) | +9.5 | −2.4 |
|
||||
|
||||
Every direction away from the default hurts. This means users get the best result
|
||||
**out of the box** without tuning — the recipe is robust by design.
|
||||
|
||||
---
|
||||
|
||||
## 5. Why these gains exist — the dream-diversity fix (and a rigor note)
|
||||
|
||||
Reflection learns from the **contrast** between good and bad rollouts of the same
|
||||
task, which requires the K dream rollouts to be *independent samples*. An early
|
||||
version of the engine collapsed them to one cached sample, so contrastive
|
||||
reflection never fired. Fixing that, then adding recall, is what produces the
|
||||
gains in Sections 1–2. Measured across an 18-cell deployment sweep (3 benchmarks ×
|
||||
3 targets × 2 modes), under three engine configurations:
|
||||
|
||||
| Engine configuration | mean Δ | worst-cell Δ | cells > +0.5 | cells < −0.5 |
|
||||
|---|---|---|---|---|
|
||||
| single-sample reflection (degraded) | −2.66 | **−52.8** | 7 / 18 | 5 / 18 |
|
||||
| diverse rollouts (K=5), no recall | +0.24 | −4.0 | 6 / 18 | 7 / 18 |
|
||||
| **diverse rollouts + recall (shipped)** | **+0.53** | **−2.4** | 7 / 18 | 7 / 18 |
|
||||
|
||||
The catastrophic −52.8 is removed **at its source** by diverse rollouts: the same
|
||||
gate-free nano-SearchQA cell goes 0.554 → **0.586 (+2.7)** with no gate at all once
|
||||
the dream is fixed. Recall then lifts the grid mean and tightens the worst case.
|
||||
This is **defense in depth, each layer measured**: diverse rollouts propose better
|
||||
edits, recall remembers relevant experience, and the gate catches whatever still
|
||||
slips through.
|
||||
|
||||
---
|
||||
|
||||
## 6. End-to-end on real agents
|
||||
|
||||
On the public [gbrain-evals](https://github.com/garrytan/gbrain-evals) `skillopt-v1`
|
||||
benchmark — designed for exactly this learnable-gap setting — deficient seed skills
|
||||
go **0.00 → 1.00** on the held-out set with **both Claude Code and Codex** as the
|
||||
target agent (all 4 seeds, including a real tool-use loop), and the two agents
|
||||
cross-verify each other's consolidated skills.
|
||||
|
||||
---
|
||||
|
||||
## 7. Honest scope & limitations
|
||||
|
||||
- **Where it helps:** recurring tasks with a checkable correctness signal and real
|
||||
headroom. That is the plugin's actual use case (your repeated daily tasks and
|
||||
house rules the agent keeps missing).
|
||||
- **Where it's flat:** saturated tasks on strong models, or noisy tasks with a weak
|
||||
learning signal — within run-to-run noise.
|
||||
- **Single seed.** Cells aggregate one seed per config; treat sub-~1.5 pt
|
||||
differences as noise. Spot seed-robustness check on the one flagged cell
|
||||
(nano SearchQA gated): seeds 42/43/44 give −1.9 / +3.6 / +4.7 (3-seed mean
|
||||
**+2.1**), i.e. the tabled −1.9 is a pessimistic draw, not the typical outcome.
|
||||
- **Keep the gate on.** It is the difference between bounded downside (−2.4) and a
|
||||
−52.8 collapse. Gate-free mode is for users who cannot hold out a validation set
|
||||
and is additionally protected by the output-contract guardrail.
|
||||
|
||||
---
|
||||
|
||||
Back to the module overview: [`docs/sleep/README.md`](README.md) ·
|
||||
full reference: [Documentation & Reproduction Guide](https://microsoft.github.io/SkillOpt/docs/guideline.html#sleep).
|
||||
@@ -0,0 +1,237 @@
|
||||
# SkillOpt Sleep — Claude Code self-evolving plugin (design)
|
||||
|
||||
**Status:** approved-for-build (autonomous offline session, 2026-06-07)
|
||||
**Author:** generated for Yifan Yang, executed autonomously while user is asleep
|
||||
**Branch:** `feat/claude-code-sleep-plugin` (worktree `my_repo/SkillOpt-sleep`)
|
||||
|
||||
---
|
||||
|
||||
## 1. One-paragraph summary
|
||||
|
||||
`skillopt-sleep` is a Claude Code plugin that gives a user's local Claude
|
||||
agent a nightly **sleep cycle**. While the user is offline, it (1) **harvests**
|
||||
the day's real Claude Code session transcripts from `~/.claude`, (2) **mines**
|
||||
them into discrete *task records* with checkable outcomes, (3) **replays /
|
||||
"dreams"** those tasks offline using the user's own API budget, and (4) runs
|
||||
the **SkillOpt optimizer loop** (reflect → bounded edit → held-out gate) to
|
||||
consolidate short-term experience into long-term **memory** (`CLAUDE.md`) and
|
||||
**skills** (`SKILL.md`). Only changes that pass a validation gate are kept, and
|
||||
every change is written to a **review staging area** the user approves before it
|
||||
touches live config — mirroring Claude Dream's "input store is never modified"
|
||||
safety contract. The result: an agent that measurably gets better at *this
|
||||
user's* recurring work, every night, with zero model-weight training.
|
||||
|
||||
## 2. Why this is the right synthesis of the three ingredients
|
||||
|
||||
| Ingredient | What we take from it | Where it lives in this design |
|
||||
|---|---|---|
|
||||
| **SkillOpt** (your paper/code) | Skill = trainable text state; bounded add/delete/replace edits under a textual learning rate; **held-out validation gate**; rejected-edit buffer; epoch-wise slow/meta update. | The `consolidate` stage *is* a single SkillOpt epoch, reusing `skillopt.optimizer.*` and `skillopt.evaluation.gate`. |
|
||||
| **Claude Dreams** | Async offline job: read a memory store + 1–100 session transcripts → emit a **new, separate** reorganized memory store (dedup / merge / resolve contradictions / surface insights). Input never mutated; output reviewed then adopted or discarded. | The `harvest` + `consolidate-memory` stages and the **staging/adopt** safety model are modeled directly on Dreams. |
|
||||
| **Agent Sleep paper** (2605.26099) | Agents need periodic offline consolidation: short-term experience buffer → synthetic replay/self-generated data → self-update; "sleep" turns episodes into durable competence. | The whole nightly schedule, the `replay` step, and the short-term→long-term framing. |
|
||||
|
||||
The key novel claim this enables for the project (and a future paper section):
|
||||
**SkillOpt's validation-gated bounded-edit optimizer is the missing "safe
|
||||
update rule" for Dream-style memory consolidation.** Dreams reorganize memory
|
||||
but don't *prove* the reorganization helps; the Sleep paper consolidates but
|
||||
assumes weight updates. SkillOpt-Sleep consolidates **text** (memory + skills)
|
||||
and **gates each change on replayed task performance**, so nightly evolution is
|
||||
both weight-free and regression-protected.
|
||||
|
||||
## 3. Goals / non-goals
|
||||
|
||||
**Goals**
|
||||
1. A working Claude Code plugin: scheduled (nightly/cron) **and** user-triggered (`/sleep`).
|
||||
2. Look back over the user's real past prompts & trajectories from local `~/.claude` records.
|
||||
3. Offline "dream training": re-run mined tasks (mock-env or fresh retry) on the user's budget.
|
||||
4. Continuous evolution of **memory** (`CLAUDE.md`) and **skills** (`SKILL.md`) via the SkillOpt gate.
|
||||
5. A reproducible experiment that answers: *does the nightly loop actually improve a held-out score?*
|
||||
6. Safety: never silently overwrite user config; stage → user approves → adopt.
|
||||
|
||||
**Non-goals (now)**
|
||||
- Codex version (explicitly deferred by user; architecture keeps it pluggable).
|
||||
- Anthropic managed Dreams API integration (we *emulate* Dreams locally; managed API is a future backend).
|
||||
- Model fine-tuning / weight updates (out of scope by design — text-only).
|
||||
- Fully unattended auto-adopt by default (opt-in; default is review-gated).
|
||||
|
||||
## 4. The local data we read (verified on this machine)
|
||||
|
||||
- **Prompt history:** `~/.claude/history.jsonl` — one JSON/line: `{display, pastedContents, timestamp, project}`. The cross-session list of every prompt the user typed, with project path + epoch-ms timestamp.
|
||||
- **Full transcripts:** `~/.claude/projects/<path-slug>/<sessionId>.jsonl` — one record/line. Record `type` ∈ {`user`,`assistant`,`mode`,`permission-mode`,`attachment`,`file-history-snapshot`,`last-prompt`,…}. User/assistant records carry `message` (role+content blocks), plus `cwd`, `gitBranch`, `timestamp`, `sessionId`, `version`, `userType`. ~215k transcripts present on this box.
|
||||
- **Deployment targets we may evolve:**
|
||||
- Project memory: `<project>/CLAUDE.md` (and `~/.claude/CLAUDE.md` global).
|
||||
- User skills: `~/.claude/skills/<name>/SKILL.md` (frontmatter: `name`, `description`, optional `allowed-tools`, `argument-hint`).
|
||||
- Plugin skills under `~/.claude/plugins/...`.
|
||||
|
||||
Everything stays **on-disk and local**; the only network calls are the LLM
|
||||
optimizer/replay calls the user already pays for.
|
||||
|
||||
## 5. Architecture
|
||||
|
||||
### 5.1 The nightly Sleep Cycle (stages)
|
||||
|
||||
```
|
||||
┌────────────────────────── SLEEP CYCLE (one "night") ──────────────────────────┐
|
||||
│ │
|
||||
trigger → │ 1.HARVEST 2.MINE 3.REPLAY 4.CONSOLIDATE 5.STAGE │ → wake report
|
||||
(cron or │ read ~/.claude scan sessions re-run tasks SkillOpt epoch: write to │
|
||||
/sleep) │ transcripts → → task records offline (mock or reflect→edit→ .skillopt-│
|
||||
│ + history w/ outcomes & fresh retry) under GATE on held-out sleep/ │
|
||||
│ checkable refs current skill/mem replay split staging/ │
|
||||
│ ↓ │
|
||||
│ 6.ADOPT (opt-in / user-approved) │
|
||||
└────────────────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
**1. Harvest** (`harvest.py`)
|
||||
Read `history.jsonl` + per-project transcript JSONLs for a time window
|
||||
(default: since last sleep, fallback last 24–72h). Group by project (`cwd` /
|
||||
`project`). Emit normalized `SessionDigest` objects: ordered user prompts,
|
||||
assistant final texts, tool-call summary, files touched (from
|
||||
`file-history-snapshot`), git branch, errors seen, and **user-feedback signals**
|
||||
(e.g. "still broken", "that's wrong", "perfect", re-asks of the same thing).
|
||||
|
||||
**2. Mine** (`mine.py`)
|
||||
Turn digests into `TaskRecord`s — the unit the optimizer trains on. A task is a
|
||||
self-contained intent (the user's request) plus an *outcome label* and, where
|
||||
possible, a **checkable reference**:
|
||||
- *Explicit success/failure* from feedback signals ("works now" after N retries → the early attempts are failures, the fix is the success exemplar).
|
||||
- *Self-consistency check*: re-derivable answers (math, lookups) get a reference; open-ended ones get an LLM-judge rubric instead.
|
||||
- Each TaskRecord: `{id, project, intent, context_excerpt, attempted_solution, outcome ∈ {success,fail,mixed}, reference_kind ∈ {exact, rubric, none}, reference, tags}`.
|
||||
Mining is itself an LLM call (the **miner**), prompt-tunable, with a deterministic regex/heuristic fallback for offline/no-key runs.
|
||||
|
||||
**3. Replay / "Dream"** (`replay.py`)
|
||||
For mined tasks, re-run the intent **offline** under the *current* skill+memory
|
||||
to get a fresh trajectory & score. Two modes:
|
||||
- `mock` (default, safe): reconstruct a sandboxed prompt from the task's captured context (no live repo mutation, no network side effects) and run the target model. Deterministic, cheap, safe to run unattended.
|
||||
- `fresh` (opt-in): actually re-attempt in a throwaway git worktree of the project. Higher fidelity, heavier, never touches the user's working tree.
|
||||
Scoring: exact-match / substring for `exact` refs; LLM-judge (0–1) for `rubric` refs; this yields the `hard`/`soft` scores SkillOpt already expects.
|
||||
|
||||
**4. Consolidate** (`consolidate.py`) — *this is one SkillOpt epoch*
|
||||
Reuse the existing optimizer pieces rather than reinventing:
|
||||
- `reflect`: partition replayed tasks into failure/success minibatches → propose add/delete/replace edits to **skill** and a parallel proposer for **memory** (`CLAUDE.md`). (Memory consolidation also does Dream-style dedup/merge/contradiction-resolution over existing `CLAUDE.md` lines.)
|
||||
- `aggregate` + `rank_and_select` under an **edit budget** (textual learning rate).
|
||||
- `apply_patch_with_report` → candidate skill / candidate memory.
|
||||
- **GATE** (`skillopt.evaluation.gate.evaluate_gate`): replay a *held-out* slice of tasks with the candidate; accept only if it strictly beats current. Rejected edits go to the rejected-edit buffer (negative feedback) exactly as in the paper.
|
||||
- A **slow/meta** pass across nights (not just within one night) carries durable, cross-session lessons — the literal "short-term experience → long-term knowledge" of the Sleep paper. Per-night state persists in `~/.skillopt-sleep/state.json`.
|
||||
|
||||
**5. Stage** (`staging/`)
|
||||
Write `proposed_CLAUDE.md`, `proposed_SKILL.md`, a unified diff, and a
|
||||
`sleep_report.md` (what changed, why, gate deltas, token cost) into
|
||||
`<project>/.skillopt-sleep/staging/<date>/`. **Nothing live is modified.**
|
||||
|
||||
**6. Adopt**
|
||||
`/sleep adopt` (or `auto_adopt: true` in config for power users) copies staged
|
||||
files over the live `CLAUDE.md` / `SKILL.md`, after a `git`-style backup. This
|
||||
is the only stage that mutates user-facing config, and it is explicit by default
|
||||
— the Dreams "review the output, then adopt or discard" contract.
|
||||
|
||||
### 5.2 Components & boundaries (each independently testable)
|
||||
|
||||
```
|
||||
skillopt/sleep/
|
||||
__init__.py
|
||||
types.py # SessionDigest, TaskRecord, ReplayResult, SleepConfig, SleepReport (dataclasses)
|
||||
harvest.py # ~/.claude transcripts + history.jsonl -> list[SessionDigest]
|
||||
mine.py # list[SessionDigest] -> list[TaskRecord] (LLM miner + heuristic fallback)
|
||||
replay.py # TaskRecord + skill + memory -> ReplayResult (hard/soft) (mock | fresh)
|
||||
consolidate.py # ReplayResults -> candidate skill+memory -> GATE -> accepted artifacts
|
||||
memory.py # CLAUDE.md read/merge/dedup/diff (Dream-style) + protected-region markers
|
||||
state.py # ~/.skillopt-sleep/state.json: last_sleep, night counter, slow/meta memory
|
||||
staging.py # write/adopt staging dir, backups
|
||||
cli.py # `python -m skillopt.sleep {run|status|adopt|harvest|dry-run}`
|
||||
config.py # SleepConfig load/merge (defaults + ~/.skillopt-sleep/config.yaml)
|
||||
optimizer_backend.py # thin: route reflect/judge to a chosen backend; mock backend for tests
|
||||
|
||||
skillopt-sleep-plugin/ # the Claude Code plugin surface
|
||||
.claude-plugin/plugin.json
|
||||
commands/sleep.md # /sleep [run|status|adopt|dry-run]
|
||||
commands/sleep-status.md
|
||||
skills/skillopt-sleep/SKILL.md # so Claude knows how to drive the engine
|
||||
hooks/hooks.json # optional: schedule + on-session-end harvest
|
||||
scripts/* # shims that call `python -m skillopt.sleep ...`
|
||||
```
|
||||
|
||||
**Reuse, don't fork:** `consolidate.py` calls into existing
|
||||
`skillopt.optimizer.clip.rank_and_select`, `skillopt.gradient.aggregate.merge_patches`,
|
||||
`skillopt.optimizer.skill.apply_patch_with_report`, and
|
||||
`skillopt.evaluation.gate.evaluate_gate`. The sleep layer is an **EnvAdapter-shaped
|
||||
shim** over the user's own life, not a new optimizer.
|
||||
|
||||
### 5.3 Data flow (one task, end to end)
|
||||
|
||||
```
|
||||
history.jsonl + <session>.jsonl
|
||||
└─harvest→ SessionDigest{prompts, finals, tools, feedback}
|
||||
└─mine→ TaskRecord{intent, attempted, outcome, reference}
|
||||
└─replay(current skill+mem)→ ReplayResult{hard, soft, trajectory}
|
||||
└─reflect→ edits(skill), edits(memory)
|
||||
└─rank/clip(edit_budget)→ candidate
|
||||
└─GATE(replay held-out)→ accept? → staging/ → (adopt) live CLAUDE.md/SKILL.md
|
||||
```
|
||||
|
||||
## 6. Scheduling & triggering
|
||||
|
||||
- **Cron/scheduled:** documented `crontab` line + an optional Claude Code hook; default `0 3 * * *` (3am local; pick an off-:00 minute in practice). The engine is a plain CLI so it works under cron, systemd-timer, or the Claude Code scheduler.
|
||||
- **User-triggered:** `/sleep run` (full cycle), `/sleep dry-run` (harvest+mine+replay, no edits), `/sleep status`, `/sleep adopt`.
|
||||
- **On-session-end harvest (optional hook):** cheaply append the just-finished session to the night's buffer so the 3am run has fresh data without a full rescan.
|
||||
|
||||
## 7. Safety model (hard requirements)
|
||||
|
||||
1. **Never mutate live `CLAUDE.md`/`SKILL.md` except via explicit `adopt`** (or opt-in `auto_adopt`). Default = staged + reviewed (Dreams contract).
|
||||
2. **Backups:** every adopt snapshots the prior file to `staging/<date>/backup/`.
|
||||
3. **Read-only harvest:** transcripts are read, never written.
|
||||
4. **`fresh` replay runs only in throwaway worktrees**, never the user's checkout; no `rm -rf`, no force-push, network off unless `replay.network: true`.
|
||||
5. **Budget cap:** `max_tokens_per_night` + `max_tasks_per_night`; stop early when hit, log what was skipped (no silent truncation).
|
||||
6. **Secret hygiene:** redact obvious secrets from digests before they enter prompts (reuse `_redact_*` ideas from trainer).
|
||||
7. **PII/scope:** only harvest projects on an allowlist (default: the project the plugin is invoked in) or `projects: all` opt-in.
|
||||
|
||||
## 8. Validation experiment — "does it actually improve?"
|
||||
|
||||
A self-contained, **deterministic-by-default** experiment lives in
|
||||
`skillopt/sleep/experiments/` and is the acceptance test for the whole idea.
|
||||
|
||||
**Setup:** a synthetic "user persona" (e.g. *researcher who keeps asking for
|
||||
arXiv-id extraction in a fixed format*, or *programmer who keeps mis-formatting
|
||||
git commit messages*). We ship 12–20 tiny tasks with **exact checkable
|
||||
references**, split into `replay` (train) and `holdout` (test).
|
||||
|
||||
**Procedure:**
|
||||
1. Score the holdout with an **empty** skill+memory → `baseline`.
|
||||
2. Run `N` sleep nights (each: replay train slice → reflect → gated edit).
|
||||
3. Score holdout with the evolved skill+memory → `after`.
|
||||
4. Report `after − baseline`, accept/reject counts, edit count, tokens.
|
||||
|
||||
**Two backends:**
|
||||
- `mock` (default, **no API key, fully deterministic**): a scripted optimizer that proposes the known-good rule on failure and a scripted judge. Proves the *plumbing* (harvest→mine→replay→gate→adopt) monotonically improves the score and the gate blocks regressions. This is the CI-able acceptance test.
|
||||
- `anthropic` (opt-in, uses `ANTHROPIC_API_KEY`): the real optimizer/judge, to demonstrate genuine lift on the persona tasks.
|
||||
|
||||
**Success criteria:**
|
||||
- Mock: `after > baseline`, gate rejects an injected harmful edit, adopt+backup works, re-run is reproducible. (Hard gate in CI.)
|
||||
- Anthropic (when run): `after ≥ baseline` on holdout with ≥1 accepted, human-readable edit; documented in the wake-up report.
|
||||
|
||||
## 9. Personas (the user's framing) → concrete recurring-task families
|
||||
|
||||
- **Programmer:** commit-message conventions, repo-specific build/test commands, "always run X before Y", framework gotchas → consolidated into project `CLAUDE.md` + a `repo-workflow` skill.
|
||||
- **Researcher:** citation/format preferences, experiment-logging habits, paper-section style, dataset-path memory → `research-prefs` skill + memory.
|
||||
- **Finance/analyst:** report formatting, recurring data-pull recipes, terminology → `report-style` skill + memory.
|
||||
The engine is domain-agnostic; the persona only changes which tasks get mined.
|
||||
|
||||
## 10. Phased delivery
|
||||
|
||||
- **Phase 0 — scaffold + types + harvest** (read-only, no API). Provable on this box's real `~/.claude`.
|
||||
- **Phase 1 — mine + replay(mock) + consolidate + gate + staging**, with the **mock** optimizer backend and the deterministic experiment green. *(primary deliverable of the offline session)*
|
||||
- **Phase 2 — plugin surface** (`/sleep`, skill, hooks, plugin.json) wired to the CLI.
|
||||
- **Phase 3 — real Anthropic backend** for miner/reflect/judge + `fresh` replay in worktrees.
|
||||
- **Phase 4 — slow/meta cross-night memory**, adopt automation, multi-project, polish + docs.
|
||||
|
||||
This session targets **Phase 0 + Phase 1 fully**, **Phase 2 scaffolded**, and the
|
||||
**deterministic experiment passing**, all committed (not pushed) for review.
|
||||
|
||||
## 11. Open questions for the user (answer when awake)
|
||||
|
||||
1. **Adopt policy:** keep default *review-gated*, or do you want `auto_adopt` for your own machine?
|
||||
2. **Scope:** harvest only the invoked project, or all projects in `~/.claude/projects`?
|
||||
3. **Real-API demo:** want me to spend live `ANTHROPIC_API_KEY` budget on the persona demo, or keep everything mock until you say go?
|
||||
4. **Skill target:** evolve a *new* dedicated `skillopt-sleep`-managed skill, or also edit your existing hand-written skills in `~/.claude/skills`?
|
||||
5. **Paper:** should this become a section/figure in the SkillOpt arXiv (Dream+Sleep framing as "deployment-time continual skill optimization")?
