chore: import upstream snapshot with attribution
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# SkillOpt-Sleep (Claude Code plugin)
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> Give your local Claude agent a **sleep cycle**. Every night it reviews your
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> past sessions offline, replays your recurring tasks on your own API budget,
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> and consolidates what it learns into **validated** memory (`CLAUDE.md`) and
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> skills (`SKILL.md`). Your agent gets better the more you use it — no
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> model-weight training.
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SkillOpt-Sleep is the **deployment-time** companion to
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[SkillOpt](https://github.com/microsoft/SkillOpt). SkillOpt trains a skill
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offline on a benchmark; SkillOpt-Sleep applies the same discipline to *your own
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daily usage*: bounded text edits, accepted only through a held-out validation
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gate, with rejected edits kept as negative feedback.
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It synthesizes three ideas:
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| Idea | Contribution |
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|---|---|
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| **SkillOpt** | skill/memory = trainable text; bounded add/delete/replace edits; **held-out gate** keeps only changes that help. |
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| **Claude Dreams** | offline consolidation over past sessions; input never mutated; output **reviewed then adopted**. |
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| **Agent sleep** | periodic offline replay turns short-term episodes into long-term skill. |
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## What it does (one "night")
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```
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harvest ~/.claude transcripts → mine recurring tasks → replay offline
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→ consolidate (reflect → bounded edit → GATE) → stage proposal → (you) adopt
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```
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Nothing live is modified until **you** run `/skillopt-sleep adopt` (the Dreams "review,
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then adopt or discard" contract). Every adopt backs up the prior file first.
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## Install
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**Requirements:** Python ≥ 3.10, and the `claude` CLI (and/or `codex` CLI) on PATH.
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```bash
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# 1) get the code (the plugin ships inside the SkillOpt repo)
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git clone https://github.com/microsoft/SkillOpt.git
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cd SkillOpt
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# 2) add the plugin to Claude Code as a local marketplace
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/plugin marketplace add ./skillopt-sleep-plugin
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/plugin install skillopt-sleep@skillopt-sleep
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# 3) verify
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/skillopt-sleep status
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```
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The plugin's bundled runner (`scripts/sleep.sh`) auto-selects a Python ≥ 3.10
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interpreter and calls the `skillopt_sleep` engine in the repo. No `pip install`
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is required for the default `mock` backend or for `claude`/`codex` backends —
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they shell out to the CLIs you already have.
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## Quick start
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```bash
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# from inside any project you use with Claude Code:
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/skillopt-sleep dry-run # safe preview: what it would learn, no changes staged
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/skillopt-sleep run # full cycle: stages a reviewed proposal (still no live edits)
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/skillopt-sleep status # see history + the latest staged proposal
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/skillopt-sleep adopt # apply the staged proposal to CLAUDE.md / SKILL.md (with backup)
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/skillopt-sleep-handoff run # same cycle, but THIS session answers the model calls
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# (no claude -p subprocess, no API key — subscription-friendly)
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```
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Or call the engine directly (Python ≥ 3.10):
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```bash
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python -m skillopt_sleep run --project "$(pwd)" --scope invoked --backend mock
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python -m skillopt_sleep run --project "$(pwd)" --backend claude # real lift via Claude
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python -m skillopt_sleep run --project "$(pwd)" --backend codex # real lift via Codex
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```
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Default backend is **`mock`** — deterministic, no API spend — so you can try the
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plumbing for free. Switch to `--backend claude` or `--backend codex` for genuine
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improvement on your own budget.
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### Handoff mode (session answers the model calls)
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`--backend handoff` runs the cycle without any model subprocess: the engine
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executes the deterministic stages and writes every model call it needs to
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`.skillopt-sleep-handoff/PROMPTS.md` + `pending.json` (exit code 3). You (or
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the `/skillopt-sleep-handoff` command, which automates the loop with isolated
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fresh-context subagents) write each raw answer to `answers/<id>.md` and re-run
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the same command; it resumes from the answers and either finishes or stages
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the next batch. Typically 3–6 rounds per night.
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```bash
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python -m skillopt_sleep run --backend handoff --project "$(pwd)"
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# ... answer .skillopt-sleep-handoff/PROMPTS.md into answers/<id>.md ...
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python -m skillopt_sleep run --backend handoff --project "$(pwd)" # resume
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```
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Answer every prompt in a **fresh context** — a session that has already seen
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the mined tasks and their references would contaminate the held-out gate.
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Details: [the plugins README](../README.md#--backend-handoff--session-executed-calls-no-api-subprocess).
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## Does it actually improve? (real models, public benchmark)
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SkillOpt-Sleep is validated against [gbrain-evals](https://github.com/garrytan/gbrain-evals)'
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public `skillopt-v1` suite — the same benchmark gbrain scores its own skill
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optimizer against. We take a deliberately **deficient** skill and run one sleep
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night; held-out scoring is done by a local rule judge (no judge-API, no way to
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grade its own homework).
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| Backend | Seed | Held-out before → after | Nights |
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|---|---|---|---|
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| **Claude (Haiku 4.5)** | brief-writer | **0.00 → 1.00** | 1 |
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| **Codex** | brief-writer | **0.00 → 1.00** | 2 |
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Both took a brief-writer with no risks section / no confidence level and, within
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1–2 nights, proposed gated edits that lifted the held-out score to perfect —
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into the protected `LEARNED` block, nothing else touched. The Codex 2-night
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trace even shows the optimizer **diagnosing its own residual failure** and
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adding a meta-rule to fix it. Full writeup + reproduction:
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[the SkillOpt-Sleep guide section](https://microsoft.github.io/SkillOpt/docs/guideline.html#sleep).
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Reproduce:
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```bash
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git clone https://github.com/garrytan/gbrain-evals /tmp/gbrain-evals
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python -m skillopt_sleep.experiments.run_gbrain --backend claude --model haiku \
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--seeds brief-writer --data-root /tmp/gbrain-evals/eval/data/skillopt-v1 \
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--nights 1 --limit-replay 3 --limit-holdout 3
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python -m skillopt_sleep.experiments.run_gbrain --backend codex \
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--seeds brief-writer --data-root /tmp/gbrain-evals/eval/data/skillopt-v1 \
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--nights 1 --limit-replay 3 --limit-holdout 3
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```
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## Deterministic proof (no API, no keys)
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```bash
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python -m skillopt_sleep.experiments.run_experiment --persona researcher --assert-improves
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python -m skillopt_sleep.experiments.run_experiment --persona programmer --assert-improves
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```
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Each prints the held-out score rising from baseline toward 1.0 as the gate
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accepts the general rules your tasks need, and confirms the gate **rejects** an
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injected harmful edit. Recorded output: [the SkillOpt-Sleep guide section](https://microsoft.github.io/SkillOpt/docs/guideline.html#sleep).
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## Schedule it nightly
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```bash
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"${CLAUDE_PLUGIN_ROOT}/scripts/install-cron.sh" "$(pwd)" # prints a crontab line; installs nothing
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```
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## Safety
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- **Read-only** harvest of `~/.claude`. `mock` replay has no side effects.
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- Proposals are **staged**, never auto-applied (unless you opt in with `--auto-adopt`).
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- Every adopt writes a backup under the staging dir's `backup/`.
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- Per-night **token/task budget caps**; secrets redacted from prompts.
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- `fresh` replay (Phase 3) runs only in throwaway git worktrees.
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## Status
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Phase 1 (engine + deterministic experiment + plugin surface) is complete.
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Phase 3 adds the real-API miner/judge and `fresh` worktree replay. See
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[`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).
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