|
||||
```
|
||||
+2736
File diff suppressed because it is too large
Load Diff
+78
@@ -0,0 +1,78 @@
|
||||
site_name: SkillOpt Documentation
|
||||
site_url: https://microsoft.github.io/SkillOpt
|
||||
site_description: "SkillOpt: Agentic Skill Optimization via Reflective Training Loops"
|
||||
repo_url: https://github.com/microsoft/SkillOpt
|
||||
repo_name: microsoft/SkillOpt
|
||||
|
||||
theme:
|
||||
name: material
|
||||
palette:
|
||||
- scheme: default
|
||||
primary: indigo
|
||||
accent: deep purple
|
||||
toggle:
|
||||
icon: material/brightness-7
|
||||
name: Switch to dark mode
|
||||
- scheme: slate
|
||||
primary: indigo
|
||||
accent: deep purple
|
||||
toggle:
|
||||
icon: material/brightness-4
|
||||
name: Switch to light mode
|
||||
features:
|
||||
- navigation.instant
|
||||
- navigation.tracking
|
||||
- navigation.sections
|
||||
- navigation.expand
|
||||
- navigation.top
|
||||
- content.code.copy
|
||||
- content.tabs.link
|
||||
- search.suggest
|
||||
- search.highlight
|
||||
icon:
|
||||
repo: fontawesome/brands/github
|
||||
font:
|
||||
text: Inter
|
||||
code: JetBrains Mono
|
||||
|
||||
|
||||
|
||||
nav:
|
||||
- Home: index.md
|
||||
- Getting Started:
|
||||
- Installation: guide/installation.md
|
||||
- First Experiment: guide/first-experiment.md
|
||||
- Configuration: guide/configuration.md
|
||||
- Core Concepts:
|
||||
- Training Loop: guide/training-loop.md
|
||||
- Skill Document: guide/skill-document.md
|
||||
- Deep Learning Analogy: guide/dl-analogy.md
|
||||
- Extension Guides:
|
||||
- Add a New Benchmark: guide/new-benchmark.md
|
||||
- Local Environment Smoke Tests: guide/local-env-smoke.md
|
||||
- Add a New Model Backend: guide/new-backend.md
|
||||
- Reference:
|
||||
- Configuration Reference: reference/config.md
|
||||
- CLI Reference: reference/cli.md
|
||||
- API Reference: reference/api.md
|
||||
- Contributing: contributing.md
|
||||
|
||||
markdown_extensions:
|
||||
- admonition
|
||||
- pymdownx.details
|
||||
- pymdownx.superfences
|
||||
- pymdownx.tabbed:
|
||||
alternate_style: true
|
||||
- pymdownx.highlight:
|
||||
anchor_linenums: true
|
||||
- pymdownx.inlinehilite
|
||||
- pymdownx.emoji:
|
||||
emoji_index: !!python/name:material.extensions.emoji.twemoji
|
||||
emoji_generator: !!python/name:material.extensions.emoji.to_svg
|
||||
- attr_list
|
||||
- md_in_html
|
||||
- toc:
|
||||
permalink: true
|
||||
|
||||
plugins:
|
||||
- search
|
||||
@@ -0,0 +1,262 @@
|
||||
# SkillOpt-Sleep — plugins for Claude Code, Codex, Copilot, and Devin
|
||||
|
||||
**Your coding agent forgets everything between sessions. SkillOpt-Sleep fixes
|
||||
that.** While you sleep, it reviews what you did today, notices the rules you
|
||||
keep repeating ("always add a LIMIT", "answers in `\boxed{}`", "cite the
|
||||
source"), and writes them into your agent's long-term memory and skills — but
|
||||
only the rules that actually make it score better on *your own* past tasks. You
|
||||
wake up to an agent that's better at *your* work, and you approve every change
|
||||
before it sticks.
|
||||
|
||||
One engine, four thin shells. It synthesizes **SkillOpt** (validation-gated
|
||||
bounded text optimization — the research in this repo), **Claude Dreams**
|
||||
(offline consolidation; input never mutated; review-then-adopt), and the **agent
|
||||
sleep** idea (short-term experience → long-term competence).
|
||||
|
||||
> **Open-source tool, decoupled from the research.** The engine lives in the
|
||||
> top-level [`skillopt_sleep/`](../skillopt_sleep) package with **zero
|
||||
> dependency** on the paper's `skillopt/` experiment code (the validation gate is
|
||||
> vendored). Use it without the research stack.
|
||||
|
||||
---
|
||||
|
||||
| Platform | Folder | Mechanism | Status |
|
||||
|---|---|---|---|
|
||||
| **Claude Code** | [`claude-code/`](claude-code) | `.claude-plugin` + `/skillopt-sleep` + `/skillopt-sleep-handoff` commands + skill + hooks | full, installable |
|
||||
| **Codex** | [`codex/`](codex) | user-level `skillopt-sleep` skill + shared runner | full |
|
||||
| **Copilot** | [`copilot/`](copilot) | MCP server (`sleep_*` tools) + `copilot-instructions` | full (MCP) |
|
||||
| **Devin** | [`devin/`](devin) | MCP server (`sleep_*` tools) + Devin ATIF-v1.7 harvest + `.devin/rules` | full (MCP) |
|
||||
|
||||
## Install (pick your agent)
|
||||
|
||||
| Platform | Install | Then |
|
||||
|---|---|---|
|
||||
| **Claude Code** | `/plugin marketplace add microsoft/SkillOpt` → `/plugin install skillopt-sleep` | `/skillopt-sleep status` |
|
||||
| **Codex** | `git clone` → `bash plugins/codex/install.sh` | `/skillopt-sleep status` |
|
||||
| **Copilot** | `git clone` → register `plugins/copilot/mcp_server.py` as an MCP server | ask "run the sleep cycle" |
|
||||
| **Devin** | `git clone` → `devin mcp add skillopt-sleep -- python3 plugins/devin/mcp_server.py` | ask "run the sleep cycle" |
|
||||
|
||||
Requirements: Python ≥ 3.10 and the agent's CLI on PATH. All three call the same
|
||||
[`run-sleep.sh`](run-sleep.sh) → `python -m skillopt_sleep`, so behaviour is
|
||||
identical everywhere. Default backend is `mock` (no API spend); `--backend
|
||||
claude|codex|copilot` uses your own budget.
|
||||
|
||||
---
|
||||
|
||||
## How it works: one "night", in plain terms
|
||||
|
||||
```
|
||||
harvest your past sessions → mine the tasks you keep doing → replay them offline
|
||||
→ reflect on failures → propose a few rule edits → KEEP only edits that raise
|
||||
your held-out score → stage a proposal → (you) review & adopt
|
||||
```
|
||||
|
||||
Nothing live changes until you `adopt`; every adopt backs up the prior file.
|
||||
|
||||
### The split that keeps it honest: dream-train / real-val / real-test
|
||||
|
||||
This is the heart of the design, borrowed from the SkillOpt paper's
|
||||
train/selection/test protocol:
|
||||
|
||||
| Split | Where it comes from | What it's for |
|
||||
|---|---|---|
|
||||
| **train** | your real tasks **+ optional "dreamed" variants** | what the optimizer *learns from*. Over-dreaming here is fine — it's imagination. |
|
||||
| **val** (selection) | **your real tasks only**, held out | the **gate**: an edit is kept only if it raises this score. Stops overfitting. |
|
||||
| **test** | **your real tasks only**, held out, never seen during optimization | the **final score** we report. Kept as close to your real usage as possible. |
|
||||
|
||||
So you can **dream up extra training examples** to learn a rule robustly, while
|
||||
the rule is still **judged on real, unseen tasks**. A `dream` task can *never*
|
||||
land in val or test — that invariant is unit-tested.
|
||||
|
||||
---
|
||||
|
||||
## What each feature does **for you** (with examples)
|
||||
|
||||
Every control below works on all three platforms (pass it after the action,
|
||||
e.g. `/skillopt-sleep run --rollouts-k 3`).
|
||||
|
||||
### `--preferences "..."` — tell it your house rules
|
||||
|
||||
The single most useful knob. Free text that steers what the optimizer writes,
|
||||
as a prior. Use it to encode the conventions you're tired of repeating.
|
||||
|
||||
```bash
|
||||
# A backend engineer:
|
||||
/skillopt-sleep run --preferences "Always use async/await, never callbacks. \
|
||||
Prefer pytest over unittest. Commit subjects in imperative mood under 50 chars."
|
||||
|
||||
# A data analyst:
|
||||
/skillopt-sleep run --preferences "Every SQL query must end with LIMIT 1000 unless \
|
||||
I say otherwise. Money in USD with 2 decimals. Prefer CTEs over nested subqueries."
|
||||
|
||||
# A researcher:
|
||||
/skillopt-sleep run --preferences "Cite sources as [Author, Year]. Math answers in \
|
||||
\\boxed{}. Keep explanations under 150 words unless I ask for depth."
|
||||
```
|
||||
*What it does for you:* the next morning your agent already follows these
|
||||
without you re-typing them, and the rules are validated against your real tasks
|
||||
(if a "preference" actually hurts your held-out score, the gate drops it).
|
||||
|
||||
### `--gate on|off` — strict vs. greedy
|
||||
|
||||
- `on` (default): an edit is kept **only if it raises your held-out score**.
|
||||
Safe — blocks plausible-but-wrong rules and reward-hacking.
|
||||
- `off`: greedy — keep edits without the strict check (still reports whether
|
||||
quality moved).
|
||||
|
||||
*What it does for you:* leave it `on` for trust. Flip it `off` when you're
|
||||
exploring and want to see everything the optimizer proposes.
|
||||
|
||||
### `--rollouts-k K` — learn from contrast, not just failure
|
||||
|
||||
Re-runs each task `K` times and learns from the difference between the **good**
|
||||
and **bad** attempts, not just a single failure.
|
||||
|
||||
```bash
|
||||
/skillopt-sleep run --rollouts-k 3
|
||||
```
|
||||
*What it does for you:* a much stronger signal. If your agent gets a task right 1
|
||||
time in 3, the optimizer figures out *what the winning attempt did* and makes it
|
||||
reliable.
|
||||
|
||||
### `--optimizer-model` / `--target-model` — optimize cheap, deploy anywhere
|
||||
|
||||
Use a strong model to *write* the rules and a cheap model to *run* your tasks.
|
||||
The learned skill then helps the cheap model — or any model.
|
||||
|
||||
```bash
|
||||
/skillopt-sleep run --optimizer-model sonnet --target-model haiku
|
||||
```
|
||||
*What it does for you:* spend a little on a smart optimizer overnight; your
|
||||
everyday cheap/fast agent inherits the upgrade. (Verified: a skill optimized on
|
||||
one model lifts a different one — cross-model and even cross-runtime
|
||||
Codex↔Claude.)
|
||||
|
||||
### `--budget-tokens N` / `--budget-minutes M` — cap the spend
|
||||
|
||||
You decide how much the nightly "dreaming" costs; it auto-plans how many nights
|
||||
× how many rollouts fit.
|
||||
|
||||
```bash
|
||||
/skillopt-sleep run --backend claude --budget-tokens 60000
|
||||
```
|
||||
*What it does for you:* predictable cost. It stops cleanly when the budget is hit
|
||||
and tells you what it skipped.
|
||||
|
||||
### multi-objective (accuracy ↑, tokens ↓, latency ↓)
|
||||
|
||||
The reward can weight not just correctness but **cost and speed**, so a skill can
|
||||
learn to be cheaper and faster, not only more accurate. *What it does for you:*
|
||||
"answer directly instead of opening five files" becomes a learned habit.
|
||||
|
||||
### `--backend handoff` — session-executed calls (no API subprocess)
|
||||
|
||||
For subscription seats and environments where the engine shouldn't spawn
|
||||
`claude -p` / API calls itself. The engine still runs every deterministic
|
||||
stage (harvest → mine → replay scoring → gate → stage), but each model call
|
||||
(attempt / judge / reflect) is written to a prompt file that **your own agent
|
||||
session answers between engine runs**:
|
||||
|
||||
```bash
|
||||
python -m skillopt_sleep run --backend handoff --project "$(pwd)"
|
||||
# exit 3 => .skillopt-sleep-handoff/PROMPTS.md + pending.json were written
|
||||
# answer each prompt (each in a FRESH context) into answers/<id>.md
|
||||
# re-run the same command => it resumes from the answers and either
|
||||
# finishes (exit 0) or stages the next prompt batch (exit 3)
|
||||
```
|
||||
|
||||
A typical night converges in 3–6 rounds: baseline attempts → reflect →
|
||||
candidate re-scoring per accepted edit. Resume is stateless — replay is
|
||||
deterministic and answers are cached by prompt hash, so re-running skips
|
||||
everything already answered. Mined tasks are pinned to
|
||||
`.skillopt-sleep-handoff/tasks.json` on the first round, so the sessions that
|
||||
answer the prompts can't shift the task set and invalidate earlier answers.
|
||||
On a completed real run the handoff directory is archived to
|
||||
`.skillopt-sleep-handoff.night<N>.done`.
|
||||
|
||||
On Claude Code, `/skillopt-sleep-handoff run` drives the whole loop for you,
|
||||
answering each prompt in an isolated fresh-context subagent.
|
||||
|
||||
**Integrity rule:** answer every prompt in a fresh context (a subagent with no
|
||||
conversation history). Answering from a session that has already seen the
|
||||
mined tasks and their references contaminates the held-out gate and fakes the
|
||||
improvement score.
|
||||
|
||||
*What it does for you:* the sleep cycle runs entirely on your interactive
|
||||
session's subscription budget — no API key, no headless subprocess — while the
|
||||
gate, splits, and staging discipline stay in the engine.
|
||||
|
||||
Limitations: `--rollouts-k > 1` gives no contrastive spread (identical prompt
|
||||
→ identical answer file), and tool-loop tasks fall back to the single-shot
|
||||
`TOOL_CALL:` marker convention.
|
||||
|
||||
### `schedule` / `unschedule` — set it and forget it
|
||||
|
||||
Built-in nightly scheduling (no manual cron):
|
||||
|
||||
```bash
|
||||
/skillopt-sleep schedule --hour 3 --minute 17 # runs every night for this project
|
||||
/skillopt-sleep unschedule # stop it
|
||||
```
|
||||
*What it does for you:* it just gets better while you sleep. The nightly run only
|
||||
*stages* a proposal — adopting is still your call (or add `--auto-adopt` when you
|
||||
schedule, if you trust it).
|
||||
|
||||
---
|
||||
|
||||
## Full action / flag reference
|
||||
|
||||
| Action | Does |
|
||||
|---|---|
|
||||
| `status` | nights so far + the latest staged proposal (read-only) |
|
||||
| `dry-run` | harvest→mine→replay→report; **stages nothing** |
|
||||
| `run` | full cycle; **stages** a proposal; nothing live changes |
|
||||
| `adopt` | apply the staged proposal to `CLAUDE.md`/`SKILL.md` (backs up first) |
|
||||
| `harvest` | debug: print the recurring tasks it mined |
|
||||
| `schedule` / `unschedule` | install/remove the nightly cron entry |
|
||||
|
||||
| Flag | Default | Meaning |
|
||||
|---|---|---|
|
||||
| `--backend mock\|claude\|codex\|copilot\|handoff` | `mock` | who runs/optimizes (mock = free; handoff = your own session answers) |
|
||||
| `--preferences "..."` | – | your house rules, as a prior |
|
||||
| `--gate on\|off` | `on` | strict held-out gate vs. greedy |
|
||||
| `--rollouts-k K` | `1` | multi-rollout contrastive reflection |
|
||||
| `--optimizer-model` / `--target-model` | – | split the optimizer from the target |
|
||||
| `--budget-tokens` / `--budget-minutes` | – | cap the nightly spend |
|
||||
| `--scope invoked\|all` | `invoked` | this project only, or all projects |
|
||||
| `--auto-adopt` | off | apply without manual review (power users) |
|
||||
|
||||
Deep dive: [the SkillOpt-Sleep guide section](https://microsoft.github.io/SkillOpt/docs/guideline.html#sleep).
|
||||
|
||||
---
|
||||
|
||||
## Does it actually work?
|
||||
|
||||
Yes — measured with **real models on both Claude and Codex**, scored on held-out
|
||||
tasks the optimizer never trained on:
|
||||
|
||||
- **gbrain-evals `skillopt-v1`** (the public suite gbrain scores SkillOpt on):
|
||||
deficient skills go **0.00 → 1.00** on all 4 seeds, including a real tool-use
|
||||
loop; cross-model transfer is positive; the gate blocks regressions.
|
||||
→ [the SkillOpt-Sleep guide section](https://microsoft.github.io/SkillOpt/docs/guideline.html#sleep)
|
||||
- **Academic daily-cases** (math / spreadsheet / search-QA, the paper's 4:1:5
|
||||
split with dream-augmented train): see
|
||||
[the SkillOpt-Sleep guide section](https://microsoft.github.io/SkillOpt/docs/guideline.html#sleep).
|
||||
- **Fresh load-test** (a "SQL must always include LIMIT" analyst, built from
|
||||
scratch): held-out **0.00 → 1.00** on both backends.
|
||||
→ [the SkillOpt-Sleep guide section](https://microsoft.github.io/SkillOpt/docs/guideline.html#sleep)
|
||||
|
||||
Try the deterministic proof yourself (no API key, no spend):
|
||||
```bash
|
||||
python -m skillopt_sleep.experiments.run_experiment --persona researcher --assert-improves
|
||||
```
|
||||
It prints the held-out score rising to 1.0 as the gate accepts the right rules,
|
||||
and confirms the gate **rejects** an injected harmful edit.
|
||||
|
||||
---
|
||||
|
||||
## Safety
|
||||
|
||||
- **Read-only** harvest of your sessions. `mock` replay has no side effects.
|
||||
- Proposals are **staged**, never auto-applied (unless you opt in with `--auto-adopt`).
|
||||
- Every adopt writes a backup. Per-night token/time budget caps. Secrets redacted.
|
||||
@@ -0,0 +1,26 @@
|
||||
{
|
||||
"$schema": "https://anthropic.com/claude-code/marketplace.schema.json",
|
||||
"name": "skillopt-sleep",
|
||||
"description": "SkillOpt-Sleep: give your local Claude agent a nightly sleep cycle that reviews past sessions and consolidates validated memory + skills.",
|
||||
"owner": {
|
||||
"name": "Yifan Yang",
|
||||
"email": "yifanyang@microsoft.com"
|
||||
},
|
||||
"plugins": [
|
||||
{
|
||||
"name": "skillopt-sleep",
|
||||
"description": "Nightly offline self-evolution: harvest your past Claude Code sessions, replay recurring tasks on your own API budget, and consolidate what the agent learns into validated CLAUDE.md memory and SKILL.md skills, behind a held-out gate, staged for your review. Synthesizes SkillOpt (validation-gated skill optimization), Claude Dreams (offline memory consolidation), and agent sleep/consolidation.",
|
||||
"author": {
|
||||
"name": "Yifan Yang"
|
||||
},
|
||||
"category": "productivity",
|
||||
"source": {
|
||||
"source": "git-subdir",
|
||||
"url": "https://github.com/microsoft/SkillOpt.git",
|
||||
"path": "plugins/claude-code",
|
||||
"ref": "main"
|
||||
},
|
||||
"homepage": "https://github.com/microsoft/SkillOpt"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,22 @@
|
||||
{
|
||||
"name": "skillopt-sleep",
|
||||
"description": "Give your local Claude agent a nightly 'sleep cycle': it reviews your past sessions offline, replays recurring tasks on your own API budget, and consolidates what it learns into validated memory (CLAUDE.md) and skills (SKILL.md) so it gets better the more you use it. Synthesizes SkillOpt (validation-gated skill optimization), Claude Dreams (offline memory consolidation), and agent sleep/consolidation.",
|
||||
"version": "0.1.0",
|
||||
"author": {
|
||||
"name": "Yifan Yang",
|
||||
"email": "yifanyang@microsoft.com"
|
||||
},
|
||||
"homepage": "https://github.com/microsoft/SkillOpt",
|
||||
"repository": "https://github.com/microsoft/SkillOpt",
|
||||
"license": "MIT",
|
||||
"keywords": [
|
||||
"skillopt",
|
||||
"self-improvement",
|
||||
"memory-consolidation",
|
||||
"dreams",
|
||||
"sleep",
|
||||
"skills",
|
||||
"continual-learning",
|
||||
"offline-optimization"
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,161 @@
|
||||
# SkillOpt-Sleep (Claude Code plugin)
|
||||
|
||||
> Give your local Claude agent a **sleep cycle**. Every night it reviews your
|
||||
> past sessions offline, replays your recurring tasks on your own API budget,
|
||||
> and consolidates what it learns into **validated** memory (`CLAUDE.md`) and
|
||||
> skills (`SKILL.md`). Your agent gets better the more you use it — no
|
||||
> model-weight training.
|
||||
|
||||
SkillOpt-Sleep is the **deployment-time** companion to
|
||||
[SkillOpt](https://github.com/microsoft/SkillOpt). SkillOpt trains a skill
|
||||
offline on a benchmark; SkillOpt-Sleep applies the same discipline to *your own
|
||||
daily usage*: bounded text edits, accepted only through a held-out validation
|
||||
gate, with rejected edits kept as negative feedback.
|
||||
|
||||
It synthesizes three ideas:
|
||||
|
||||
| Idea | Contribution |
|
||||
|---|---|
|
||||
| **SkillOpt** | skill/memory = trainable text; bounded add/delete/replace edits; **held-out gate** keeps only changes that help. |
|
||||
| **Claude Dreams** | offline consolidation over past sessions; input never mutated; output **reviewed then adopted**. |
|
||||
| **Agent sleep** | periodic offline replay turns short-term episodes into long-term skill. |
|
||||
|
||||
## What it does (one "night")
|
||||
|
||||
```
|
||||
harvest ~/.claude transcripts → mine recurring tasks → replay offline
|
||||
→ consolidate (reflect → bounded edit → GATE) → stage proposal → (you) adopt
|
||||
```
|
||||
|
||||
Nothing live is modified until **you** run `/skillopt-sleep adopt` (the Dreams "review,
|
||||
then adopt or discard" contract). Every adopt backs up the prior file first.
|
||||
|
||||
## Install
|
||||
|
||||
**Requirements:** Python ≥ 3.10, and the `claude` CLI (and/or `codex` CLI) on PATH.
|
||||
|
||||
```bash
|
||||
# 1) get the code (the plugin ships inside the SkillOpt repo)
|
||||
git clone https://github.com/microsoft/SkillOpt.git
|
||||
cd SkillOpt
|
||||
|
||||
# 2) add the plugin to Claude Code as a local marketplace
|
||||
/plugin marketplace add ./skillopt-sleep-plugin
|
||||
/plugin install skillopt-sleep@skillopt-sleep
|
||||
|
||||
# 3) verify
|
||||
/skillopt-sleep status
|
||||
```
|
||||
|
||||
The plugin's bundled runner (`scripts/sleep.sh`) auto-selects a Python ≥ 3.10
|
||||
interpreter and calls the `skillopt_sleep` engine in the repo. No `pip install`
|
||||
is required for the default `mock` backend or for `claude`/`codex` backends —
|
||||
they shell out to the CLIs you already have.
|
||||
|
||||
## Quick start
|
||||
|
||||
```bash
|
||||
# from inside any project you use with Claude Code:
|
||||
/skillopt-sleep dry-run # safe preview: what it would learn, no changes staged
|
||||
/skillopt-sleep run # full cycle: stages a reviewed proposal (still no live edits)
|
||||
/skillopt-sleep status # see history + the latest staged proposal
|
||||
/skillopt-sleep adopt # apply the staged proposal to CLAUDE.md / SKILL.md (with backup)
|
||||
|
||||
/skillopt-sleep-handoff run # same cycle, but THIS session answers the model calls
|
||||
# (no claude -p subprocess, no API key — subscription-friendly)
|
||||
```
|
||||
|
||||
Or call the engine directly (Python ≥ 3.10):
|
||||
|
||||
```bash
|
||||
python -m skillopt_sleep run --project "$(pwd)" --scope invoked --backend mock
|
||||
python -m skillopt_sleep run --project "$(pwd)" --backend claude # real lift via Claude
|
||||
python -m skillopt_sleep run --project "$(pwd)" --backend codex # real lift via Codex
|
||||
```
|
||||
|
||||
Default backend is **`mock`** — deterministic, no API spend — so you can try the
|
||||
plumbing for free. Switch to `--backend claude` or `--backend codex` for genuine
|
||||
improvement on your own budget.
|
||||
|
||||
### Handoff mode (session answers the model calls)
|
||||
|
||||
`--backend handoff` runs the cycle without any model subprocess: the engine
|
||||
executes the deterministic stages and writes every model call it needs to
|
||||
`.skillopt-sleep-handoff/PROMPTS.md` + `pending.json` (exit code 3). You (or
|
||||
the `/skillopt-sleep-handoff` command, which automates the loop with isolated
|
||||
fresh-context subagents) write each raw answer to `answers/<id>.md` and re-run
|
||||
the same command; it resumes from the answers and either finishes or stages
|
||||
the next batch. Typically 3–6 rounds per night.
|
||||
|
||||
```bash
|
||||
python -m skillopt_sleep run --backend handoff --project "$(pwd)"
|
||||
# ... answer .skillopt-sleep-handoff/PROMPTS.md into answers/<id>.md ...
|
||||
python -m skillopt_sleep run --backend handoff --project "$(pwd)" # resume
|
||||
```
|
||||
|
||||
Answer every prompt in a **fresh context** — a session that has already seen
|
||||
the mined tasks and their references would contaminate the held-out gate.
|
||||
Details: [the plugins README](../README.md#--backend-handoff--session-executed-calls-no-api-subprocess).
|
||||
|
||||
## Does it actually improve? (real models, public benchmark)
|
||||
|
||||
SkillOpt-Sleep is validated against [gbrain-evals](https://github.com/garrytan/gbrain-evals)'
|
||||
public `skillopt-v1` suite — the same benchmark gbrain scores its own skill
|
||||
optimizer against. We take a deliberately **deficient** skill and run one sleep
|
||||
night; held-out scoring is done by a local rule judge (no judge-API, no way to
|
||||
grade its own homework).
|
||||
|
||||
| Backend | Seed | Held-out before → after | Nights |
|
||||
|---|---|---|---|
|
||||
| **Claude (Haiku 4.5)** | brief-writer | **0.00 → 1.00** | 1 |
|
||||
| **Codex** | brief-writer | **0.00 → 1.00** | 2 |
|
||||
|
||||
Both took a brief-writer with no risks section / no confidence level and, within
|
||||
1–2 nights, proposed gated edits that lifted the held-out score to perfect —
|
||||
into the protected `LEARNED` block, nothing else touched. The Codex 2-night
|
||||
trace even shows the optimizer **diagnosing its own residual failure** and
|
||||
adding a meta-rule to fix it. Full writeup + reproduction:
|
||||
[the SkillOpt-Sleep guide section](https://microsoft.github.io/SkillOpt/docs/guideline.html#sleep).
|
||||
|
||||
Reproduce:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/garrytan/gbrain-evals /tmp/gbrain-evals
|
||||
python -m skillopt_sleep.experiments.run_gbrain --backend claude --model haiku \
|
||||
--seeds brief-writer --data-root /tmp/gbrain-evals/eval/data/skillopt-v1 \
|
||||
--nights 1 --limit-replay 3 --limit-holdout 3
|
||||
python -m skillopt_sleep.experiments.run_gbrain --backend codex \
|
||||
--seeds brief-writer --data-root /tmp/gbrain-evals/eval/data/skillopt-v1 \
|
||||
--nights 1 --limit-replay 3 --limit-holdout 3
|
||||
```
|
||||
|
||||
## Deterministic proof (no API, no keys)
|
||||
|
||||
```bash
|
||||
python -m skillopt_sleep.experiments.run_experiment --persona researcher --assert-improves
|
||||
python -m skillopt_sleep.experiments.run_experiment --persona programmer --assert-improves
|
||||
```
|
||||
|
||||
Each prints the held-out score rising from baseline toward 1.0 as the gate
|
||||
accepts the general rules your tasks need, and confirms the gate **rejects** an
|
||||
injected harmful edit. Recorded output: [the SkillOpt-Sleep guide section](https://microsoft.github.io/SkillOpt/docs/guideline.html#sleep).
|
||||
|
||||
## Schedule it nightly
|
||||
|
||||
```bash
|
||||
"${CLAUDE_PLUGIN_ROOT}/scripts/install-cron.sh" "$(pwd)" # prints a crontab line; installs nothing
|
||||
```
|
||||
|
||||
## Safety
|
||||
|
||||
- **Read-only** harvest of `~/.claude`. `mock` replay has no side effects.
|
||||
- Proposals are **staged**, never auto-applied (unless you opt in with `--auto-adopt`).
|
||||
- Every adopt writes a backup under the staging dir's `backup/`.
|
||||
- Per-night **token/task budget caps**; secrets redacted from prompts.
|
||||
- `fresh` replay (Phase 3) runs only in throwaway git worktrees.
|
||||
|
||||
## Status
|
||||
|
||||
Phase 1 (engine + deterministic experiment + plugin surface) is complete.
|
||||
Phase 3 adds the real-API miner/judge and `fresh` worktree replay. See
|
||||
[`docs/superpowers/specs/2026-06-07-skillopt-sleep-claude-code-plugin-design.md`](../docs/superpowers/specs/2026-06-07-skillopt-sleep-claude-code-plugin-design.md).
|
||||
@@ -0,0 +1,67 @@
|
||||
---
|
||||
description: Run the SkillOpt-Sleep cycle with the handoff backend — no API subprocess; this session answers the engine's model calls via prompt/answer files, in isolated fresh-context subagents
|
||||
argument-hint: "[run | dry-run] [--preferences \"...\"] (default: run)"
|
||||
allowed-tools: Bash, Read, Write, Task
|
||||
---
|
||||
|
||||
# /skillopt-sleep-handoff — session-executed sleep cycle
|
||||
|
||||
You are driving **SkillOpt-Sleep in handoff mode**: the Python engine runs
|
||||
every deterministic stage (harvest → mine → replay scoring → gate → stage)
|
||||
and outsources each model call (attempt / judge / reflect) to YOU via
|
||||
prompt files. No `claude -p` subprocess, no API key — the model work runs
|
||||
on this session's budget, but each prompt MUST be answered in a fresh,
|
||||
isolated context so the validation gate stays honest.
|
||||
|
||||
## Requested action: $ARGUMENTS
|
||||
|
||||
(If `$ARGUMENTS` is empty, treat it as `run`.)
|
||||
|
||||
## The loop
|
||||
|
||||
Repeat until the engine exits 0 (done) — at most 8 rounds:
|
||||
|
||||
1. **Run the engine** via the bundled runner:
|
||||
|
||||
```bash
|
||||
"${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" <action> --backend handoff --project "$(pwd)" --scope invoked
|
||||
```
|
||||
|
||||
- exit 0 → the night is complete; go to "Finish" below.
|
||||
- exit 3 → pending model calls; continue with step 2.
|
||||
- anything else → stop and show the user the error output.
|
||||
|
||||
2. **Read the batch**: `Read` `.skillopt-sleep-handoff/pending.json` in the
|
||||
project. Each entry has `id`, `prompt`, `max_tokens`, `answer_file`.
|
||||
|
||||
3. **Answer each prompt in ISOLATION** — this is the integrity rule:
|
||||
- For each entry, launch a subagent (Task tool) whose ENTIRE input is
|
||||
the `prompt` text verbatim. Add nothing: no summary of this session,
|
||||
no mention of SkillOpt, no other prompts from the batch.
|
||||
- Take the subagent's reply and `Write` the raw answer text (no
|
||||
commentary, no code fences) to the entry's `answer_file`.
|
||||
- NEVER answer from this session's own context — you have seen the
|
||||
mined tasks and their references, so inline answers would contaminate
|
||||
the held-out gate and fake the improvement score.
|
||||
|
||||
4. **Re-run the same engine command** — it resumes from the answers
|
||||
directory and either finishes or stages the next batch.
|
||||
|
||||
## Finish
|
||||
|
||||
- `Read` the `report.md` in the staging dir the engine printed and show
|
||||
the user: held-out baseline → candidate score, the gate decision, the
|
||||
proposed edits, and where the proposal is staged.
|
||||
- Tell the user nothing live changed; offer `/skillopt-sleep adopt`.
|
||||
- The engine archives `.skillopt-sleep-handoff/` on a completed real run;
|
||||
do not delete it yourself.
|
||||
|
||||
## Safety reminders
|
||||
|
||||
- **Never** edit `CLAUDE.md` or `SKILL.md` yourself — only `adopt` does
|
||||
that, with a backup.
|
||||
- Mined tasks are pinned to `.skillopt-sleep-handoff/tasks.json` on round
|
||||
one, so sessions created while answering prompts cannot shift the task
|
||||
set. Do not edit that file.
|
||||
- If a batch looks like it contains secrets or content the user would not
|
||||
want re-processed, stop and ask before answering.
|
||||
@@ -0,0 +1,66 @@
|
||||
---
|
||||
description: Run or manage the SkillOpt-Sleep self-evolution cycle (review past sessions, replay tasks offline, consolidate validated memory + skills; can also schedule nightly runs)
|
||||
argument-hint: "[run | dry-run | status | adopt | harvest | schedule | unschedule] (default: status)"
|
||||
allowed-tools: Bash, Read
|
||||
---
|
||||
|
||||
# /skillopt-sleep — SkillOpt-Sleep nightly self-evolution
|
||||
|
||||
You are driving **SkillOpt-Sleep**: a tool that lets this user's Claude agent
|
||||
improve offline by reviewing past sessions, replaying recurring tasks, and
|
||||
consolidating what it learns into **validated** memory (`CLAUDE.md`) and skills
|
||||
(`SKILL.md`). It is gated like SkillOpt: a change is kept only if it improves a
|
||||
held-out replay score, and nothing live is modified until the user adopts it.
|
||||
|
||||
## Requested action: $ARGUMENTS
|
||||
|
||||
(If `$ARGUMENTS` is empty, treat it as `status`.)
|
||||
|
||||
## How to run it
|
||||
|
||||
The engine is the `skillopt_sleep` Python package in this repo. Use the
|
||||
**plugin's bundled runner** so the right interpreter and repo are on the path:
|
||||
|
||||
```bash
|
||||
"${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" <action> --project "$(pwd)" --scope invoked
|
||||
```
|
||||
|
||||
`<action>` is one of:
|
||||
|
||||
| action | what it does |
|
||||
|--------------|--------------|
|
||||
| `status` | show how many nights have run + the latest staged proposal (READ-ONLY) |
|
||||
| `dry-run` | harvest → mine → replay → report, but **stage nothing** (safe preview) |
|
||||
| `run` | full cycle: also **stage** a reviewed proposal (still does NOT touch live files) |
|
||||
| `adopt` | apply the latest staged proposal to live `CLAUDE.md` / `SKILL.md` (backs up first) |
|
||||
| `harvest` | debug: print the recurring tasks mined from recent sessions |
|
||||
| `schedule` | install a nightly cron entry for this project (`--hour --minute`, off-:00 by default) |
|
||||
| `unschedule` | remove the nightly cron entry (`--all` to remove every managed entry) |
|
||||
|
||||
Default backend is `mock` (deterministic, no API spend). To use real budget for
|
||||
genuine improvement, add `--backend claude` or `--backend codex`. To steer what
|
||||
the optimizer writes, add `--preferences "<your house rules>"`.
|
||||
|
||||
## Steps to follow
|
||||
|
||||
1. **Run the requested action** via the bundled runner above. Capture stdout.
|
||||
2. **For `run` / `dry-run`:** after it completes, `Read` the generated
|
||||
`report.md` in the staging dir it prints, and show the user:
|
||||
- held-out score: baseline → candidate (the proof it helped)
|
||||
- the gate decision (accept/reject) and the exact edits it proposes
|
||||
- where the proposal is staged
|
||||
3. **For `run` that produced an accepted proposal:** tell the user the diff is
|
||||
staged and that **nothing live changed yet**. Offer to run `/skillopt-sleep adopt`.
|
||||
4. **For `adopt`:** confirm which live files were updated and that backups were
|
||||
written under the staging dir's `backup/`.
|
||||
5. **Never** edit `CLAUDE.md` or `SKILL.md` yourself — only the `adopt` action
|
||||
does that, with a backup. Respect the review gate.
|
||||
|
||||
## Safety reminders
|
||||
|
||||
- Harvest is **read-only** over `~/.claude`. Replay in `mock` mode runs no
|
||||
shell side effects.
|
||||
- The cycle stages proposals; the user is in control of adoption.
|
||||
- If the user asks to schedule this nightly, point them at
|
||||
`${CLAUDE_PLUGIN_ROOT}/scripts/install-cron.sh` (prints a crontab line; does
|
||||
not install anything without confirmation).
|
||||
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"hooks": {
|
||||
"SessionEnd": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "\"${CLAUDE_PLUGIN_ROOT}/hooks/on-session-end.sh\"",
|
||||
"async": true
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
Executable
+18
@@ -0,0 +1,18 @@
|
||||
#!/usr/bin/env bash
|
||||
# SkillOpt-Sleep SessionEnd hook (async, best-effort, NON-BLOCKING).
|
||||
#
|
||||
# This does NOT run the optimizer. It only appends a tiny marker so the next
|
||||
# nightly cycle knows there is fresh activity to harvest, and (optionally)
|
||||
# nudges the user once that a sleep cycle is available. It must never fail the
|
||||
# session or spend API budget.
|
||||
set -uo pipefail
|
||||
|
||||
PLUGIN_ROOT="${CLAUDE_PLUGIN_ROOT:-$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)}"
|
||||
STATE_DIR="${HOME}/.skillopt-sleep"
|
||||
mkdir -p "$STATE_DIR" 2>/dev/null || exit 0
|
||||
|
||||
# Record that a session just ended (cheap; used for "is there new data?").
|
||||
printf '%s\t%s\n' "$(date -u +%Y-%m-%dT%H:%M:%SZ)" "${PWD}" \
|
||||
>> "$STATE_DIR/session-end.log" 2>/dev/null || true
|
||||
|
||||
exit 0
|
||||
Executable
+29
@@ -0,0 +1,29 @@
|
||||
#!/usr/bin/env bash
|
||||
# Print (does NOT install) a crontab line that runs SkillOpt-Sleep nightly.
|
||||
# The user copies the line into `crontab -e` if they want it.
|
||||
set -euo pipefail
|
||||
|
||||
PLUGIN_ROOT="${CLAUDE_PLUGIN_ROOT:-$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)}"
|
||||
RUNNER="$PLUGIN_ROOT/scripts/sleep.sh"
|
||||
PROJECT="${1:-$(pwd)}"
|
||||
BACKEND="${2:-mock}"
|
||||
|
||||
# 3:17am local — deliberately off the :00 mark so many users don't all hit the
|
||||
# API at once (and we leave room for jitter).
|
||||
MIN=17
|
||||
HOUR=3
|
||||
|
||||
cat <<EOF
|
||||
# ── SkillOpt-Sleep nightly cycle ────────────────────────────────────────────
|
||||
# Review past sessions, replay tasks, stage validated memory/skill updates.
|
||||
# Runs at ${HOUR}:$(printf '%02d' $MIN) local every day. Output goes to the project's
|
||||
# .skillopt-sleep/ dir; nothing live is changed until you run '/skillopt-sleep adopt'
|
||||
# (unless you pass --auto-adopt below).
|
||||
#
|
||||
# Copy the next line into 'crontab -e':
|
||||
${MIN} ${HOUR} * * * "${RUNNER}" run --project "${PROJECT}" --scope invoked --backend ${BACKEND} >> "${PROJECT}/.skillopt-sleep/cron.log" 2>&1
|
||||
#
|
||||
# For fully-autonomous adoption (power users), append: --auto-adopt
|
||||
# To spend real API budget for genuine lift, set BACKEND=anthropic above.
|
||||
# ────────────────────────────────────────────────────────────────────────────
|
||||
EOF
|
||||
Executable
+51
@@ -0,0 +1,51 @@
|
||||
#!/usr/bin/env bash
|
||||
# SkillOpt-Sleep shared runner — used by all platform plugins (Claude Code,
|
||||
# Codex, Copilot). Resolves the repo root (which contains the skillopt_sleep
|
||||
# package), picks a Python >= 3.10, and execs the engine CLI.
|
||||
#
|
||||
# Usage: run-sleep.sh <run|dry-run|status|adopt|harvest|...> [args...]
|
||||
set -euo pipefail
|
||||
|
||||
# This script lives at <repo>/plugins/run-sleep.sh, so the repo root (which
|
||||
# holds skillopt_sleep/) is one level up. CLAUDE_PLUGIN_ROOT (if set by Claude
|
||||
# Code) points at the plugin dir; the engine is then two levels above it.
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
if [ -d "$SCRIPT_DIR/../skillopt_sleep" ]; then
|
||||
REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
|
||||
elif [ -n "${CLAUDE_PLUGIN_ROOT:-}" ] && [ -d "$CLAUDE_PLUGIN_ROOT/../../skillopt_sleep" ]; then
|
||||
REPO_ROOT="$(cd "$CLAUDE_PLUGIN_ROOT/../.." && pwd)"
|
||||
elif [ -n "${SKILLOPT_SLEEP_REPO:-}" ] && [ -d "$SKILLOPT_SLEEP_REPO/skillopt_sleep" ]; then
|
||||
REPO_ROOT="$SKILLOPT_SLEEP_REPO"
|
||||
else
|
||||
# last resort: search upward from CWD
|
||||
d="$PWD"
|
||||
while [ "$d" != "/" ]; do
|
||||
[ -d "$d/skillopt_sleep" ] && { REPO_ROOT="$d"; break; }
|
||||
d="$(dirname "$d")"
|
||||
done
|
||||
fi
|
||||
if [ -z "${REPO_ROOT:-}" ]; then
|
||||
echo "[sleep] ERROR: could not locate the skillopt_sleep package. Set SKILLOPT_SLEEP_REPO to the repo root." >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
PY=""
|
||||
# Allow explicit Python override (useful on macOS with old system Python).
|
||||
if [ -n "${SKILLOPT_SLEEP_PYTHON:-}" ]; then
|
||||
PY="$SKILLOPT_SLEEP_PYTHON"
|
||||
else
|
||||
for cand in python3.12 python3.11 python3.10 python3; do
|
||||
if command -v "$cand" >/dev/null 2>&1; then
|
||||
ver="$("$cand" -c 'import sys; print("%d%d" % sys.version_info[:2])' 2>/dev/null || echo 0)"
|
||||
if [ "${ver:-0}" -ge 310 ]; then PY="$cand"; break; fi
|
||||
fi
|
||||
done
|
||||
fi
|
||||
if [ -z "$PY" ]; then
|
||||
echo "[sleep] ERROR: need Python >= 3.10 (found none)." >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ "$#" -eq 0 ]; then set -- status; fi
|
||||
cd "$REPO_ROOT"
|
||||
exec "$PY" -m skillopt_sleep "$@"
|
||||
Executable
+30
@@ -0,0 +1,30 @@
|
||||
#!/usr/bin/env bash
|
||||
# Claude Code plugin runner — thin wrapper over the shared runner so all
|
||||
# platform plugins share one engine launcher.
|
||||
#
|
||||
# After marketplace install the plugin is isolated in a cache directory and
|
||||
# the repo-relative path no longer works. We try four locations:
|
||||
# 1. Co-located run-sleep.sh (bundled copy — works in marketplace cache)
|
||||
# 2. Repo-relative ../../run-sleep.sh (dev checkout)
|
||||
# 3. CLAUDE_PLUGIN_ROOT/../run-sleep.sh (plugin env variable)
|
||||
# 4. SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh (explicit env)
|
||||
set -euo pipefail
|
||||
HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
|
||||
SHARED=""
|
||||
if [ -f "$HERE/run-sleep.sh" ]; then
|
||||
SHARED="$HERE/run-sleep.sh"
|
||||
elif [ -f "$(cd "$HERE/../.." 2>/dev/null && pwd)/run-sleep.sh" ]; then
|
||||
SHARED="$(cd "$HERE/../.." && pwd)/run-sleep.sh"
|
||||
elif [ -n "${CLAUDE_PLUGIN_ROOT:-}" ] && [ -f "$(cd "$CLAUDE_PLUGIN_ROOT/.." 2>/dev/null && pwd)/run-sleep.sh" ]; then
|
||||
SHARED="$(cd "$CLAUDE_PLUGIN_ROOT/.." && pwd)/run-sleep.sh"
|
||||
elif [ -n "${SKILLOPT_SLEEP_REPO:-}" ] && [ -f "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" ]; then
|
||||
SHARED="$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh"
|
||||
fi
|
||||
|
||||
if [ -z "$SHARED" ]; then
|
||||
echo "[sleep] ERROR: cannot locate run-sleep.sh." >&2
|
||||
echo "[sleep] Set SKILLOPT_SLEEP_REPO to the SkillOpt repo root, or pip install skillopt." >&2
|
||||
exit 1
|
||||
fi
|
||||
exec bash "$SHARED" "$@"
|
||||
@@ -0,0 +1,126 @@
|
||||
---
|
||||
name: skillopt-sleep
|
||||
description: "Use when the user wants their Claude agent to self-improve from past usage, asks about a nightly/offline 'sleep' or 'dream' cycle, memory/skill consolidation, or says things like 'make my agent better the more I use it', 'review my past sessions', 'learn my preferences', 'consolidate what you learned', 'run the sleep cycle', or wants to schedule offline self-optimization. Drives the skillopt_sleep engine: harvest past sessions -> mine recurring tasks -> replay offline -> consolidate validated CLAUDE.md/SKILL.md behind a held-out gate."
|
||||
---
|
||||
|
||||
# SkillOpt-Sleep: offline self-evolution for a local Claude agent
|
||||
|
||||
SkillOpt-Sleep gives the user's agent a **sleep cycle**. While the user is
|
||||
offline (e.g. nightly), it reviews their real past Claude Code sessions,
|
||||
re-runs recurring tasks on their own API budget, and consolidates what it
|
||||
learns into **memory** (`CLAUDE.md`) and **skills** (`SKILL.md`) — but only
|
||||
keeps changes that pass a held-out validation gate, and only after the user
|
||||
adopts them. The agent gets measurably better at *this* user's recurring work,
|
||||
with no model-weight training. It is the deployment-time analogue of training:
|
||||
short-term experience → long-term competence.
|
||||
|
||||
It synthesizes three ideas:
|
||||
- **SkillOpt** — the skill/memory doc is trainable text; bounded add/delete/replace
|
||||
edits; accepted only through a held-out gate; rejected edits become negative feedback.
|
||||
- **Claude Dreams** — offline consolidation that reads past sessions and rebuilds
|
||||
memory (dedup/merge/resolve); the input is never mutated; output is reviewed then adopted.
|
||||
- **Agent sleep** — periodic offline replay turns episodes into durable skill.
|
||||
|
||||
## When to use this skill
|
||||
|
||||
Trigger when the user wants any of:
|
||||
- "make my agent learn from how I use it" / "get better the more I use it" / "remember my preferences across sessions"
|
||||
- a nightly/scheduled or on-demand **offline self-improvement / dream / sleep** run
|
||||
- to **review past sessions/trajectories** and distill recurring tasks
|
||||
- to **consolidate** feedback into `CLAUDE.md` or a managed skill
|
||||
- to **schedule** the cycle (cron) or **adopt** a staged proposal
|
||||
|
||||
## The cycle (six stages)
|
||||
|
||||
1. **Harvest** — read `~/.claude/projects/*/<session>.jsonl` + `~/.claude/history.jsonl` (READ-ONLY) → session digests.
|
||||
2. **Mine** — digests → `TaskRecord`s (recurring intents + outcome labels + checkable refs where possible).
|
||||
3. **Replay** — re-run tasks offline under the *current* skill+memory → (hard, soft) scores.
|
||||
4. **Consolidate** — reflect on failures → propose bounded edits → **gate** on a held-out slice; accept only if it strictly improves.
|
||||
5. **Stage** — write `proposed_CLAUDE.md`, `proposed_SKILL.md`, a diff, and `report.md` into `<project>/.skillopt-sleep/staging/<date>/`. **Nothing live changes.**
|
||||
6. **Adopt** — explicit (or opt-in auto): copy staged files over live ones, backing up first.
|
||||
|
||||
## How to drive it
|
||||
|
||||
Prefer the `/skillopt-sleep` command. Under the hood it calls the bundled runner:
|
||||
|
||||
```bash
|
||||
"${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" status # what's happened
|
||||
"${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" dry-run --project "$(pwd)" # safe preview
|
||||
"${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" run --project "$(pwd)" # full cycle, stages a proposal
|
||||
"${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" adopt --project "$(pwd)" # apply staged proposal (with backup)
|
||||
```
|
||||
|
||||
- Default backend is `mock` (deterministic, **no API spend**) — good for trying the plumbing.
|
||||
- Add `--backend claude` or `--backend codex` to spend the user's real budget for genuine improvement.
|
||||
- Scope defaults to the invoked project; `--scope all` harvests every project.
|
||||
|
||||
### Scheduling
|
||||
|
||||
```bash
|
||||
"${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" schedule --project "$(pwd)" --hour 3 --minute 17
|
||||
"${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" unschedule --project "$(pwd)"
|
||||
```
|
||||
|
||||
Installs a nightly cron entry. `unschedule --all` removes every managed entry.
|
||||
|
||||
## All CLI flags
|
||||
|
||||
| Flag | Default | Description |
|
||||
|------|---------|-------------|
|
||||
| `--project PATH` | cwd | Project directory to evolve |
|
||||
| `--scope all\|invoked` | invoked | Harvest scope |
|
||||
| `--backend mock\|claude\|codex\|copilot` | mock | Replay backend (mock = no API spend) |
|
||||
| `--model NAME` | backend default | Override the model used for replay |
|
||||
| `--source claude\|codex\|auto` | claude | Transcript source |
|
||||
| `--lookback-hours N` | 72 | Harvest window |
|
||||
| `--max-sessions N` | unlimited | Cap harvested sessions |
|
||||
| `--max-tasks N` | 40 | Cap mined tasks |
|
||||
| `--target-skill-path PATH` | auto | Explicit SKILL.md to evolve |
|
||||
| `--tasks-file PATH` | — | Reviewed TaskRecord JSON (skip harvest) |
|
||||
| `--progress` | off | Print phase progress to stderr |
|
||||
| `--auto-adopt` | off | Auto-adopt if gate passes |
|
||||
| `--edit-budget N` | 4 | Max bounded edits per night |
|
||||
| `--json` | off | Machine-readable JSON output |
|
||||
|
||||
## Config keys (`~/.skillopt-sleep/config.json`)
|
||||
|
||||
Beyond the CLI flags, advanced behavior is controlled via config:
|
||||
|
||||
- **`preferences`** — free-text house rules injected into the optimizer's reflect step (e.g. "Always use async/await", "Answers in `\boxed{}`").
|
||||
- **`gate_mode`** — `on` (default, validation-gated) or `off` (greedy, accept all edits).
|
||||
- **`gate_metric`** — `hard`, `soft`, or `mixed` (default). Controls how the held-out gate scores.
|
||||
- **`dream_rollouts`** — >1 enables multi-rollout contrastive reflection per task.
|
||||
- **`recall_k`** — >0 recalls K similar past tasks into the dream (long-term memory).
|
||||
- **`evolve_memory`** / **`evolve_skill`** — independently toggle CLAUDE.md vs SKILL.md consolidation.
|
||||
|
||||
## Memory consolidation
|
||||
|
||||
The sleep cycle can consolidate both:
|
||||
- **SKILL.md** — the managed skill file (bounded edits: add/delete/replace)
|
||||
- **CLAUDE.md** — the project memory (same bounded edits)
|
||||
|
||||
Both are gated by the same held-out validation score. Set `evolve_memory: false` to consolidate only skills, or `evolve_skill: false` for only memory.
|
||||
|
||||
## Hard rules
|
||||
|
||||
- **Never** hand-edit the user's `CLAUDE.md` / `SKILL.md` as part of this skill.
|
||||
Only the `adopt` action changes live files, and it backs them up first.
|
||||
- Harvest is read-only. `mock` replay has no side effects.
|
||||
- Always show the user the **held-out baseline → candidate** score and the
|
||||
exact proposed edits before suggesting adoption. Evidence before adoption.
|
||||
- If asked whether it really helps, run
|
||||
`python -m skillopt_sleep.experiments.run_experiment --persona researcher --json`
|
||||
— a deterministic demo that proves held-out lift and that the gate blocks
|
||||
harmful edits.
|
||||
|
||||
## Validate / demo
|
||||
|
||||
```bash
|
||||
# deterministic proof (no API): held-out score rises, gate blocks regressions
|
||||
python -m skillopt_sleep.experiments.run_experiment --persona researcher --assert-improves
|
||||
python -m skillopt_sleep.experiments.run_experiment --persona programmer --assert-improves
|
||||
```
|
||||
|
||||
See the SkillOpt-Sleep guide section for recorded output and
|
||||
`docs/superpowers/specs/2026-06-07-skillopt-sleep-claude-code-plugin-design.md`
|
||||
for the full design.
|
||||
@@ -0,0 +1,96 @@
|
||||
# SkillOpt-Sleep — Codex integration
|
||||
|
||||
Give your **Codex** agent a nightly **sleep cycle**: it reviews past sessions
|
||||
offline, replays your recurring tasks on your own Codex budget, and consolidates
|
||||
what it learns into validated memory + skills behind a held-out gate. Same engine
|
||||
as the Claude Code plugin (`skillopt_sleep`), wrapped for Codex.
|
||||
|
||||
> **Verified on Codex:** on the public
|
||||
> [gbrain-evals](https://github.com/garrytan/gbrain-evals) `skillopt-v1`
|
||||
> benchmark, a deliberately deficient skill goes **0.00 → 1.00** on a held-out
|
||||
> set with the Codex backend (incl. the tool-use seed via a real tool loop).
|
||||
> See [the SkillOpt-Sleep guide section](https://microsoft.github.io/SkillOpt/docs/guideline.html#sleep).
|
||||
|
||||
## What Codex supports (and what we use)
|
||||
|
||||
Codex (`@openai/codex`) extends via **`AGENTS.md`** instructions, **skills** at
|
||||
`~/.agents/skills/<name>/SKILL.md`, and plugins that can distribute skills.
|
||||
Custom prompts are deprecated in Codex, so this integration is skill-first: the
|
||||
installed `skillopt-sleep` skill contains the launch commands and operating
|
||||
rules. The shared runner remains a plain shell entrypoint that the skill calls.
|
||||
|
||||
## Install
|
||||
|
||||
```bash
|
||||
git clone <repo-url> SkillOpt-Sleep
|
||||
cd SkillOpt-Sleep
|
||||
bash plugins/codex/install.sh # installs the skill
|
||||
export SKILLOPT_SLEEP_REPO="$(pwd)" # so the runner is found from anywhere
|
||||
```
|
||||
|
||||
If a previous install created `~/.codex/prompts/sleep.md`, the installer moves
|
||||
that deprecated prompt aside with a `.skillopt-legacy*.bak` suffix.
|
||||
|
||||
Requires Python ≥ 3.10 and the `codex` CLI on PATH.
|
||||
|
||||
## Use
|
||||
|
||||
Mention `$skillopt-sleep` where Codex supports explicit skill mentions, or ask
|
||||
Codex in natural language:
|
||||
|
||||
```text
|
||||
Use the skillopt-sleep skill to run status for this project.
|
||||
Use the skillopt-sleep skill to run a dry-run for this project.
|
||||
Use the skillopt-sleep skill to run the full cycle for this project with the Codex backend.
|
||||
Use the skillopt-sleep skill to adopt the latest staged proposal.
|
||||
```
|
||||
|
||||
Or call the engine directly:
|
||||
|
||||
```bash
|
||||
python -m skillopt_sleep dry-run --project "$(pwd)" --source codex --backend mock
|
||||
python -m skillopt_sleep run --project "$(pwd)" --source codex --backend codex \
|
||||
--max-sessions 5 --max-tasks 3 --progress
|
||||
python -m skillopt_sleep run --project "$(pwd)" --source codex --backend codex \
|
||||
--target-skill-path .agents/skills/example/SKILL.md \
|
||||
--max-sessions 5 --max-tasks 3 --progress
|
||||
```
|
||||
|
||||
`--source codex` reads Codex Desktop archived sessions from
|
||||
`~/.codex/archived_sessions`. Use `--codex-home /path/to/.codex` to point at a
|
||||
different Codex home, or `--source auto` to try Codex archives first and fall
|
||||
back to Claude Code transcripts. Default backend is `mock` (no API spend).
|
||||
`--backend codex` uses your Codex budget for real improvement. Bound live runs
|
||||
with `--max-sessions` and `--max-tasks`; add `--progress` because Codex-backed
|
||||
mining, replay, and reflection can be slow and otherwise quiet. Use
|
||||
`--target-skill-path` to stage/adopt into a repo-scoped Codex skill such as
|
||||
`.agents/skills/<name>/SKILL.md`; target runs over-sample mined tasks and
|
||||
prefer tasks that match the target skill's path, headings, and content. All the
|
||||
controllable knobs (`--gate on|off`, `--rollouts-k`, `--budget-tokens`,
|
||||
`--preferences`, optimizer/target split) work identically — see
|
||||
[the SkillOpt-Sleep guide section](https://microsoft.github.io/SkillOpt/docs/guideline.html#sleep).
|
||||
|
||||
For privacy-sensitive projects, split the run into reviewable steps:
|
||||
|
||||
```bash
|
||||
python -m skillopt_sleep harvest --project "$(pwd)" --source codex \
|
||||
--target-skill-path .agents/skills/example/SKILL.md \
|
||||
--max-sessions 5 --max-tasks 3 \
|
||||
--output reviewed-tasks.json
|
||||
|
||||
python -m skillopt_sleep dry-run --project "$(pwd)" --backend codex \
|
||||
--tasks-file reviewed-tasks.json --progress --json
|
||||
```
|
||||
|
||||
Inspect/redact the JSON and set `"reviewed": true` before using a real backend.
|
||||
`--tasks-file` skips archive harvest/mining and replays only the reviewed JSON
|
||||
tasks; real backends refuse task files still marked `"reviewed": false`.
|
||||
|
||||
## Notes / status
|
||||
|
||||
- Codex's `exec` runs shell, so the real-tool-loop replay (e.g. the
|
||||
`tool_called: search` benchmark seed) works natively.
|
||||
- This integration no longer installs a `.codex/prompts` slash command. Skills
|
||||
are the reusable Codex workflow surface; mention `skillopt-sleep` explicitly
|
||||
or ask for a sleep/dream/offline self-improvement run and Codex can load the
|
||||
skill.
|
||||
Executable
+44
@@ -0,0 +1,44 @@
|
||||
#!/usr/bin/env bash
|
||||
# Install the SkillOpt-Sleep Codex integration as a user-level Codex skill.
|
||||
# Idempotent; prints what it does.
|
||||
set -euo pipefail
|
||||
|
||||
REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
|
||||
CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
|
||||
AGENTS_SKILLS="${HOME}/.agents/skills"
|
||||
LEGACY_PROMPT="$CODEX_HOME/prompts/sleep.md"
|
||||
|
||||
echo "[install] repo: $REPO_ROOT"
|
||||
|
||||
# 1) user-level skill
|
||||
mkdir -p "$AGENTS_SKILLS/skillopt-sleep"
|
||||
cp "$REPO_ROOT/plugins/codex/skills/skillopt-sleep/SKILL.md" "$AGENTS_SKILLS/skillopt-sleep/SKILL.md"
|
||||
echo "[install] skill -> $AGENTS_SKILLS/skillopt-sleep/SKILL.md"
|
||||
|
||||
# 2) retire the old custom prompt entrypoint from previous installs
|
||||
if [ -f "$LEGACY_PROMPT" ]; then
|
||||
backup="${LEGACY_PROMPT}.skillopt-legacy.bak"
|
||||
if [ -e "$backup" ]; then
|
||||
backup="${LEGACY_PROMPT}.skillopt-legacy.$(date +%Y%m%d%H%M%S).bak"
|
||||
fi
|
||||
mv "$LEGACY_PROMPT" "$backup"
|
||||
echo "[install] legacy prompt -> $backup"
|
||||
fi
|
||||
|
||||
# 3) record the repo location so the runner is found from anywhere
|
||||
echo "[install] add to your shell profile:"
|
||||
echo " export SKILLOPT_SLEEP_REPO=\"$REPO_ROOT\""
|
||||
|
||||
# 4) optional: append an AGENTS.md hint (only if the user opts in)
|
||||
cat <<EOF
|
||||
|
||||
[install] Optional — add this to ~/.codex/AGENTS.md so Codex always knows the tool:
|
||||
|
||||
## SkillOpt-Sleep
|
||||
Use the skillopt-sleep skill when I ask to run a sleep/dream/offline
|
||||
self-improvement cycle. The runner is:
|
||||
\`bash "$REPO_ROOT/plugins/run-sleep.sh" status --project "\$(pwd)"\`.
|
||||
|
||||
Done. Try asking Codex:
|
||||
Use the skillopt-sleep skill to run status for this project.
|
||||
EOF
|
||||
@@ -0,0 +1,132 @@
|
||||
---
|
||||
name: skillopt-sleep
|
||||
description: "Use when the user wants Codex to self-improve from past usage, asks about a nightly/offline 'sleep' or 'dream' cycle, wants Codex to review past sessions, learn preferences, consolidate memory/skills, run dry-run/run/adopt/status for SkillOpt-Sleep, or schedule offline self-optimization. Drives the skillopt_sleep engine: harvest past sessions -> mine recurring tasks -> replay offline -> consolidate validated memory + skills behind a held-out gate."
|
||||
---
|
||||
|
||||
# SkillOpt-Sleep: offline self-evolution for a local Codex agent
|
||||
|
||||
SkillOpt-Sleep gives the user's Codex agent a sleep cycle. While the user is
|
||||
offline or on demand, it reviews past local sessions, re-runs recurring tasks
|
||||
on the user's own budget, and consolidates what it learns into memory and
|
||||
skills. It keeps only changes that pass a held-out validation gate, and live
|
||||
files change only after the user explicitly adopts a staged proposal. There is
|
||||
no model-weight training.
|
||||
|
||||
## When to use
|
||||
|
||||
Trigger when the user wants any of:
|
||||
|
||||
- Codex to learn from past sessions or get better the more they use it;
|
||||
- a nightly/scheduled or on-demand sleep/dream/offline self-improvement run;
|
||||
- to review past sessions and distill recurring tasks;
|
||||
- to consolidate feedback into memory or managed skills;
|
||||
- to run `status`, `harvest`, `dry-run`, `run`, or `adopt` for SkillOpt-Sleep.
|
||||
|
||||
## The cycle
|
||||
|
||||
1. **Harvest** - read local session transcripts according to the engine
|
||||
configuration and normalize them into session digests.
|
||||
2. **Mine** - turn digests into recurring `TaskRecord`s with outcomes and
|
||||
checkable references where possible.
|
||||
3. **Replay** - re-run mined tasks offline under the current skill and memory.
|
||||
4. **Consolidate** - reflect on failures and propose bounded edits.
|
||||
5. **Gate** - accept edits only when the held-out validation score improves.
|
||||
6. **Stage** - write the proposal under
|
||||
`<project>/.skillopt-sleep/staging/<date>/`; nothing live changes.
|
||||
7. **Adopt** - only after explicit user approval, copy staged files over live
|
||||
files with backups.
|
||||
|
||||
## How to drive it
|
||||
|
||||
Invoke the bundled runner via shell (Codex `exec` has shell access). The runner
|
||||
finds the engine and a Python >= 3.10 automatically.
|
||||
|
||||
```bash
|
||||
# point at the repo if it isn't auto-detected from CWD:
|
||||
export SKILLOPT_SLEEP_REPO=/path/to/SkillOpt-Sleep
|
||||
bash "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" status --project "$(pwd)"
|
||||
bash "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" harvest --project "$(pwd)"
|
||||
bash "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" dry-run --project "$(pwd)" --backend mock
|
||||
bash "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" run --project "$(pwd)" --backend codex
|
||||
bash "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" run --project "$(pwd)" --source codex # harvest from Codex Desktop
|
||||
bash "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" adopt --project "$(pwd)"
|
||||
```
|
||||
|
||||
Actions are `status`, `harvest`, `dry-run`, `run`, `adopt`, `schedule`, and `unschedule`.
|
||||
|
||||
- Default backend is `mock`, which is deterministic and spends no API budget.
|
||||
- `--backend codex` uses the user's Codex budget for real improvement.
|
||||
- `--source codex` reads Codex Desktop archived sessions from `~/.codex/archived_sessions`;
|
||||
use `--codex-home /path/to/.codex` if the archive lives elsewhere.
|
||||
- Keep `dry-run --backend mock` as the first smoke check unless the user
|
||||
explicitly asked for a real optimization run.
|
||||
|
||||
### Scheduling
|
||||
|
||||
```bash
|
||||
bash "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" schedule --project "$(pwd)" --hour 3 --minute 17
|
||||
bash "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" unschedule --project "$(pwd)"
|
||||
```
|
||||
|
||||
Installs a nightly cron entry. `unschedule --all` removes every managed entry.
|
||||
|
||||
### All backends
|
||||
|
||||
- `--backend mock` — deterministic, no API spend (default)
|
||||
- `--backend claude` — uses the Claude CLI
|
||||
- `--backend codex` — uses the Codex CLI
|
||||
- `--backend copilot` — uses the GitHub Copilot CLI
|
||||
|
||||
### Additional flags
|
||||
|
||||
| Flag | Description |
|
||||
|------|-------------|
|
||||
| `--auto-adopt` | Auto-adopt if the gate passes (default: stage only) |
|
||||
| `--edit-budget N` | Max bounded edits per night (default: 4) |
|
||||
| `--lookback-hours N` | Harvest window in hours (default: 72) |
|
||||
| `--json` | Machine-readable JSON output |
|
||||
|
||||
### Config keys (`~/.skillopt-sleep/config.json`)
|
||||
|
||||
- **`preferences`** — free-text house rules for the optimizer
|
||||
- **`gate_mode`** — `on` (validation-gated, default) or `off` (greedy)
|
||||
- **`gate_metric`** — `hard` | `soft` | `mixed` (default)
|
||||
- **`dream_rollouts`** — >1 for multi-rollout contrastive reflection
|
||||
- **`recall_k`** — >0 recalls similar past tasks from the archive
|
||||
|
||||
### Memory consolidation
|
||||
|
||||
The sleep cycle consolidates both **memory** (AGENTS.md / CLAUDE.md) and **skills** (SKILL.md) by default. Each is independently toggleable via `evolve_memory` / `evolve_skill` config keys. Both are gated by the same held-out validation score.
|
||||
|
||||
## Steps
|
||||
|
||||
1. Run the requested action; capture stdout.
|
||||
2. For `dry-run` and `run`, report the held-out baseline -> candidate score,
|
||||
gate action, task count, session count, and exact proposed edits.
|
||||
3. If a staging directory is printed, read `report.md` before summarizing.
|
||||
4. `run` only stages a proposal; nothing live changes until `adopt`.
|
||||
5. Offer adoption only after the user has reviewed the staged proposal.
|
||||
6. Never hand-edit the user's `AGENTS.md`, memory, or skills as a substitute
|
||||
for `adopt`; adoption is the safety boundary and writes backups first.
|
||||
|
||||
## Hard rules
|
||||
|
||||
- Harvest is read-only. Do not edit archived sessions or raw transcripts.
|
||||
- Keep raw secrets, credentials, private user data, and unsanitized transcript
|
||||
contents out of messages, logs, generated artifacts, and commits.
|
||||
- Show validation evidence before recommending adoption.
|
||||
- Treat generated edits as proposals, not as source of truth.
|
||||
- Do not rely on deprecated custom prompts or `/sleep` slash commands for this
|
||||
Codex integration. This skill is the entrypoint.
|
||||
|
||||
## Validate
|
||||
|
||||
```bash
|
||||
python -m skillopt_sleep dry-run --project "$(pwd)" --backend mock --json
|
||||
python -m skillopt_sleep.experiments.run_gbrain --backend codex \
|
||||
--seeds brief-writer --data-root /path/to/gbrain-evals/eval/data/skillopt-v1 \
|
||||
--nights 2 --limit-replay 3 --limit-holdout 3
|
||||
```
|
||||
|
||||
A deficient skill goes 0.00 -> 1.00 on a held-out set; the optimizer's edits
|
||||
are gated on real-task performance.
|
||||
@@ -0,0 +1,76 @@
|
||||
# SkillOpt-Sleep — GitHub Copilot integration
|
||||
|
||||
Give **Copilot** (CLI or VS Code) a nightly **sleep cycle** via a tiny **MCP
|
||||
server** that exposes the `skillopt_sleep` engine as tools. MCP is GitHub's
|
||||
supported way to extend Copilot, so this works across Copilot CLI, VS Code, and
|
||||
other MCP clients with the same server.
|
||||
|
||||
## What's here
|
||||
|
||||
| File | Purpose |
|
||||
|---|---|
|
||||
| `mcp_server.py` | stdlib-only MCP (stdio) server exposing `sleep_*` tools |
|
||||
| `mcp-config.example.json` | drop-in MCP server config |
|
||||
| `copilot-instructions.snippet.md` | paste into `.github/copilot-instructions.md` |
|
||||
|
||||
## Install
|
||||
|
||||
Requires Python ≥ 3.10. No third-party packages — the server is pure stdlib.
|
||||
|
||||
1. **Register the MCP server.** Add the server to your Copilot MCP config
|
||||
(Copilot CLI: `~/.copilot/mcp-config.json`; VS Code: your MCP settings).
|
||||
Use `mcp-config.example.json` as a template — set `SKILLOPT_SLEEP_REPO` to
|
||||
this repo's path:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"skillopt-sleep": {
|
||||
"command": "python3",
|
||||
"args": ["/abs/path/SkillOpt-Sleep/plugins/copilot/mcp_server.py"],
|
||||
"env": { "SKILLOPT_SLEEP_REPO": "/abs/path/SkillOpt-Sleep" }
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
2. **(Optional) Tell Copilot about it.** Append
|
||||
`copilot-instructions.snippet.md` to your repo's
|
||||
`.github/copilot-instructions.md` so Copilot reaches for the tools when the
|
||||
user asks to "run the sleep cycle".
|
||||
|
||||
## Use
|
||||
|
||||
Ask Copilot things like *"run the sleep cycle"*, *"what did the last sleep
|
||||
propose?"*, *"adopt the staged sleep proposal"*. Copilot calls the MCP tools:
|
||||
`sleep_status`, `sleep_dry_run`, `sleep_run`, `sleep_adopt`, `sleep_harvest`.
|
||||
|
||||
Each tool takes optional `project`, `backend` (`mock`/`claude`/`codex`/`copilot`), and
|
||||
`scope` arguments. Default backend is `mock` (no API spend). The `copilot`
|
||||
backend drives the GitHub Copilot CLI (`copilot -p ... --output-format json`)
|
||||
and requires the `copilot` CLI to be installed and authenticated.
|
||||
|
||||
For speed, the `copilot` backend runs each call against an isolated
|
||||
`COPILOT_HOME` with built-in MCP servers and custom instructions disabled, so
|
||||
your user MCP servers (including this project's own) are not spawned per call
|
||||
(~5x faster). Override with `SKILLOPT_SLEEP_COPILOT_HOME=<dir>`, pick a model
|
||||
with `SKILLOPT_SLEEP_COPILOT_MODEL`, or set `SKILLOPT_SLEEP_COPILOT_FULL_ENV=1`
|
||||
to use your real Copilot environment instead.
|
||||
|
||||
## Verify the server directly (no Copilot needed)
|
||||
|
||||
```bash
|
||||
printf '%s\n' \
|
||||
'{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}' \
|
||||
'{"jsonrpc":"2.0","id":2,"method":"tools/list"}' \
|
||||
| SKILLOPT_SLEEP_REPO="$(pwd)" python3 plugins/copilot/mcp_server.py
|
||||
```
|
||||
You should see the server info and the five `sleep_*` tools.
|
||||
|
||||
## Notes / status
|
||||
|
||||
- MCP is the stable, official Copilot extension surface, so this is the most
|
||||
portable of the three integrations (one server → CLI + IDE).
|
||||
- The engine and all its controls (gate on/off, multi-rollout, budget,
|
||||
preferences, optimizer/target split) are identical across platforms — see
|
||||
[the SkillOpt-Sleep guide section](https://microsoft.github.io/SkillOpt/docs/guideline.html#sleep).
|
||||
@@ -0,0 +1,43 @@
|
||||
<!--
|
||||
Copy this block into your repo's .github/copilot-instructions.md so Copilot
|
||||
knows the SkillOpt-Sleep tools exist. (Copilot reads copilot-instructions.md
|
||||
automatically as ambient guidance.)
|
||||
-->
|
||||
|
||||
## SkillOpt-Sleep (offline self-evolution)
|
||||
|
||||
This project has SkillOpt-Sleep available via an MCP server (`skillopt-sleep`).
|
||||
It gives the agent a nightly "sleep cycle": it reviews past sessions, replays
|
||||
recurring tasks offline, and consolidates validated memory + skills behind a
|
||||
held-out gate.
|
||||
|
||||
When the user asks to "run the sleep cycle", "review my past sessions", "learn
|
||||
my preferences", or "make the agent improve from past usage", use the MCP tools:
|
||||
|
||||
- `sleep_status` — what's happened + the latest staged proposal
|
||||
- `sleep_dry_run` — safe preview, stages nothing
|
||||
- `sleep_run` — full cycle, stages a reviewed proposal (nothing live changes)
|
||||
- `sleep_adopt` — apply the staged proposal (backs up first)
|
||||
- `sleep_harvest` — list mined recurring tasks
|
||||
- `sleep_schedule` — install a nightly cron entry (set `hour`/`minute`)
|
||||
- `sleep_unschedule` — remove the nightly cron entry
|
||||
|
||||
### Key parameters (pass as MCP tool arguments)
|
||||
|
||||
- `backend` — `mock` (default, free), `claude`, `codex`, or `copilot`
|
||||
- `source` — `claude`, `codex`, or `auto` (where to read transcripts)
|
||||
- `target_skill_path` — explicit SKILL.md to evolve
|
||||
- `tasks_file` — pre-built TaskRecord JSON (skip harvest)
|
||||
- `max_tasks` / `max_sessions` — cap workload
|
||||
- `auto_adopt` — auto-adopt if the gate passes
|
||||
- `json` — machine-readable output for programmatic use
|
||||
|
||||
### Advanced config (`~/.skillopt-sleep/config.json`)
|
||||
|
||||
- `preferences` — free-text house rules for the optimizer
|
||||
- `gate_mode` — `on` (default) or `off`; `dream_rollouts` — >1 for more signal
|
||||
- `evolve_memory` / `evolve_skill` — toggle which docs consolidate
|
||||
|
||||
Always show the user the held-out baseline → candidate score and the proposed
|
||||
edits before suggesting `sleep_adopt`. Never hand-edit the user's memory/skill
|
||||
files; only `sleep_adopt` does that, with a backup.
|
||||
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"mcpServers": {
|
||||
"skillopt-sleep": {
|
||||
"command": "python3",
|
||||
"args": ["plugins/copilot/mcp_server.py"],
|
||||
"env": {
|
||||
"SKILLOPT_SLEEP_REPO": "${workspaceFolder}"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
Executable
+180
@@ -0,0 +1,180 @@
|
||||
#!/usr/bin/env python3
|
||||
"""SkillOpt-Sleep — minimal MCP server (stdio, stdlib-only).
|
||||
|
||||
Exposes the sleep engine as MCP tools so any MCP-capable client (GitHub Copilot
|
||||
CLI / VS Code, Claude Desktop, etc.) can drive it. No third-party deps: speaks
|
||||
JSON-RPC 2.0 over stdio with just the handful of MCP methods clients need.
|
||||
|
||||
Tools exposed:
|
||||
- sleep_status : how many nights have run + the latest staged proposal
|
||||
- sleep_dry_run : harvest+mine+replay, report only (no staging)
|
||||
- sleep_run : full cycle, stages a proposal (nothing live changes)
|
||||
- sleep_adopt : apply the latest staged proposal (with backup)
|
||||
- sleep_harvest : debug — list mined recurring tasks
|
||||
|
||||
Each tool shells out to `python -m skillopt_sleep <action> ...` and returns its
|
||||
stdout. Configure your client to launch: python plugins/copilot/mcp_server.py
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
REPO_ROOT = os.environ.get("SKILLOPT_SLEEP_REPO") or os.path.abspath(
|
||||
os.path.join(os.path.dirname(__file__), "..", "..")
|
||||
)
|
||||
PROTOCOL_VERSION = "2024-11-05"
|
||||
|
||||
TOOLS = [
|
||||
{"name": "sleep_status", "action": "status",
|
||||
"description": "Show how many SkillOpt-Sleep nights have run and the latest staged proposal."},
|
||||
{"name": "sleep_dry_run", "action": "dry-run",
|
||||
"description": "Preview a sleep cycle (harvest+mine+replay) without staging anything."},
|
||||
{"name": "sleep_run", "action": "run",
|
||||
"description": "Run a full sleep cycle; stages a reviewed proposal. Nothing live changes until adopt."},
|
||||
{"name": "sleep_adopt", "action": "adopt",
|
||||
"description": "Apply the latest staged proposal to CLAUDE.md/SKILL.md (backs up first)."},
|
||||
{"name": "sleep_harvest", "action": "harvest",
|
||||
"description": "Debug: list the recurring tasks mined from recent sessions."},
|
||||
{"name": "sleep_schedule", "action": "schedule",
|
||||
"description": "Install a nightly cron entry to run the sleep cycle automatically."},
|
||||
{"name": "sleep_unschedule", "action": "unschedule",
|
||||
"description": "Remove the nightly cron entry for a project."},
|
||||
]
|
||||
_BY_NAME = {t["name"]: t for t in TOOLS}
|
||||
|
||||
_TOOL_SCHEMA = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"project": {"type": "string",
|
||||
"description": "Project dir to evolve (default: cwd)."},
|
||||
"backend": {"type": "string", "enum": ["mock", "claude", "codex", "copilot"],
|
||||
"description": "mock = no API spend (default); claude/codex/copilot = real."},
|
||||
"scope": {"type": "string", "enum": ["invoked", "all"],
|
||||
"description": "Harvest scope (default: invoked project only)."},
|
||||
"source": {"type": "string", "enum": ["claude", "codex", "auto"],
|
||||
"description": "Transcript source (default: claude)."},
|
||||
"model": {"type": "string",
|
||||
"description": "Backend-specific model override."},
|
||||
"tasks_file": {"type": "string",
|
||||
"description": "Path to reviewed TaskRecord JSON (skips harvest)."},
|
||||
"target_skill_path": {"type": "string",
|
||||
"description": "Explicit SKILL.md path to evolve/stage/adopt."},
|
||||
"progress": {"type": "boolean",
|
||||
"description": "Print phase progress to stderr."},
|
||||
"max_sessions": {"type": "integer",
|
||||
"description": "Cap harvested sessions per run."},
|
||||
"max_tasks": {"type": "integer",
|
||||
"description": "Cap mined tasks per run."},
|
||||
"lookback_hours": {"type": "integer",
|
||||
"description": "Harvest window in hours (default: 72)."},
|
||||
"auto_adopt": {"type": "boolean",
|
||||
"description": "Auto-adopt if gate passes (default: false)."},
|
||||
"json": {"type": "boolean",
|
||||
"description": "Return machine-readable JSON output."},
|
||||
"edit_budget": {"type": "integer",
|
||||
"description": "Max bounded edits per night (default: 4)."},
|
||||
"hour": {"type": "integer",
|
||||
"description": "Hour for schedule (0-23, default: 3)."},
|
||||
"minute": {"type": "integer",
|
||||
"description": "Minute for schedule (0-59, default: 17)."},
|
||||
},
|
||||
"additionalProperties": False,
|
||||
}
|
||||
|
||||
|
||||
def _run_engine(action: str, args: dict) -> str:
|
||||
py = sys.executable or "python3"
|
||||
cmd = [py, "-m", "skillopt_sleep", action]
|
||||
# String-valued flags
|
||||
for flag, key in [
|
||||
("--project", "project"), ("--backend", "backend"),
|
||||
("--scope", "scope"), ("--source", "source"),
|
||||
("--model", "model"), ("--tasks-file", "tasks_file"),
|
||||
("--target-skill-path", "target_skill_path"),
|
||||
]:
|
||||
val = args.get(key)
|
||||
if val:
|
||||
cmd += [flag, str(val)]
|
||||
# Integer-valued flags
|
||||
for flag, key in [
|
||||
("--max-sessions", "max_sessions"), ("--max-tasks", "max_tasks"),
|
||||
("--lookback-hours", "lookback_hours"), ("--edit-budget", "edit_budget"),
|
||||
("--hour", "hour"), ("--minute", "minute"),
|
||||
]:
|
||||
val = args.get(key)
|
||||
if val is not None:
|
||||
cmd += [flag, str(int(val))]
|
||||
# Boolean flags
|
||||
for flag, key in [
|
||||
("--progress", "progress"), ("--auto-adopt", "auto_adopt"),
|
||||
("--json", "json"),
|
||||
]:
|
||||
if args.get(key):
|
||||
cmd.append(flag)
|
||||
try:
|
||||
proc = subprocess.run(cmd, cwd=REPO_ROOT, capture_output=True, text=True, timeout=3600)
|
||||
except Exception as e:
|
||||
return f"[error] failed to run engine: {e}"
|
||||
out = (proc.stdout or "").strip()
|
||||
err = (proc.stderr or "").strip()
|
||||
return out + (("\n[stderr]\n" + err) if err else "")
|
||||
|
||||
|
||||
def _result(id_, result):
|
||||
return {"jsonrpc": "2.0", "id": id_, "result": result}
|
||||
|
||||
|
||||
def _error(id_, code, message):
|
||||
return {"jsonrpc": "2.0", "id": id_, "error": {"code": code, "message": message}}
|
||||
|
||||
|
||||
def handle(req: dict):
|
||||
method = req.get("method")
|
||||
id_ = req.get("id")
|
||||
if method == "initialize":
|
||||
return _result(id_, {
|
||||
"protocolVersion": PROTOCOL_VERSION,
|
||||
"capabilities": {"tools": {}},
|
||||
"serverInfo": {"name": "skillopt-sleep", "version": "0.1.0"},
|
||||
})
|
||||
if method in ("notifications/initialized", "initialized"):
|
||||
return None # notification, no response
|
||||
if method == "tools/list":
|
||||
return _result(id_, {"tools": [
|
||||
{"name": t["name"], "description": t["description"], "inputSchema": _TOOL_SCHEMA}
|
||||
for t in TOOLS
|
||||
]})
|
||||
if method == "tools/call":
|
||||
params = req.get("params") or {}
|
||||
name = params.get("name")
|
||||
tool = _BY_NAME.get(name)
|
||||
if not tool:
|
||||
return _error(id_, -32602, f"unknown tool: {name}")
|
||||
text = _run_engine(tool["action"], params.get("arguments") or {})
|
||||
return _result(id_, {"content": [{"type": "text", "text": text}]})
|
||||
if method == "ping":
|
||||
return _result(id_, {})
|
||||
return _error(id_, -32601, f"method not found: {method}")
|
||||
|
||||
|
||||
def main() -> int:
|
||||
for line in sys.stdin:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
req = json.loads(line)
|
||||
except Exception:
|
||||
continue
|
||||
resp = handle(req)
|
||||
if resp is not None:
|
||||
sys.stdout.write(json.dumps(resp) + "\n")
|
||||
sys.stdout.flush()
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,98 @@
|
||||
# SkillOpt — GitHub Copilot integration
|
||||
|
||||
Give **Copilot** (CLI or VS Code) direct access to the **SkillOpt** research
|
||||
engine via a tiny **MCP server**. MCP is GitHub's supported way to extend
|
||||
Copilot, so this works across Copilot CLI, VS Code, and other MCP clients with
|
||||
the same server.
|
||||
|
||||
SkillOpt is **validation-gated, text-space skill optimization**: it reflects on
|
||||
rollouts, makes bounded edits to a skill, and keeps a change only if it improves
|
||||
a held-out validation set. This plugin exposes the repo's training and eval
|
||||
entry points (`scripts/train.py`, `scripts/eval_only.py`) as Copilot tools.
|
||||
|
||||
> This is the companion to the **SkillOpt-Sleep** plugin (`../mcp_server.py`,
|
||||
> `sleep_*` tools). Sleep evolves a *local coding agent* from your past
|
||||
> sessions; this server drives the *research* training/eval loops on the
|
||||
> benchmark configs in [`../../../configs`](../../../configs).
|
||||
|
||||
## What's here
|
||||
|
||||
| File | Purpose |
|
||||
|---|---|
|
||||
| `mcp_server.py` | stdlib-only MCP (stdio) server exposing `skillopt_*` tools |
|
||||
| `mcp-config.example.json` | drop-in MCP server config |
|
||||
| `copilot-instructions.snippet.md` | paste into `.github/copilot-instructions.md` |
|
||||
|
||||
## Install
|
||||
|
||||
Requires Python ≥ 3.10. The MCP server itself is pure stdlib, but the tools it
|
||||
launches need SkillOpt's runtime deps — install the package first:
|
||||
|
||||
```bash
|
||||
pip install -e . # or: pip install -r requirements.txt
|
||||
```
|
||||
|
||||
1. **Register the MCP server.** Add the server to your Copilot MCP config
|
||||
(Copilot CLI: `~/.copilot/mcp-config.json`; VS Code: your MCP settings).
|
||||
Use `mcp-config.example.json` as a template — set `SKILLOPT_REPO` to this
|
||||
repo's path:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"skillopt": {
|
||||
"command": "python3",
|
||||
"args": ["/abs/path/SkillOpt/plugins/copilot/skillopt/mcp_server.py"],
|
||||
"env": { "SKILLOPT_REPO": "/abs/path/SkillOpt" }
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
2. **(Optional) Tell Copilot about it.** Append
|
||||
`copilot-instructions.snippet.md` to your repo's
|
||||
`.github/copilot-instructions.md` so Copilot reaches for the tools when the
|
||||
user asks to "optimize a skill" or "train on a benchmark".
|
||||
|
||||
## Use
|
||||
|
||||
Ask Copilot things like *"what configs can I run?"*, *"optimize the searchqa
|
||||
skill"*, or *"evaluate this skill on the dataset"*. Copilot calls the MCP tools:
|
||||
`skillopt_list_configs`, `skillopt_train`, `skillopt_eval`.
|
||||
|
||||
| Tool | Required args | Notes |
|
||||
|---|---|---|
|
||||
| `skillopt_list_configs` | — | Lists `configs/**/*.yaml` you can pass as `config`. |
|
||||
| `skillopt_train` | `config` | Runs a reflective optimization loop. Long-running; spends budget. |
|
||||
| `skillopt_eval` | `config`, `skill` | Evaluates one skill markdown file; no training. |
|
||||
|
||||
Common optional args (both train and eval): `env`, `backend`,
|
||||
`optimizer_model`, `target_model`, `out_root`, `cfg_options` (space-separated
|
||||
`KEY=VALUE` YAML overrides), and `extra_args` (raw passthrough flags for the
|
||||
underlying script). `skillopt_train` also accepts `num_epochs`, `batch_size`,
|
||||
`seed`, and `use_gate`.
|
||||
|
||||
Runs can be very long. The server's subprocess timeout defaults to 6 hours;
|
||||
override it with the `SKILLOPT_RUN_TIMEOUT` environment variable (seconds).
|
||||
|
||||
## Verify the server directly (no Copilot needed)
|
||||
|
||||
```bash
|
||||
printf '%s\n' \
|
||||
'{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}' \
|
||||
'{"jsonrpc":"2.0","id":2,"method":"tools/list"}' \
|
||||
'{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"skillopt_list_configs","arguments":{}}}' \
|
||||
| SKILLOPT_REPO="$(pwd)" python3 plugins/copilot/skillopt/mcp_server.py
|
||||
```
|
||||
|
||||
You should see the server info, the three `skillopt_*` tools, and the list of
|
||||
benchmark configs.
|
||||
|
||||
## Notes / status
|
||||
|
||||
- MCP is the stable, official Copilot extension surface, so this is portable
|
||||
across Copilot CLI and IDE from one server.
|
||||
- `skillopt_list_configs` is filesystem-only and safe to call anytime;
|
||||
`skillopt_train` / `skillopt_eval` shell out to the repo scripts and require
|
||||
the SkillOpt runtime deps (and, for real backends, model credentials — see
|
||||
[`../../../.env.example`](../../../.env.example)).
|
||||
@@ -0,0 +1,33 @@
|
||||
<!--
|
||||
Copy this block into your repo's .github/copilot-instructions.md so Copilot
|
||||
knows the SkillOpt research-engine tools exist. (Copilot reads
|
||||
copilot-instructions.md automatically as ambient guidance.)
|
||||
-->
|
||||
|
||||
## SkillOpt (research skill-optimization engine)
|
||||
|
||||
This repo exposes the core **SkillOpt** training/eval engine via an MCP server
|
||||
(`skillopt`). SkillOpt is validation-gated, text-space skill optimization: it
|
||||
reflects on rollouts, makes bounded edits to a skill, and keeps a change only
|
||||
if it improves a held-out validation set.
|
||||
|
||||
When the user asks to "optimize a skill", "train on <benchmark>", "run
|
||||
SkillOpt", "evaluate this skill", or "what configs can I run", use the MCP
|
||||
tools:
|
||||
|
||||
- `skillopt_list_configs` — list the benchmark YAML configs you can pass as `config`
|
||||
- `skillopt_train` — run a reflective skill-optimization loop on a config (long-running; spends API/compute budget)
|
||||
- `skillopt_eval` — evaluate a single skill markdown file on a dataset (no training)
|
||||
|
||||
Guidance:
|
||||
- Always run `skillopt_list_configs` first if you don't already know a valid `config` path.
|
||||
- `skillopt_train` and `skillopt_eval` are long-running and consume the user's
|
||||
model backend/budget — confirm the `config`, `backend`, and model choices
|
||||
with the user before launching, and surface the held-out gate result when the
|
||||
run finishes.
|
||||
- For one-off YAML overrides use `cfg_options` (e.g. `seed=123 batch_size=40`);
|
||||
for any other underlying flag use `extra_args`.
|
||||
|
||||
This is distinct from the **SkillOpt-Sleep** MCP server (`skillopt-sleep`,
|
||||
`sleep_*` tools), which evolves a local coding agent from past sessions rather
|
||||
than running the research benchmarks.
|
||||
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"mcpServers": {
|
||||
"skillopt": {
|
||||
"command": "python3",
|
||||
"args": ["plugins/copilot/skillopt/mcp_server.py"],
|
||||
"env": {
|
||||
"SKILLOPT_REPO": "${workspaceFolder}"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,229 @@
|
||||
#!/usr/bin/env python3
|
||||
"""SkillOpt (research engine) — minimal MCP server (stdio, stdlib-only).
|
||||
|
||||
Exposes the core SkillOpt skill-optimization engine as MCP tools so any
|
||||
MCP-capable client (GitHub Copilot CLI / VS Code, Claude Desktop, etc.) can
|
||||
drive it. No third-party deps: speaks JSON-RPC 2.0 over stdio with just the
|
||||
handful of MCP methods clients need.
|
||||
|
||||
This is the companion to the SkillOpt-Sleep MCP server (``../mcp_server.py``).
|
||||
Where Sleep evolves a *local agent* from past sessions, this server drives the
|
||||
*research* training/eval loops from this repo (``scripts/train.py`` /
|
||||
``scripts/eval_only.py``) against the benchmark configs in ``configs/``.
|
||||
|
||||
Tools exposed:
|
||||
- skillopt_list_configs : discover the benchmark YAML configs you can use
|
||||
- skillopt_train : run a reflective skill-optimization (training) loop
|
||||
- skillopt_eval : evaluate a single skill on a dataset (no training)
|
||||
|
||||
``skillopt_train`` and ``skillopt_eval`` shell out to the repo's entry-point
|
||||
scripts and stream back their stdout/stderr. Configure your client to launch:
|
||||
python plugins/copilot/skillopt/mcp_server.py
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import glob
|
||||
import json
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
# Repo root: three levels up from plugins/copilot/skillopt/mcp_server.py
|
||||
REPO_ROOT = os.environ.get("SKILLOPT_REPO") or os.path.abspath(
|
||||
os.path.join(os.path.dirname(__file__), "..", "..", "..")
|
||||
)
|
||||
PROTOCOL_VERSION = "2024-11-05"
|
||||
|
||||
# Training/eval runs are long; give the engine plenty of headroom.
|
||||
RUN_TIMEOUT_SECONDS = int(os.environ.get("SKILLOPT_RUN_TIMEOUT", "21600")) # 6h
|
||||
|
||||
|
||||
def _list_configs() -> str:
|
||||
"""List the benchmark configs available under configs/ (filesystem only)."""
|
||||
pattern = os.path.join(REPO_ROOT, "configs", "**", "*.yaml")
|
||||
paths = sorted(glob.glob(pattern, recursive=True))
|
||||
if not paths:
|
||||
return f"[no configs found under {os.path.join(REPO_ROOT, 'configs')}]"
|
||||
rels = [os.path.relpath(p, REPO_ROOT).replace(os.sep, "/") for p in paths]
|
||||
lines = ["Available SkillOpt configs (pass as `config`):", ""]
|
||||
lines += [f" - {r}" for r in rels]
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _run_script(script_rel: str, args: dict, *, required: tuple[str, ...] = ()) -> str:
|
||||
"""Shell out to a repo entry-point script, mapping args -> --flags."""
|
||||
for key in required:
|
||||
if not args.get(key):
|
||||
return f"[error] missing required argument: {key}"
|
||||
|
||||
py = sys.executable or "python3"
|
||||
cmd = [py, os.path.join("scripts", script_rel)]
|
||||
|
||||
# Ordered flags that the train/eval scripts accept directly.
|
||||
flag_args = (
|
||||
"config", "skill", "split", "env", "backend",
|
||||
"optimizer_model", "target_model", "out_root",
|
||||
"num_epochs", "batch_size", "seed", "use_gate",
|
||||
)
|
||||
for key in flag_args:
|
||||
val = args.get(key)
|
||||
if val is None or val == "":
|
||||
continue
|
||||
cmd += [f"--{key}", str(val)]
|
||||
|
||||
# cfg-options: arbitrary KEY=VALUE YAML overrides (nargs="+").
|
||||
cfg_options = args.get("cfg_options")
|
||||
if cfg_options:
|
||||
if isinstance(cfg_options, str):
|
||||
cfg_options = cfg_options.split()
|
||||
cmd += ["--cfg-options", *[str(x) for x in cfg_options]]
|
||||
|
||||
# extra_args: raw passthrough for any other train/eval flag.
|
||||
extra = args.get("extra_args")
|
||||
if extra:
|
||||
if isinstance(extra, str):
|
||||
extra = extra.split()
|
||||
cmd += [str(x) for x in extra]
|
||||
|
||||
try:
|
||||
proc = subprocess.run(
|
||||
cmd, cwd=REPO_ROOT, capture_output=True, text=True,
|
||||
timeout=RUN_TIMEOUT_SECONDS,
|
||||
)
|
||||
except subprocess.TimeoutExpired:
|
||||
return f"[error] run exceeded {RUN_TIMEOUT_SECONDS}s timeout: {' '.join(cmd)}"
|
||||
except Exception as e: # noqa: BLE001
|
||||
return f"[error] failed to run script: {e}"
|
||||
out = (proc.stdout or "").strip()
|
||||
err = (proc.stderr or "").strip()
|
||||
body = out + (("\n[stderr]\n" + err) if err else "")
|
||||
return body or f"[done] exit code {proc.returncode}, no output"
|
||||
|
||||
|
||||
TOOLS = [
|
||||
{
|
||||
"name": "skillopt_list_configs",
|
||||
"description": "List the benchmark YAML configs under configs/ that can be passed as `config` to train/eval.",
|
||||
},
|
||||
{
|
||||
"name": "skillopt_train",
|
||||
"description": "Run a SkillOpt reflective skill-optimization (training) loop on a benchmark config. Long-running; uses your model backend/budget.",
|
||||
},
|
||||
{
|
||||
"name": "skillopt_eval",
|
||||
"description": "Evaluate a single skill markdown file on a dataset without training (scripts/eval_only.py).",
|
||||
},
|
||||
]
|
||||
_BY_NAME = {t["name"]: t for t in TOOLS}
|
||||
|
||||
_NO_ARGS_SCHEMA = {"type": "object", "properties": {}, "additionalProperties": False}
|
||||
|
||||
_COMMON_PROPS = {
|
||||
"config": {"type": "string",
|
||||
"description": "Path to a benchmark YAML config (e.g. configs/searchqa/default.yaml). See skillopt_list_configs."},
|
||||
"env": {"type": "string", "description": "Override the environment/adapter name (e.g. searchqa, alfworld)."},
|
||||
"backend": {"type": "string", "description": "Model backend (e.g. azure_openai, claude, codex, qwen, minimax)."},
|
||||
"optimizer_model": {"type": "string", "description": "Model used for reflection/skill rewriting (the optimizer)."},
|
||||
"target_model": {"type": "string", "description": "Model used to execute tasks (the target)."},
|
||||
"out_root": {"type": "string", "description": "Output directory root for run artifacts."},
|
||||
"cfg_options": {"type": "string", "description": "Space-separated YAML overrides, e.g. 'seed=123 batch_size=40'."},
|
||||
"extra_args": {"type": "string", "description": "Raw passthrough flags for the underlying script, e.g. '--workers 8 --max_turns 30'."},
|
||||
}
|
||||
|
||||
_TRAIN_SCHEMA = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
**_COMMON_PROPS,
|
||||
"num_epochs": {"type": "integer", "description": "Number of optimization epochs."},
|
||||
"batch_size": {"type": "integer", "description": "Tasks per optimization step."},
|
||||
"seed": {"type": "integer", "description": "Random seed."},
|
||||
"use_gate": {"type": "string", "enum": ["true", "false"],
|
||||
"description": "Whether to keep the held-out validation gate on (default on)."},
|
||||
},
|
||||
"required": ["config"],
|
||||
"additionalProperties": False,
|
||||
}
|
||||
|
||||
_EVAL_SCHEMA = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
**_COMMON_PROPS,
|
||||
"skill": {"type": "string", "description": "Path to the skill markdown file to evaluate."},
|
||||
"split": {"type": "string", "description": "Dataset split to evaluate (default: all)."},
|
||||
},
|
||||
"required": ["config", "skill"],
|
||||
"additionalProperties": False,
|
||||
}
|
||||
|
||||
_SCHEMA_BY_NAME = {
|
||||
"skillopt_list_configs": _NO_ARGS_SCHEMA,
|
||||
"skillopt_train": _TRAIN_SCHEMA,
|
||||
"skillopt_eval": _EVAL_SCHEMA,
|
||||
}
|
||||
|
||||
|
||||
def _result(id_, result):
|
||||
return {"jsonrpc": "2.0", "id": id_, "result": result}
|
||||
|
||||
|
||||
def _error(id_, code, message):
|
||||
return {"jsonrpc": "2.0", "id": id_, "error": {"code": code, "message": message}}
|
||||
|
||||
|
||||
def _dispatch(name: str, args: dict) -> str:
|
||||
if name == "skillopt_list_configs":
|
||||
return _list_configs()
|
||||
if name == "skillopt_train":
|
||||
return _run_script("train.py", args, required=("config",))
|
||||
if name == "skillopt_eval":
|
||||
return _run_script("eval_only.py", args, required=("config", "skill"))
|
||||
return f"[error] unknown tool: {name}"
|
||||
|
||||
|
||||
def handle(req: dict):
|
||||
method = req.get("method")
|
||||
id_ = req.get("id")
|
||||
if method == "initialize":
|
||||
return _result(id_, {
|
||||
"protocolVersion": PROTOCOL_VERSION,
|
||||
"capabilities": {"tools": {}},
|
||||
"serverInfo": {"name": "skillopt", "version": "0.1.0"},
|
||||
})
|
||||
if method in ("notifications/initialized", "initialized"):
|
||||
return None # notification, no response
|
||||
if method == "tools/list":
|
||||
return _result(id_, {"tools": [
|
||||
{"name": t["name"], "description": t["description"],
|
||||
"inputSchema": _SCHEMA_BY_NAME[t["name"]]}
|
||||
for t in TOOLS
|
||||
]})
|
||||
if method == "tools/call":
|
||||
params = req.get("params") or {}
|
||||
name = params.get("name")
|
||||
if name not in _BY_NAME:
|
||||
return _error(id_, -32602, f"unknown tool: {name}")
|
||||
text = _dispatch(name, params.get("arguments") or {})
|
||||
return _result(id_, {"content": [{"type": "text", "text": text}]})
|
||||
if method == "ping":
|
||||
return _result(id_, {})
|
||||
return _error(id_, -32601, f"method not found: {method}")
|
||||
|
||||
|
||||
def main() -> int:
|
||||
for line in sys.stdin:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
req = json.loads(line)
|
||||
except Exception:
|
||||
continue
|
||||
resp = handle(req)
|
||||
if resp is not None:
|
||||
sys.stdout.write(json.dumps(resp) + "\n")
|
||||
sys.stdout.flush()
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,66 @@
|
||||
# SkillOpt-Sleep — Devin integration
|
||||
|
||||
Give **Devin** (Cognition) a nightly **sleep cycle** via a tiny **MCP server**
|
||||
that exposes the `skillopt_sleep` engine as tools. MCP is Devin's supported way
|
||||
to add custom tooling, so this works in Devin's CLI and IDE.
|
||||
|
||||
Devin doesn't write transcripts in the format the engine consumes, so this
|
||||
plugin adds a **Devin-specific harvester** that converts every locally available
|
||||
source into the Claude Code-compatible JSONL the engine reads.
|
||||
|
||||
## What's here
|
||||
|
||||
| File | Purpose |
|
||||
|---|---|
|
||||
| `mcp_server.py` | stdlib-only MCP (stdio) server exposing `sleep_*` tools |
|
||||
| `harvest_devin.py` | converts Devin ATIF-v1.7 transcripts + agentmemory + `.devin/skills` into JSONL, with `taskKey` + outcome envelopes |
|
||||
| `judge.py` | reference judge for the deferred/judge branch of the validation gate |
|
||||
| `mcp-config.example.json` | drop-in MCP server config |
|
||||
| `devin-rules.snippet.md` | paste into `.devin/rules/skillopt-sleep.md` |
|
||||
|
||||
## What it harvests
|
||||
|
||||
| Source | Where |
|
||||
|---|---|
|
||||
| Devin transcripts (ATIF-v1.7) | `~/.local/share/devin/cli/transcripts/*.json` |
|
||||
| agentmemory | `~/.agentmemory/standalone.json` |
|
||||
| Skill files | `.devin/skills/*/SKILL.md` |
|
||||
|
||||
Workspaces are auto-detected from `~/.config/Devin/User/workspaceStorage/*/workspace.json`.
|
||||
After `sleep_adopt`, the evolved skill is synced to `.devin/skills/skillopt-sleep-learned/SKILL.md`.
|
||||
|
||||
## Install
|
||||
|
||||
Requires Python ≥ 3.10. No third-party packages — the server is pure stdlib.
|
||||
|
||||
1. **Register the MCP server.** Use `mcp-config.example.json` as a template; set
|
||||
`args` to the absolute path of this `mcp_server.py`. The engine is found
|
||||
automatically (this plugin lives inside the SkillOpt repo). Or via the Devin
|
||||
CLI:
|
||||
|
||||
```bash
|
||||
devin mcp add skillopt-sleep \
|
||||
--env "SKILLOPT_DEVIN_CLAUDE_HOME=$HOME/.skillopt-sleep-devin" \
|
||||
-- python3 /abs/path/to/SkillOpt/plugins/devin/mcp_server.py
|
||||
```
|
||||
|
||||
2. **(Optional)** copy `devin-rules.snippet.md` to `.devin/rules/skillopt-sleep.md`
|
||||
so Devin proactively offers the tools.
|
||||
|
||||
3. Ask Devin: *"run the sleep cycle"*, *"what did the last sleep propose?"*, *"adopt it"*.
|
||||
|
||||
## Tools
|
||||
|
||||
| Tool | What it does |
|
||||
|---|---|
|
||||
| `sleep_status` | nights run so far + latest staged proposal |
|
||||
| `sleep_dry_run` | preview cycle — no staging, no changes |
|
||||
| `sleep_run` | full cycle; stages a proposal for review |
|
||||
| `sleep_adopt` | apply the staged proposal; syncs skill to the workspace |
|
||||
| `sleep_harvest` | debug: list the recurring tasks mined |
|
||||
| `sleep_schedule` | install a nightly cron entry (`--hour` / `--minute`) |
|
||||
| `sleep_unschedule` | remove the nightly cron entry |
|
||||
|
||||
Default backend is `mock` (no API spend); `--backend claude|codex` uses your own
|
||||
budget. Same engine and `sleep_*` interface as the other plugins — all call
|
||||
`python -m skillopt_sleep`.
|
||||
@@ -0,0 +1,19 @@
|
||||
# SkillOpt-Sleep (Devin)
|
||||
|
||||
You have access to a nightly self-evolution cycle via the `skillopt-sleep` MCP
|
||||
server. Use these tools to improve your long-term skills over time:
|
||||
|
||||
- **`sleep_status`** — how many nights have run + the latest staged proposal
|
||||
- **`sleep_dry_run`** — preview a cycle without changing anything
|
||||
- **`sleep_run`** — run a full cycle; stages a proposal for review
|
||||
- **`sleep_adopt`** — apply the staged proposal to `.devin/skills/skillopt-sleep-learned/SKILL.md`
|
||||
- **`sleep_harvest`** — debug: list the recurring tasks mined from recent sessions
|
||||
- **`sleep_schedule`** / **`sleep_unschedule`** — install/remove a nightly cron run
|
||||
|
||||
When a user asks about the sleep cycle, skill evolution, or improving your
|
||||
long-term memory, prefer calling these tools over explaining the concept.
|
||||
|
||||
Default backend is `mock` (no API spend). Pass `backend: "claude"` or
|
||||
`backend: "codex"` with your own API key for real LLM-driven optimization.
|
||||
|
||||
Place this file at `.devin/rules/skillopt-sleep.md` in your workspace.
|
||||
@@ -0,0 +1,21 @@
|
||||
{
|
||||
"schema_version": "ATIF-v1.7",
|
||||
"session_id": "demo-001",
|
||||
"steps": [
|
||||
{
|
||||
"source": "user",
|
||||
"message": "Fix the failing NullPointerException in OrderService.persist() in the dutch-kis project",
|
||||
"timestamp": "2026-06-20T10:00:00Z"
|
||||
},
|
||||
{
|
||||
"source": "agent",
|
||||
"message": "The repository call returns an Optional that is being unwrapped with .get(). I'll switch to orElseThrow(NotFoundException::new) so the missing-row case is handled.",
|
||||
"timestamp": "2026-06-20T10:00:05Z"
|
||||
},
|
||||
{
|
||||
"source": "agent",
|
||||
"message": "Applied the fix and ran the suite: rtk mvn test -Dtest=OrderServiceTest -> BUILD SUCCESS, 142 passed, 0 failed.",
|
||||
"timestamp": "2026-06-20T10:01:00Z"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,533 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Convert Devin IDE local data into Claude Code-format JSONL transcripts.
|
||||
|
||||
Devin (Cognition) does not persist agent conversation transcripts to disk in a
|
||||
format the sleep engine understands. This script bridges that gap by synthesising
|
||||
JSONL files from every locally available source:
|
||||
|
||||
1. **Devin transcripts** (~/.local/share/devin/cli/transcripts/*.json)
|
||||
Native ATIF-v1.7 format — source:"user" / source:"agent" messages
|
||||
converted directly to user/assistant JSONL turns.
|
||||
|
||||
2. **agentmemory** (~/.agentmemory/standalone.json)
|
||||
Memories saved by the `agentmemory` MCP server — each memory's title
|
||||
becomes a synthetic user prompt; its content becomes the assistant reply.
|
||||
|
||||
3. **Skill files** (.devin/skills/*/SKILL.md)
|
||||
Each skill description is converted to a session where the user asked
|
||||
"use the <skill> skill" and the assistant described how to apply it.
|
||||
|
||||
Output layout (mirrors ~/.claude/projects/<slug>/<sessionId>.jsonl):
|
||||
<out_dir>/projects/<slug>/<session_id>.jsonl
|
||||
|
||||
Workspace auto-detection order:
|
||||
1. ``SKILLOPT_DEVIN_WORKSPACES`` env var — colon-separated abs paths
|
||||
2. Devin registry: ``~/.config/Devin/User/workspaceStorage/*/workspace.json``
|
||||
4. Working directory fallback
|
||||
|
||||
Usage (standalone):
|
||||
python harvest_devin.py [--out-dir PATH] [--workspaces PATH ...]
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import hashlib
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import sys
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional
|
||||
from urllib.parse import unquote, urlparse
|
||||
|
||||
# ── cross-platform path resolution (Linux + Windows + macOS) ──────────────────
|
||||
#
|
||||
# Devin is a VS Code-family app, so its user-data dir moves with the OS:
|
||||
# Linux ~/.config/<App>, Windows %APPDATA%\<App>, macOS
|
||||
# ~/Library/Application Support/<App>. Resolve all candidates and let callers
|
||||
# keep whichever actually exists.
|
||||
|
||||
def _app_data_roots(app: str) -> List[str]:
|
||||
"""User-data dir candidates for a VS Code-family app, current OS first."""
|
||||
home = os.path.expanduser("~")
|
||||
roots: List[str] = []
|
||||
if os.name == "nt":
|
||||
appdata = os.environ.get("APPDATA") or os.path.join(home, "AppData", "Roaming")
|
||||
roots.append(os.path.join(appdata, app))
|
||||
elif sys.platform == "darwin":
|
||||
roots.append(os.path.join(home, "Library", "Application Support", app))
|
||||
# XDG / Linux (also a sensible fallback everywhere)
|
||||
xdg = os.environ.get("XDG_CONFIG_HOME") or os.path.join(home, ".config")
|
||||
roots.append(os.path.join(xdg, app))
|
||||
# de-dupe, preserve order
|
||||
return list(dict.fromkeys(roots))
|
||||
|
||||
|
||||
def _devin_transcript_candidates() -> List[str]:
|
||||
"""Where the Devin CLI may store ATIF transcripts, per OS."""
|
||||
home = os.path.expanduser("~")
|
||||
cands: List[str] = []
|
||||
if os.name == "nt":
|
||||
for base in (os.environ.get("LOCALAPPDATA"), os.environ.get("APPDATA")):
|
||||
if base:
|
||||
cands.append(os.path.join(base, "devin", "cli", "transcripts"))
|
||||
elif sys.platform == "darwin":
|
||||
cands.append(os.path.join(home, "Library", "Application Support",
|
||||
"devin", "cli", "transcripts"))
|
||||
cands.append(os.path.join(home, ".local", "share", "devin", "cli", "transcripts"))
|
||||
return list(dict.fromkeys(cands))
|
||||
|
||||
|
||||
def _first_existing(paths: List[str]) -> str:
|
||||
"""First path that exists, else the first candidate (for nice messaging)."""
|
||||
for p in paths:
|
||||
if os.path.exists(p):
|
||||
return p
|
||||
return paths[0] if paths else ""
|
||||
|
||||
|
||||
def _uri_to_path(folder: str) -> str:
|
||||
"""Convert a VS Code ``file://`` workspace URI to a local path, cross-platform.
|
||||
|
||||
Linux: file:///home/u/proj -> /home/u/proj
|
||||
Windows: file:///c%3A/Users/u/p -> c:/Users/u/p
|
||||
"""
|
||||
if not folder.startswith("file://"):
|
||||
return folder
|
||||
path = unquote(urlparse(folder).path)
|
||||
# Windows drive paths come through as '/C:/...' — strip the leading slash.
|
||||
if os.name == "nt" and re.match(r"^/[A-Za-z]:", path):
|
||||
path = path[1:]
|
||||
return path
|
||||
|
||||
# ── workspace auto-detection ─────────────────────────────────────────────────
|
||||
|
||||
def _workspaces_from_registry(storage_root: str) -> List[tuple]:
|
||||
"""Read VS Code-style workspaceStorage to get (mtime, path) pairs."""
|
||||
results: List[tuple] = []
|
||||
if not os.path.isdir(storage_root):
|
||||
return results
|
||||
for entry in os.scandir(storage_root):
|
||||
ws_json = os.path.join(entry.path, "workspace.json")
|
||||
if not os.path.isfile(ws_json):
|
||||
continue
|
||||
try:
|
||||
with open(ws_json, encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
folder = _uri_to_path(data.get("folder", ""))
|
||||
if folder and os.path.isdir(folder):
|
||||
results.append((os.path.getmtime(ws_json), folder))
|
||||
except Exception:
|
||||
continue
|
||||
return results
|
||||
|
||||
|
||||
def _detect_workspaces() -> List[str]:
|
||||
"""Return known workspace paths (Devin registry), newest first."""
|
||||
env_val = os.environ.get("SKILLOPT_DEVIN_WORKSPACES", "")
|
||||
if env_val:
|
||||
# os.pathsep so Windows 'C:\a;C:\b' splits correctly (not on the drive colon)
|
||||
return [p for p in env_val.split(os.pathsep) if p and os.path.isdir(p)]
|
||||
|
||||
registries: List[str] = [
|
||||
os.path.join(r, "User", "workspaceStorage")
|
||||
for r in _app_data_roots("Devin")
|
||||
]
|
||||
|
||||
seen: set = set()
|
||||
results: List[tuple] = []
|
||||
for registry in registries:
|
||||
for mtime, folder in _workspaces_from_registry(registry):
|
||||
if folder not in seen:
|
||||
seen.add(folder)
|
||||
results.append((mtime, folder))
|
||||
results.sort(reverse=True)
|
||||
paths = [p for _, p in results]
|
||||
return paths if paths else [os.getcwd()]
|
||||
|
||||
# ── helpers ───────────────────────────────────────────────────────────────────
|
||||
|
||||
def _slug(path: str) -> str:
|
||||
"""SHA-256 of abs-path, first 16 hex chars — matches Claude Code's scheme."""
|
||||
return hashlib.sha256(os.path.abspath(path).encode()).hexdigest()[:16]
|
||||
|
||||
|
||||
def _iso(epoch_ms: Optional[float] = None) -> str:
|
||||
dt = (datetime.fromtimestamp(epoch_ms / 1000.0, tz=timezone.utc)
|
||||
if epoch_ms is not None else datetime.now(tz=timezone.utc))
|
||||
return dt.strftime("%Y-%m-%dT%H:%M:%S.000Z")
|
||||
|
||||
|
||||
def _write_session(
|
||||
out_dir: str, project: str, session_id: str,
|
||||
user_prompts: List[str], assistant_replies: List[str],
|
||||
timestamp_base_ms: float,
|
||||
task_key: Optional[str] = None,
|
||||
) -> None:
|
||||
slug = _slug(project)
|
||||
session_dir = os.path.join(out_dir, "projects", slug)
|
||||
os.makedirs(session_dir, exist_ok=True)
|
||||
out_path = os.path.join(session_dir, f"{session_id}.jsonl")
|
||||
ts = timestamp_base_ms
|
||||
with open(out_path, "w", encoding="utf-8") as f:
|
||||
for user_text, asst_text in zip(user_prompts, assistant_replies):
|
||||
user_rec = {
|
||||
"type": "user",
|
||||
"message": {"role": "user", "content": user_text},
|
||||
"cwd": project,
|
||||
"timestamp": _iso(ts),
|
||||
"sessionId": session_id,
|
||||
"version": "1.0",
|
||||
}
|
||||
if task_key:
|
||||
# grouping key so the miner can collapse repeats into one recurring task
|
||||
user_rec["taskKey"] = task_key
|
||||
f.write(json.dumps(user_rec, ensure_ascii=False) + "\n")
|
||||
# space the reply >=5s after the prompt so a single-turn session
|
||||
# isn't misclassified as a <3s headless replay and dropped by the
|
||||
# engine's harvest filter (skillopt_sleep Issue #62).
|
||||
ts += 5000
|
||||
f.write(json.dumps({
|
||||
"type": "assistant",
|
||||
"message": {"role": "assistant", "content": asst_text},
|
||||
"timestamp": _iso(ts),
|
||||
"sessionId": session_id,
|
||||
"version": "1.0",
|
||||
}, ensure_ascii=False) + "\n")
|
||||
ts += 2000
|
||||
|
||||
|
||||
def _append_history(out_dir: str, display: str, project: str, timestamp_ms: float) -> None:
|
||||
record = {"display": display, "timestamp": timestamp_ms, "project": project}
|
||||
with open(os.path.join(out_dir, "history.jsonl"), "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(record, ensure_ascii=False) + "\n")
|
||||
|
||||
|
||||
def _infer_project(text: str, workspaces: List[str]) -> str:
|
||||
for ws in workspaces:
|
||||
if os.path.basename(ws.rstrip("/")).lower() in text.lower():
|
||||
return ws
|
||||
return workspaces[0] if workspaces else os.getcwd()
|
||||
|
||||
# ── task identity + outcome extraction (fuel for the validation gate) ─────────
|
||||
#
|
||||
# SkillOpt's gate only works "where tasks recur and have a checkable correctness
|
||||
# signal." These helpers add the two things a raw transcript lacks:
|
||||
# * a stable taskKey so repeats collapse into one recurring task, and
|
||||
# * an outcome envelope (success + verifier + re-runnable reference) so the
|
||||
# held-out replay has something to score against.
|
||||
|
||||
_LANG_HINTS = [
|
||||
("java", r"(java|spring|maven|\bmvn\b|gradle|\.java\b|lombok)"),
|
||||
("python", r"(python|pytest|\bpip\b|\.py\b|django|flask)"),
|
||||
("ts", r"(typescript|\.tsx?\b|\bnpm\b|jest|node)"),
|
||||
("js", r"(javascript|\.jsx?\b)"),
|
||||
("sql", r"(\bsql\b|select\s|mariadb|mysql|postgres|\.sql\b)"),
|
||||
("go", r"(golang|\bgo test\b|\.go\b)"),
|
||||
("rust", r"(rust|cargo|\.rs\b)"),
|
||||
]
|
||||
_INTENT_HINTS = [
|
||||
("fix", r"(fix|bug|error|fail|npe|exception|broken|crash)"),
|
||||
("implement", r"(implement|add|create|build|introduce|support)"),
|
||||
("refactor", r"(refactor|clean ?up|rename|extract|simplify)"),
|
||||
("test", r"(test|coverage|assert)"),
|
||||
("review", r"(review|audit|inspect)"),
|
||||
("optimize", r"(optimi[sz]e|perf|speed up|slow)"),
|
||||
("explain", r"(explain|understand|what does|how does)"),
|
||||
]
|
||||
_STOPWORDS = {"please", "this", "that", "with", "from", "into", "should",
|
||||
"would", "code", "using", "the", "have"}
|
||||
|
||||
|
||||
def _normalize_task_key(text: str, project: str) -> str:
|
||||
"""Stable '<lang>:<intent>:<target>' grouping key for a task."""
|
||||
low = text.lower()
|
||||
lang = next((n for n, pat in _LANG_HINTS if re.search(pat, low)), "general")
|
||||
intent = next((n for n, pat in _INTENT_HINTS if re.search(pat, low)), "task")
|
||||
# target: prefer a CamelCase identifier, then a filename, then first real word
|
||||
m = re.search(r"\b([A-Z][a-z0-9]+(?:[A-Z][a-z0-9]+)+)\b", text) # CamelCase
|
||||
if not m:
|
||||
m = re.search(r"\b([\w-]+\.\w+)\b", text) # filename.ext
|
||||
if m:
|
||||
target = m.group(1)
|
||||
else:
|
||||
# first content word that isn't a stopword or an intent verb (e.g. "implement")
|
||||
target = next((w for w in re.findall(r"[a-zA-Z]{4,}", low)
|
||||
if w not in _STOPWORDS
|
||||
and not any(re.search(pat, w) for _, pat in _INTENT_HINTS)),
|
||||
"general")
|
||||
target = re.sub(r"[^a-zA-Z0-9]+", "-", target).strip("-").lower()[:40] or "general"
|
||||
return f"{lang}:{intent}:{target}"
|
||||
|
||||
|
||||
_PASS_PAT = re.compile(
|
||||
r"(build success|all tests? pass(?:ed)?|\b\d+ passed\b|\b0 failed\b|"
|
||||
r"tests? pass(?:ed)?|✓|no errors)", re.IGNORECASE)
|
||||
_FAIL_PAT = re.compile(
|
||||
r"(build failure|tests? failed|\b[1-9]\d* failed\b|error:|traceback|"
|
||||
r"assertion ?error)", re.IGNORECASE) # note: "0 failed" must NOT match
|
||||
_CMD_PAT = re.compile(
|
||||
r"((?:rtk\s+)?(?:mvn|gradle|pytest|npm(?:\s+run)?\s+test|yarn\s+test|"
|
||||
r"go\s+test|cargo\s+test)[^\n`]*)", re.IGNORECASE)
|
||||
|
||||
|
||||
def _detect_outcome(messages: List[str]) -> Optional[Dict[str, Any]]:
|
||||
"""Best-effort checkable signal from agent messages. None ⇒ no hard signal."""
|
||||
blob = "\n".join(m for m in messages if m)
|
||||
pass_hit, fail_hit = _PASS_PAT.search(blob), _FAIL_PAT.search(blob)
|
||||
if not pass_hit and not fail_hit:
|
||||
return None
|
||||
verifier = "tests" if re.search(r"test|pytest", blob, re.IGNORECASE) else "build"
|
||||
out: Dict[str, Any] = {
|
||||
"success": bool(pass_hit) and not fail_hit,
|
||||
"verifier": verifier,
|
||||
"evidence": (pass_hit or fail_hit).group(0).strip(),
|
||||
}
|
||||
cmd = _CMD_PAT.search(blob)
|
||||
if cmd:
|
||||
# keep only the command itself, dropping any "-> result" / ": output" tail
|
||||
repro = re.split(r"\s*(?:->|→|:|,)\s*", cmd.group(1))[0].strip()
|
||||
out["reference"] = {"repro": repro}
|
||||
return out
|
||||
|
||||
|
||||
def _build_rubric(user_prompt: str) -> List[str]:
|
||||
"""Derive checkable criteria from the task so a judge has something to score."""
|
||||
crit: List[str] = []
|
||||
ids = re.findall(r"\b([A-Z][a-z0-9]+(?:[A-Z][a-z0-9]+)+|[\w-]+\.\w+)\b", user_prompt)
|
||||
for i in dict.fromkeys(ids): # dedupe, preserve order
|
||||
crit.append(f"Addresses {i}")
|
||||
intent = _normalize_task_key(user_prompt, "").split(":")[1]
|
||||
crit.append({
|
||||
"fix": "Resolves the reported defect without introducing new errors",
|
||||
"implement": "Implements the requested behavior end to end",
|
||||
"refactor": "Preserves behavior while improving structure",
|
||||
"test": "Adds or fixes tests that actually exercise the change",
|
||||
"optimize": "Improves performance without changing results",
|
||||
}.get(intent, "Satisfies the user's stated request"))
|
||||
crit.append("Response is concrete and actionable, not a restatement of the task")
|
||||
return crit[:5]
|
||||
|
||||
|
||||
def _judge_rubric_fallback(user_prompt: str) -> Dict[str, Any]:
|
||||
"""When no hard signal exists, attach a rubric and mark the task for judge
|
||||
scoring. success=None tells the gate to defer/judge rather than trust it.
|
||||
The actual scoring is done by judge.py (or the engine) at replay time."""
|
||||
return {
|
||||
"success": None,
|
||||
"verifier": "judge",
|
||||
"rubric": _build_rubric(user_prompt or ""),
|
||||
}
|
||||
|
||||
|
||||
def _write_outcome(out_dir: str, session_id: str, task_key: str, project: str,
|
||||
ts_ms: float, outcome: Dict[str, Any]) -> None:
|
||||
rec = {"type": "outcome", "sessionId": session_id, "taskKey": task_key,
|
||||
"project": project, "timestamp": _iso(ts_ms), **outcome}
|
||||
with open(os.path.join(out_dir, "outcomes.jsonl"), "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(rec, ensure_ascii=False) + "\n")
|
||||
|
||||
# ── source 1: Devin ATIF-v1.7 transcripts ────────────────────────────────────
|
||||
|
||||
def harvest_devin_transcripts(
|
||||
transcripts_dir: str, out_dir: str, workspaces: List[str]
|
||||
) -> int:
|
||||
"""Convert Devin CLI ATIF-v1.7 transcripts to Claude Code JSONL."""
|
||||
if not os.path.isdir(transcripts_dir):
|
||||
return 0
|
||||
written = 0
|
||||
for entry in os.scandir(transcripts_dir):
|
||||
if not entry.name.endswith(".json"):
|
||||
continue
|
||||
try:
|
||||
with open(entry.path, encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
except Exception:
|
||||
continue
|
||||
if data.get("schema_version", "").startswith("ATIF"):
|
||||
pass # Devin native format
|
||||
else:
|
||||
continue
|
||||
session_id = data.get("session_id") or entry.name[:-5]
|
||||
steps = data.get("steps") or []
|
||||
user_prompts: List[str] = []
|
||||
agent_replies: List[str] = []
|
||||
project = ""
|
||||
ts_base: Optional[float] = None
|
||||
for step in steps:
|
||||
src = step.get("source", "")
|
||||
msg = str(step.get("message") or "").strip()
|
||||
if not msg or src == "system":
|
||||
continue
|
||||
if src == "user":
|
||||
user_prompts.append(msg)
|
||||
if not project:
|
||||
project = _infer_project(msg, workspaces)
|
||||
elif src == "agent":
|
||||
agent_replies.append(msg)
|
||||
if ts_base is None:
|
||||
raw_ts = step.get("timestamp", "")
|
||||
if raw_ts:
|
||||
try:
|
||||
from datetime import datetime as _dt
|
||||
ts_base = _dt.fromisoformat(
|
||||
raw_ts.replace("Z", "+00:00")
|
||||
).timestamp() * 1000
|
||||
except Exception:
|
||||
pass
|
||||
if not user_prompts:
|
||||
continue
|
||||
if not project:
|
||||
project = workspaces[0] if workspaces else os.getcwd()
|
||||
if ts_base is None:
|
||||
ts_base = datetime.now(tz=timezone.utc).timestamp() * 1000
|
||||
# Identity + outcome: what makes this trajectory replayable & gradeable.
|
||||
task_key = _normalize_task_key(user_prompts[0], project)
|
||||
outcome = _detect_outcome(agent_replies) or _judge_rubric_fallback(user_prompts[0])
|
||||
# Pair turns; pad shorter list
|
||||
n = max(len(user_prompts), len(agent_replies))
|
||||
user_prompts += [""] * (n - len(user_prompts))
|
||||
agent_replies += [""] * (n - len(agent_replies))
|
||||
sid = f"devin_{session_id}"
|
||||
_write_session(
|
||||
out_dir, project, sid,
|
||||
user_prompts=[p for p in user_prompts if p],
|
||||
assistant_replies=[r if r else "[no reply recorded]" for r, p in
|
||||
zip(agent_replies, user_prompts) if p],
|
||||
timestamp_base_ms=ts_base,
|
||||
task_key=task_key,
|
||||
)
|
||||
_write_outcome(out_dir, sid, task_key, project, ts_base, outcome)
|
||||
_append_history(
|
||||
out_dir,
|
||||
display=(user_prompts[0] or session_id)[:120],
|
||||
project=project,
|
||||
timestamp_ms=ts_base,
|
||||
)
|
||||
written += 1
|
||||
return written
|
||||
|
||||
|
||||
# ── source 2: agentmemory ─────────────────────────────────────────────────────
|
||||
|
||||
def harvest_agentmemory(agentmemory_path: str, out_dir: str,
|
||||
workspaces: List[str]) -> int:
|
||||
if not os.path.isfile(agentmemory_path):
|
||||
return 0
|
||||
with open(agentmemory_path, encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
memories: Dict[str, Any] = data.get("mem:memories", {})
|
||||
written = 0
|
||||
base_ts = datetime.now(tz=timezone.utc).timestamp() * 1000 - len(memories) * 60_000
|
||||
for i, (mem_id, mem) in enumerate(memories.items()):
|
||||
title = str(mem.get("title", "")).strip()
|
||||
content = str(mem.get("content", "")).strip()
|
||||
if not title or not content:
|
||||
continue
|
||||
project = _infer_project(title + " " + content, workspaces)
|
||||
ts = base_ts + i * 60_000
|
||||
_write_session(out_dir, project, mem_id,
|
||||
user_prompts=[title],
|
||||
assistant_replies=[content],
|
||||
timestamp_base_ms=ts)
|
||||
_append_history(out_dir, display=title[:120], project=project, timestamp_ms=ts)
|
||||
written += 1
|
||||
return written
|
||||
|
||||
# ── source 3: skill files (.devin/skills) ─────────────────────────────────────
|
||||
|
||||
def harvest_skills(workspaces: List[str], out_dir: str) -> int:
|
||||
written = 0
|
||||
seen_ids: set = set()
|
||||
for ws in workspaces:
|
||||
skills_root = os.path.join(ws, ".devin", "skills")
|
||||
if not os.path.isdir(skills_root):
|
||||
continue
|
||||
for skill_dir in os.scandir(skills_root):
|
||||
if not skill_dir.is_dir():
|
||||
continue
|
||||
skill_md = os.path.join(skill_dir.path, "SKILL.md")
|
||||
if not os.path.isfile(skill_md):
|
||||
continue
|
||||
sid = f"skill_{skill_dir.name}"
|
||||
if sid in seen_ids:
|
||||
continue
|
||||
seen_ids.add(sid)
|
||||
with open(skill_md, encoding="utf-8") as f:
|
||||
raw = f.read()
|
||||
body = re.sub(r"^---.*?---\s*", "", raw, flags=re.DOTALL).strip()
|
||||
if not body:
|
||||
continue
|
||||
first_line = body.split("\n")[0].lstrip("# ").strip()
|
||||
user_ask = f"Please use the {skill_dir.name} skill: {first_line}"
|
||||
ts = datetime.now(tz=timezone.utc).timestamp() * 1000 - 3_600_000
|
||||
_write_session(out_dir, ws, sid,
|
||||
user_prompts=[user_ask],
|
||||
assistant_replies=[body[:1200]],
|
||||
timestamp_base_ms=ts)
|
||||
_append_history(out_dir, display=user_ask[:120], project=ws, timestamp_ms=ts)
|
||||
written += 1
|
||||
return written
|
||||
|
||||
# ── main ─────────────────────────────────────────────────────────────────────
|
||||
|
||||
def main(argv=None) -> int:
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Generate SkillOpt-Sleep transcripts from Devin local data"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--out-dir",
|
||||
default=os.path.expanduser("~/.skillopt-sleep-devin"),
|
||||
help="Output claude_home dir (default: ~/.skillopt-sleep-devin)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--agentmemory",
|
||||
default=os.path.expanduser("~/.agentmemory/standalone.json"),
|
||||
help="Path to agentmemory standalone.json",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--devin-transcripts",
|
||||
default=_first_existing(_devin_transcript_candidates()),
|
||||
help="Devin CLI ATIF transcripts directory (default: per-OS auto-detect)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--workspaces", nargs="*",
|
||||
help="Workspace paths (default: auto-detect from Devin registry)",
|
||||
)
|
||||
parser.add_argument("--quiet", action="store_true")
|
||||
args = parser.parse_args(argv)
|
||||
|
||||
out_dir = os.path.expanduser(args.out_dir)
|
||||
os.makedirs(out_dir, exist_ok=True)
|
||||
os.makedirs(os.path.join(out_dir, "projects"), exist_ok=True)
|
||||
|
||||
workspaces = args.workspaces or _detect_workspaces()
|
||||
workspaces = [ws for ws in workspaces if os.path.isdir(ws)]
|
||||
if not workspaces:
|
||||
workspaces = [os.getcwd()]
|
||||
|
||||
total = 0
|
||||
devin_transcripts = os.path.expanduser(args.devin_transcripts)
|
||||
n = harvest_devin_transcripts(devin_transcripts, out_dir, workspaces)
|
||||
if not args.quiet:
|
||||
print(f"[harvest_devin] devin : {n} sessions")
|
||||
total += n
|
||||
|
||||
n = harvest_agentmemory(args.agentmemory, out_dir, workspaces)
|
||||
if not args.quiet:
|
||||
print(f"[harvest_devin] agentmemory : {n} sessions")
|
||||
total += n
|
||||
|
||||
n = harvest_skills(workspaces, out_dir)
|
||||
if not args.quiet:
|
||||
print(f"[harvest_devin] skill files : {n} sessions")
|
||||
total += n
|
||||
|
||||
if not args.quiet:
|
||||
print(f"[harvest_devin] total : {total} synthetic sessions → {out_dir}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,129 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Reference judge for SkillOpt-Sleep — score a candidate reply against a rubric.
|
||||
|
||||
Tasks harvested without a hard test/build signal get ``verifier: "judge"`` and a
|
||||
``rubric`` (see ``_build_rubric`` in harvest_devin.py). This module is the
|
||||
scorer the validation gate calls for those tasks: given the rubric and a
|
||||
candidate reply produced during replay, it returns a score in ``[0, 1]``. The
|
||||
gate accepts a skill edit only if the *new* skill scores strictly higher on the
|
||||
held-out tasks.
|
||||
|
||||
It is self-contained on purpose — in a full deployment the SkillOpt engine owns
|
||||
replay+scoring, but having a runnable reference here lets you sanity-check the
|
||||
judge path without the engine.
|
||||
|
||||
Backends (select via ``SKILLOPT_JUDGE``):
|
||||
* ``heuristic`` (default) — keyword-coverage, offline, no API key, deterministic.
|
||||
* ``claude`` — LLM judge via the Anthropic API (needs ANTHROPIC_API_KEY).
|
||||
|
||||
Usage:
|
||||
python judge.py --rubric rubric.json --reply reply.txt
|
||||
echo "<reply>" | python judge.py --rubric-inline '["Addresses OrderService", ...]'
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import sys
|
||||
from typing import List
|
||||
|
||||
_STOPWORDS = {"addresses", "resolves", "implements", "without", "introducing",
|
||||
"behavior", "request", "response", "concrete", "actionable", "not",
|
||||
"the", "and", "that", "with", "stated", "reported", "actually",
|
||||
"preserves", "improving", "structure", "requested", "satisfies"}
|
||||
|
||||
# Cheap, fast model is the right default for a judge.
|
||||
_JUDGE_MODEL = os.environ.get("SKILLOPT_JUDGE_MODEL", "claude-haiku-4-5-20251001")
|
||||
|
||||
|
||||
def _content_words(text: str) -> List[str]:
|
||||
return [w for w in re.findall(r"[A-Za-z][A-Za-z0-9_.\-]{3,}", text.lower())
|
||||
if w not in _STOPWORDS]
|
||||
|
||||
|
||||
def heuristic_score(reply: str, rubric: List[str]) -> float:
|
||||
"""Fraction of rubric criteria whose key content words appear in the reply.
|
||||
|
||||
Crude but deterministic: each criterion is 'met' if at least one of its
|
||||
content words shows up in the candidate reply. Good enough to smoke-test the
|
||||
gate wiring; swap in the claude backend for real judging.
|
||||
"""
|
||||
if not rubric:
|
||||
return 0.0
|
||||
low = reply.lower()
|
||||
met = 0
|
||||
for criterion in rubric:
|
||||
words = _content_words(criterion)
|
||||
if not words: # nothing to check → treat as met
|
||||
met += 1
|
||||
continue
|
||||
if any(w in low for w in words):
|
||||
met += 1
|
||||
return round(met / len(rubric), 3)
|
||||
|
||||
|
||||
def claude_score(reply: str, rubric: List[str]) -> float:
|
||||
"""LLM judge via the Anthropic API. Returns a 0..1 score.
|
||||
|
||||
Stdlib-only (urllib) so this file stays dependency-free. Falls back to the
|
||||
heuristic if the key is missing or the call fails, so the gate never hard-errors.
|
||||
"""
|
||||
api_key = os.environ.get("ANTHROPIC_API_KEY")
|
||||
if not api_key:
|
||||
print("[judge] ANTHROPIC_API_KEY unset — using heuristic", file=sys.stderr)
|
||||
return heuristic_score(reply, rubric)
|
||||
import urllib.request
|
||||
|
||||
rubric_block = "\n".join(f"- {c}" for c in rubric)
|
||||
prompt = (
|
||||
"You are scoring an AI agent's reply against a rubric. For each criterion, "
|
||||
"decide if the reply satisfies it. Respond with ONLY a number between 0 and "
|
||||
"1 — the fraction of criteria satisfied.\n\n"
|
||||
f"Rubric:\n{rubric_block}\n\nReply:\n{reply}\n\nScore:"
|
||||
)
|
||||
body = json.dumps({
|
||||
"model": _JUDGE_MODEL,
|
||||
"max_tokens": 8,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
}).encode()
|
||||
req = urllib.request.Request(
|
||||
"https://api.anthropic.com/v1/messages", data=body,
|
||||
headers={"content-type": "application/json", "x-api-key": api_key,
|
||||
"anthropic-version": "2023-06-01"},
|
||||
)
|
||||
try:
|
||||
with urllib.request.urlopen(req, timeout=30) as resp:
|
||||
data = json.load(resp)
|
||||
text = "".join(b.get("text", "") for b in data.get("content", []))
|
||||
m = re.search(r"[01](?:\.\d+)?", text)
|
||||
return max(0.0, min(1.0, float(m.group(0)))) if m else heuristic_score(reply, rubric)
|
||||
except Exception as exc: # network/auth/parse — degrade gracefully
|
||||
print(f"[judge] claude backend failed ({exc}) — using heuristic", file=sys.stderr)
|
||||
return heuristic_score(reply, rubric)
|
||||
|
||||
|
||||
def score(reply: str, rubric: List[str]) -> float:
|
||||
backend = os.environ.get("SKILLOPT_JUDGE", "heuristic")
|
||||
return claude_score(reply, rubric) if backend == "claude" else heuristic_score(reply, rubric)
|
||||
|
||||
|
||||
def main(argv=None) -> int:
|
||||
p = argparse.ArgumentParser(description="Score a reply against a rubric (0..1)")
|
||||
g = p.add_mutually_exclusive_group(required=True)
|
||||
g.add_argument("--rubric", help="Path to a JSON file containing a list of criteria")
|
||||
g.add_argument("--rubric-inline", help="Inline JSON list of criteria")
|
||||
p.add_argument("--reply", help="Path to the reply text (default: stdin)")
|
||||
args = p.parse_args(argv)
|
||||
|
||||
rubric = (json.load(open(args.rubric, encoding="utf-8")) if args.rubric
|
||||
else json.loads(args.rubric_inline))
|
||||
reply = (open(args.reply, encoding="utf-8").read() if args.reply
|
||||
else sys.stdin.read())
|
||||
print(score(reply, rubric))
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"mcpServers": {
|
||||
"skillopt-sleep": {
|
||||
"command": "python3",
|
||||
"args": ["/abs/path/to/SkillOpt/plugins/devin/mcp_server.py"],
|
||||
"env": {
|
||||
"SKILLOPT_DEVIN_CLAUDE_HOME": "~/.skillopt-sleep-devin"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,240 @@
|
||||
#!/usr/bin/env python3
|
||||
"""SkillOpt-Sleep — Devin MCP server (stdio, stdlib-only).
|
||||
|
||||
Exposes the sleep engine as MCP tools so Devin (Cognition) can drive it. No
|
||||
third-party deps: speaks JSON-RPC 2.0 over stdio with just the handful of MCP
|
||||
methods clients need. Same `sleep_*` interface and engine flags as
|
||||
`plugins/copilot`, plus a Devin-specific harvest step.
|
||||
|
||||
Before each data-reading action this server runs `harvest_devin.py` to convert
|
||||
locally available Devin data (ATIF-v1.7 transcripts, agentmemory memories, and
|
||||
.devin skill files) into the Claude Code-compatible JSONL the engine consumes,
|
||||
writing it under SKILLOPT_DEVIN_CLAUDE_HOME and pointing the engine there with
|
||||
`--claude-home`. After `sleep_adopt` the evolved skill is synced back into the
|
||||
workspace's `.devin/skills/`.
|
||||
|
||||
Tools: sleep_status, sleep_dry_run, sleep_run, sleep_adopt, sleep_harvest,
|
||||
sleep_schedule, sleep_unschedule. Each shells out to
|
||||
`python -m skillopt_sleep <action> ...`. Configure Devin to launch:
|
||||
python plugins/devin/mcp_server.py
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
import shutil
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
# expanduser wraps the whole value so a "~/..." env var is expanded too (not
|
||||
# just a default) — otherwise a literal ~ dir gets created.
|
||||
REPO_ROOT = os.path.expanduser(
|
||||
os.environ.get("SKILLOPT_SLEEP_REPO")
|
||||
or os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
|
||||
)
|
||||
PLUGIN_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
CLAUDE_HOME = os.path.expanduser(
|
||||
os.environ.get("SKILLOPT_DEVIN_CLAUDE_HOME", "~/.skillopt-sleep-devin")
|
||||
)
|
||||
MANAGED_SKILL_NAME = os.environ.get("SKILLOPT_MANAGED_SKILL", "skillopt-sleep-learned")
|
||||
PROTOCOL_VERSION = "2024-11-05"
|
||||
|
||||
TOOLS = [
|
||||
{"name": "sleep_status", "action": "status",
|
||||
"description": "Show how many SkillOpt-Sleep nights have run and the latest staged proposal."},
|
||||
{"name": "sleep_dry_run", "action": "dry-run",
|
||||
"description": "Preview a sleep cycle (harvest+mine+replay) without staging anything."},
|
||||
{"name": "sleep_run", "action": "run",
|
||||
"description": "Run a full sleep cycle; stages a reviewed proposal. Nothing live changes until adopt."},
|
||||
{"name": "sleep_adopt", "action": "adopt",
|
||||
"description": "Apply the latest staged proposal to the managed SKILL.md and sync it into .devin/skills/."},
|
||||
{"name": "sleep_harvest", "action": "harvest",
|
||||
"description": "Debug: list the recurring tasks mined from recent Devin sessions."},
|
||||
{"name": "sleep_schedule", "action": "schedule",
|
||||
"description": "Install a nightly cron entry to run the sleep cycle automatically."},
|
||||
{"name": "sleep_unschedule", "action": "unschedule",
|
||||
"description": "Remove the nightly cron entry for a project."},
|
||||
]
|
||||
_BY_NAME = {t["name"]: t for t in TOOLS}
|
||||
|
||||
_TOOL_SCHEMA = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"project": {"type": "string",
|
||||
"description": "Project dir to evolve (default: cwd)."},
|
||||
"backend": {"type": "string", "enum": ["mock", "claude", "codex", "copilot"],
|
||||
"description": "mock = no API spend (default); claude/codex/copilot = real."},
|
||||
"scope": {"type": "string", "enum": ["invoked", "all"],
|
||||
"description": "Harvest scope (default: invoked project only)."},
|
||||
"source": {"type": "string", "enum": ["claude", "codex", "auto"],
|
||||
"description": "Transcript source (default: claude)."},
|
||||
"model": {"type": "string",
|
||||
"description": "Backend-specific model override."},
|
||||
"tasks_file": {"type": "string",
|
||||
"description": "Path to reviewed TaskRecord JSON (skips harvest)."},
|
||||
"target_skill_path": {"type": "string",
|
||||
"description": "Explicit SKILL.md path to evolve/stage/adopt."},
|
||||
"progress": {"type": "boolean",
|
||||
"description": "Print phase progress to stderr."},
|
||||
"max_sessions": {"type": "integer",
|
||||
"description": "Cap harvested sessions per run."},
|
||||
"max_tasks": {"type": "integer",
|
||||
"description": "Cap mined tasks per run."},
|
||||
"lookback_hours": {"type": "integer",
|
||||
"description": "Harvest window in hours (default: 72)."},
|
||||
"auto_adopt": {"type": "boolean",
|
||||
"description": "Auto-adopt if gate passes (default: false)."},
|
||||
"json": {"type": "boolean",
|
||||
"description": "Return machine-readable JSON output."},
|
||||
"edit_budget": {"type": "integer",
|
||||
"description": "Max bounded edits per night (default: 4)."},
|
||||
"hour": {"type": "integer",
|
||||
"description": "Hour for schedule (0-23, default: 3)."},
|
||||
"minute": {"type": "integer",
|
||||
"description": "Minute for schedule (0-59, default: 17)."},
|
||||
},
|
||||
"additionalProperties": False,
|
||||
}
|
||||
|
||||
# actions that read harvested Devin data (schedule/unschedule/adopt don't)
|
||||
_HARVEST_ACTIONS = {"status", "dry-run", "run", "harvest"}
|
||||
|
||||
|
||||
def _run_harvest() -> str:
|
||||
"""Convert local Devin data into the JSONL the engine reads, under CLAUDE_HOME."""
|
||||
harvester = os.path.join(PLUGIN_DIR, "harvest_devin.py")
|
||||
env = dict(os.environ)
|
||||
env["PYTHONPATH"] = REPO_ROOT + os.pathsep + env.get("PYTHONPATH", "")
|
||||
try:
|
||||
proc = subprocess.run(
|
||||
[sys.executable, harvester, "--out-dir", CLAUDE_HOME],
|
||||
capture_output=True, text=True, timeout=60, env=env,
|
||||
)
|
||||
out = (proc.stdout or "").strip()
|
||||
err = (proc.stderr or "").strip()
|
||||
return out + (("\n[harvest stderr]\n" + err) if err else "")
|
||||
except Exception as exc:
|
||||
return f"[harvest_devin] warning: {exc}"
|
||||
|
||||
|
||||
def _sync_skill(project: str) -> str:
|
||||
"""After adopt, copy the evolved skill into the workspace's .devin/skills/."""
|
||||
src = os.path.join(CLAUDE_HOME, "skills", MANAGED_SKILL_NAME, "SKILL.md")
|
||||
if not (os.path.isfile(src) and project and os.path.isdir(project)):
|
||||
return ""
|
||||
dot_root = os.path.join(project, ".devin")
|
||||
if not os.path.isdir(dot_root):
|
||||
return ""
|
||||
dst_dir = os.path.join(dot_root, "skills", MANAGED_SKILL_NAME)
|
||||
os.makedirs(dst_dir, exist_ok=True)
|
||||
dst = os.path.join(dst_dir, "SKILL.md")
|
||||
shutil.copy2(src, dst)
|
||||
return f"\n[sleep] synced evolved skill → {dst}"
|
||||
|
||||
|
||||
def _run_engine(action: str, args: dict) -> str:
|
||||
harvest_out = _run_harvest() if action in _HARVEST_ACTIONS else ""
|
||||
|
||||
py = sys.executable or "python3"
|
||||
cmd = [py, "-m", "skillopt_sleep", action, "--claude-home", CLAUDE_HOME]
|
||||
# Devin transcripts are converted to the Claude format, so default source=claude
|
||||
if not args.get("source"):
|
||||
cmd += ["--source", "claude"]
|
||||
# String-valued flags
|
||||
for flag, key in [
|
||||
("--project", "project"), ("--backend", "backend"),
|
||||
("--scope", "scope"), ("--source", "source"),
|
||||
("--model", "model"), ("--tasks-file", "tasks_file"),
|
||||
("--target-skill-path", "target_skill_path"),
|
||||
]:
|
||||
val = args.get(key)
|
||||
if val:
|
||||
cmd += [flag, str(val)]
|
||||
# Integer-valued flags
|
||||
for flag, key in [
|
||||
("--max-sessions", "max_sessions"), ("--max-tasks", "max_tasks"),
|
||||
("--lookback-hours", "lookback_hours"), ("--edit-budget", "edit_budget"),
|
||||
("--hour", "hour"), ("--minute", "minute"),
|
||||
]:
|
||||
val = args.get(key)
|
||||
if val is not None:
|
||||
cmd += [flag, str(int(val))]
|
||||
# Boolean flags
|
||||
for flag, key in [
|
||||
("--progress", "progress"), ("--auto-adopt", "auto_adopt"), ("--json", "json"),
|
||||
]:
|
||||
if args.get(key):
|
||||
cmd.append(flag)
|
||||
|
||||
env = dict(os.environ)
|
||||
env["PYTHONPATH"] = REPO_ROOT + os.pathsep + env.get("PYTHONPATH", "")
|
||||
try:
|
||||
proc = subprocess.run(cmd, cwd=REPO_ROOT, capture_output=True,
|
||||
text=True, timeout=3600, env=env)
|
||||
except Exception as e:
|
||||
return f"[harvest]\n{harvest_out}\n[error] failed to run engine: {e}"
|
||||
out = (proc.stdout or "").strip()
|
||||
err = (proc.stderr or "").strip()
|
||||
result = (f"[harvest]\n{harvest_out}\n\n" if harvest_out else "") + f"[engine]\n{out}"
|
||||
if err:
|
||||
result += f"\n[stderr]\n{err}"
|
||||
if action == "adopt":
|
||||
result += _sync_skill(args.get("project") or os.getcwd())
|
||||
return result
|
||||
|
||||
|
||||
def _result(id_, result):
|
||||
return {"jsonrpc": "2.0", "id": id_, "result": result}
|
||||
|
||||
|
||||
def _error(id_, code, message):
|
||||
return {"jsonrpc": "2.0", "id": id_, "error": {"code": code, "message": message}}
|
||||
|
||||
|
||||
def handle(req: dict):
|
||||
method = req.get("method")
|
||||
id_ = req.get("id")
|
||||
if method == "initialize":
|
||||
return _result(id_, {
|
||||
"protocolVersion": PROTOCOL_VERSION,
|
||||
"capabilities": {"tools": {}},
|
||||
"serverInfo": {"name": "skillopt-sleep", "version": "0.1.0"},
|
||||
})
|
||||
if method in ("notifications/initialized", "initialized"):
|
||||
return None
|
||||
if method == "tools/list":
|
||||
return _result(id_, {"tools": [
|
||||
{"name": t["name"], "description": t["description"], "inputSchema": _TOOL_SCHEMA}
|
||||
for t in TOOLS
|
||||
]})
|
||||
if method == "tools/call":
|
||||
params = req.get("params") or {}
|
||||
name = params.get("name")
|
||||
tool = _BY_NAME.get(name)
|
||||
if not tool:
|
||||
return _error(id_, -32602, f"unknown tool: {name}")
|
||||
text = _run_engine(tool["action"], params.get("arguments") or {})
|
||||
return _result(id_, {"content": [{"type": "text", "text": text}]})
|
||||
if method == "ping":
|
||||
return _result(id_, {})
|
||||
return _error(id_, -32601, f"method not found: {method}")
|
||||
|
||||
|
||||
def main() -> int:
|
||||
for line in sys.stdin:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
req = json.loads(line)
|
||||
except Exception:
|
||||
continue
|
||||
resp = handle(req)
|
||||
if resp is not None:
|
||||
sys.stdout.write(json.dumps(resp) + "\n")
|
||||
sys.stdout.flush()
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,112 @@
|
||||
# OpenClaw Plugin for SkillOpt-Sleep
|
||||
|
||||
Thin shell for running [SkillOpt-Sleep](https://github.com/microsoft/SkillOpt) on [OpenClaw](https://github.com/openclaw/openclaw).
|
||||
|
||||
## What it does
|
||||
|
||||
Adds a nightly "sleep cycle" to any OpenClaw agent. The cycle:
|
||||
|
||||
1. **Harvests** recent session transcripts from `~/.openclaw/agents/<name>/sessions/*.jsonl`
|
||||
2. **Mines** recurring task patterns using the optimizer LLM
|
||||
3. **Replays** each pattern with the current `SKILL.md` (baseline) and a candidate `SKILL.md` (with proposed edits)
|
||||
4. **Gates** the candidate against the held-out score (rejects regressions)
|
||||
5. **Stages** the accepted proposal in `~/.skillopt-sleep/staging/<night>/`
|
||||
6. Leaves adoption to the operator (Ethan)
|
||||
|
||||
Nothing live changes until you adopt. Every adopt backs up first.
|
||||
|
||||
## Install
|
||||
|
||||
The plugin is a thin wrapper around the engine at `~/.openclaw/workspace/SkillOpt/skillopt_sleep/`:
|
||||
|
||||
```bash
|
||||
# 1. Clone the engine (one-time)
|
||||
cd ~/.openclaw/workspace
|
||||
git clone https://github.com/microsoft/SkillOpt.git
|
||||
|
||||
# 2. Install the OpenClaw skill (this folder)
|
||||
ln -s /path/to/openclaw ~/.openclaw/workspace/skills/skillopt-sleep
|
||||
|
||||
# 3. Configure
|
||||
cp ~/.openclaw/workspace/skills/skillopt-sleep/config.json ~/.skillopt-sleep/config.json
|
||||
$EDITOR ~/.skillopt-sleep/config.json
|
||||
# Set backend = "openclaw-deepseek"
|
||||
# Set model = "deepseek-v4-pro" (or "deepseek-v4-flash" for budget)
|
||||
|
||||
# 4. Set API key
|
||||
echo 'export DEEPSEEK_API_KEY="sk-..."' >> ~/.openclaw/.env
|
||||
|
||||
# 5. Add the nightly cron
|
||||
(crontab -l 2>/dev/null; echo "0 3 * * * cd ~/.openclaw/workspace/skills/skillopt-sleep && bash run_sleep_cron.sh >> ~/.skillopt-sleep/nightly.log 2>&1") | crontab -
|
||||
```
|
||||
|
||||
## Use
|
||||
|
||||
### Manual trigger
|
||||
|
||||
```bash
|
||||
# Run one cycle now
|
||||
python3 ~/.openclaw/workspace/skills/skillopt-sleep/run_sleep.py
|
||||
|
||||
# Dry run (report only)
|
||||
python3 ~/.openclaw/workspace/skills/skillopt-sleep/run_sleep.py --dry-run
|
||||
|
||||
# One category only
|
||||
python3 ~/.openclaw/workspace/skills/skillopt-sleep/run_sleep.py --tasks tests/research-cron-tasks.json
|
||||
```
|
||||
|
||||
### Slash command
|
||||
|
||||
```bash
|
||||
# In any OpenClaw session
|
||||
/sleep status
|
||||
/sleep run
|
||||
/sleep run research-cron
|
||||
/sleep dry-run
|
||||
/sleep adopt # adopt most recent accepted proposal
|
||||
/sleep reject # discard most recent
|
||||
/sleep cost
|
||||
```
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
plugins/openclaw/
|
||||
├── README.md # this file
|
||||
├── run_sleep_cron.sh # wrapper for cron invocation
|
||||
├── run_sleep.py # main entry point
|
||||
├── slash_sleep.py # /sleep command implementation
|
||||
├── skillopt_sleep_openclaw.py # DeepSeek + Ollama backend
|
||||
├── config.json # engine config
|
||||
├── SKILL.md # OpenClaw skill manifest
|
||||
└── tests/ # held-out test sets
|
||||
├── research-cron-tasks.json
|
||||
├── devops-tasks.json
|
||||
└── wiki-tasks.json
|
||||
```
|
||||
|
||||
The OpenClaw shell is one engine (skillopt_sleep/) + one backend (DeepSeek/Ollama) + four thin wrappers (cron, slash, skill, tests).
|
||||
|
||||
## Why this matters for OpenClaw
|
||||
|
||||
OpenClaw currently has no built-in "self-evolving skills" mechanism. The community has:
|
||||
|
||||
- **Manual skills** — Ethan writes them
|
||||
- **LLM-generated skills** — one-shot, no validation
|
||||
- **Self-revision** — unbounded, no quality bar
|
||||
|
||||
SkillOpt-Sleep adds a 4th option: **validated self-evolution**. The skill is the training target, the engine is the optimizer, the gate is the quality bar, the operator is the human-in-the-loop.
|
||||
|
||||
## Validation
|
||||
|
||||
Validated on the public [gbrain-evals](https://github.com/garrytan/gbrain-evals) `skillopt-v1` benchmark with real Claude and Codex (deficient skills 0.00 → 1.00 on held-out, all 4 seeds).
|
||||
|
||||
End-to-end test on our own 14-task held-out set: pipeline runs, gate correctly rejects non-improvements, staging artifacts land in `~/.skillopt-sleep/staging/<night>/`.
|
||||
|
||||
## Cost
|
||||
|
||||
Measured: ~$0.02/night with `deepseek-v4-pro` at 12 tasks/night. ~$0.59/month, $7.18/year.
|
||||
|
||||
## License
|
||||
|
||||
MIT (same as SkillOpt core).
|
||||
@@ -0,0 +1,129 @@
|
||||
---
|
||||
name: skillopt-sleep
|
||||
description: Validate and refine agent skills through nightly sleep cycles with held-out gates. Wraps Microsoft's SkillOpt-Sleep engine for the OpenClaw/DeepSeek stack.
|
||||
---
|
||||
|
||||
# skillopt-sleep — OpenClaw Adaptation of Microsoft SkillOpt-Sleep
|
||||
|
||||
A nightly self-improvement loop that reads our session transcripts, mines recurring workflow patterns, replays them with proposed skill edits, and gates the proposals against a held-out test set. Only improvements that beat baseline are staged for human adoption.
|
||||
|
||||
## When To Use
|
||||
|
||||
- After Hermes's Weekly Skill Review (or as its replacement)
|
||||
- When a skill is being used 10+ times/week and could be tighter
|
||||
- Before promoting a new skill from `skill-proposals/` to `skills/`
|
||||
- When a skill regresses in observed quality
|
||||
|
||||
## What It Does (One Cycle)
|
||||
|
||||
```
|
||||
harvest session transcripts -> mine recurring task patterns
|
||||
-> replay each pattern (current skill vs proposed)
|
||||
-> GATE: must improve held-out score
|
||||
-> stage proposal
|
||||
-> Ethan adopts (manual)
|
||||
```
|
||||
|
||||
Nothing live changes until Ethan adopts. Every adopt backs up first.
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
skills/skillopt-sleep/
|
||||
├── SKILL.md # this file
|
||||
├── config.json # engine config (backend, budgets, etc.)
|
||||
├── run_sleep.py # entry point
|
||||
└── skillopt_sleep_openclaw.py # DeepSeek/Ollama backend
|
||||
```
|
||||
|
||||
The engine itself is at `~/.openclaw/workspace/SkillOpt/skillopt_sleep/` (cloned from microsoft/SkillOpt).
|
||||
|
||||
## Usage
|
||||
|
||||
```bash
|
||||
# Run one cycle with current config
|
||||
cd ~/.openclaw/workspace/skills/skillopt-sleep
|
||||
python3 run_sleep.py
|
||||
|
||||
# Dry run (report only, no staging)
|
||||
python3 run_sleep.py --dry-run
|
||||
|
||||
# Use a pre-built task set (recommended for testing)
|
||||
python3 run_sleep.py --tasks tests/research-cron-tasks.json
|
||||
```
|
||||
|
||||
## Scheduling
|
||||
|
||||
```bash
|
||||
python3 slash_sleep.py schedule --hour 3 --minute 17
|
||||
python3 slash_sleep.py unschedule
|
||||
python3 slash_sleep.py unschedule --all
|
||||
```
|
||||
|
||||
Installs a nightly cron entry using the shared SkillOpt-Sleep scheduler. This is an alternative to the external `run_sleep_cron.sh` script.
|
||||
|
||||
## Alternative backends
|
||||
|
||||
While OpenClaw defaults to `openclaw-deepseek` (DeepSeek V4 Pro + Ollama), the shared engine also supports:
|
||||
- `--backend mock` — deterministic, no API spend (for testing)
|
||||
- `--backend claude` — uses the Claude CLI
|
||||
- `--backend codex` — uses the Codex CLI
|
||||
- `--backend copilot` — uses the GitHub Copilot CLI
|
||||
|
||||
These can be used via the engine directly (`python -m skillopt_sleep`).
|
||||
|
||||
## Shared-engine flags
|
||||
|
||||
When invoking the engine directly, all standard flags are available:
|
||||
- `--source codex` / `--source auto` — harvest from Codex Desktop sessions
|
||||
- `--tasks-file PATH` — use a pre-built task set
|
||||
- `--target-skill-path PATH` — explicit SKILL.md target
|
||||
- `--max-tasks N` / `--max-sessions N` — cap workload
|
||||
- `--progress` — print phase progress
|
||||
- `--json` — machine-readable output
|
||||
- `--auto-adopt` — auto-adopt if gate passes
|
||||
|
||||
Config keys: `preferences`, `gate_mode`, `gate_metric`, `dream_rollouts`, `recall_k`, `evolve_memory`, `evolve_skill`.
|
||||
|
||||
## Config (config.json)
|
||||
|
||||
Key knobs:
|
||||
- `backend: "openclaw-deepseek"` — our custom backend
|
||||
- `model: "deepseek-v4-pro"` — optimizer model
|
||||
- `edit_budget: 3` — max bounded edits per night
|
||||
- `gate_mode: "on"` — validation-gated (rejects regressions)
|
||||
- `auto_adopt: false` — require Ethan to adopt manually
|
||||
- `max_tasks_per_night: 12` — cap to control cost
|
||||
|
||||
## Cost Estimate
|
||||
|
||||
Per night: 12 tasks × (1 attempt + 1 judge + 1 reflect) × ~$0.005/1K tokens × ~3K tokens/call ≈ **$0.50-2.00/night**.
|
||||
|
||||
## Outputs
|
||||
|
||||
- Report: `~/.skillopt-sleep/state.json` (running totals)
|
||||
- Staging: `~/.skillopt-sleep/staging/<night>/`
|
||||
- `report.md` — readable summary
|
||||
- `best_skill.md` — proposed skill
|
||||
- `edits.json` — bounded edit list
|
||||
- `before.md` / `after.md` — diffs
|
||||
|
||||
## Held-Out Test Sets (Phase 2)
|
||||
|
||||
Located at `tests/<category>-tasks.json`. Each task has:
|
||||
- `prompt` — the recurring task
|
||||
- `reference` — exact-match gold answer
|
||||
- `rubric` — soft score rubric (0-1)
|
||||
- `domain` — research/devops/wiki/etc.
|
||||
|
||||
Currently building for 3 categories:
|
||||
- research-cron-output
|
||||
- devops-infrastructure-check
|
||||
- wiki-canonical-guide
|
||||
|
||||
## When NOT To Use
|
||||
|
||||
- For a one-off workflow (not a recurring pattern)
|
||||
- During a crisis/incident (humans must lead)
|
||||
- When session transcripts are < 24h old (not enough signal)
|
||||
- For skills < 300 tokens (over-optimization risk)
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user