yaojingang
422 lines
24 KiB
Markdown
422 lines
24 KiB
Markdown
# Yao Meta Skill
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[](https://github.com/yaojingang/yao-meta-skill/actions/workflows/test.yml)
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[](LICENSE)
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[](README.md)
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[](docs/README.zh-CN.md)
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[](docs/README.ja-JP.md)
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[](docs/README.fr-FR.md)
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[](docs/README.ru-RU.md)
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`YAO` stands for `Yielding AI Outcomes` — the goal is not to generate more prompt text, but to produce reusable AI assets and real operational outcomes.
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`yao-meta-skill` is a lightweight but rigorous system for creating, evaluating, packaging, and governing reusable agent skills.
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[Quick Start](#quick-start) · [Examples](examples/README.md) · [Evals](evals/README.md) · [Failure Library](failures/README.md) · [Method Doctrine](#method-doctrine)
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It turns rough workflows, transcripts, prompts, notes, and runbooks into reusable skill packages with:
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- a clear trigger surface
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- a lean `SKILL.md`
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- optional references, scripts, and evals
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- neutral source metadata plus client-specific adapters
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- governance, promotion, and portability checks built into the default flow
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## Architecture
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The system is intentionally layered so users can understand it from top to bottom: route first, choose method second, validate third, then package and govern the result.
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```mermaid
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flowchart TD
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A["Inputs<br/>workflows / prompts / transcripts / docs / notes"] --> B["Router<br/>SKILL.md"]
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B --> C["Method Layer<br/>references/"]
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B --> D["Authoring Flow<br/>scripts/yao.py"]
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C --> C1["Archetypes"]
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C --> C2["Gate Selection"]
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C --> C3["Non-Skill Decision"]
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C --> C4["Operating Modes"]
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C --> C5["Governance"]
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C --> C6["Resource Boundaries"]
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D --> E["Create<br/>init / template"]
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D --> F["Validate<br/>lint / boundary / governance"]
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D --> G["Evaluate<br/>trigger / suites / judge / confusion"]
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D --> H["Promote<br/>promotion policy / candidate registry"]
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D --> I["Package<br/>neutral source -> target adapters"]
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D --> J["Report<br/>history / scorecards / context / portability"]
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E --> K["Skill Package"]
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F --> K
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G --> L["evals/"]
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H --> M["reports/"]
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I --> N["dist/ or target outputs"]
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K --> K1["SKILL.md"]
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K --> K2["agents/interface.yaml"]
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K --> K3["manifest.json"]
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K --> K4["optional references / scripts / evals / reports"]
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L --> M
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```
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Read the diagram in five layers:
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- **Inputs**: rough operational material becomes the source for a reusable skill package.
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- **Router**: `SKILL.md` stays small and decides boundary, mode, and output contract first.
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- **Method layer**: doctrine files explain whether the request should become a skill and which quality gates it deserves.
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- **Authoring flow**: the unified CLI turns creation, validation, optimization, promotion, reporting, and packaging into one path.
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- **Evidence and outputs**: the result is not only a skill package, but also eval artifacts, governance signals, portability outputs, and iteration history.
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## Quick Start
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1. Describe the workflow, prompt set, or repeated task you want to turn into a skill.
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2. Use `yao-meta-skill` to generate or improve the package in scaffold, production, or library mode.
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3. Run `context_sizer.py`, `resource_boundary_check.py`, `governance_check.py`, `trigger_eval.py`, and `cross_packager.py` as needed to validate and export the result.
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Or use the unified authoring CLI:
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```bash
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python3 scripts/yao.py validate .
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python3 scripts/yao.py workspace-flow --target root --label first-pass
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python3 scripts/yao.py package . --platform generic --output-dir dist
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```
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## 5-Minute Workflow
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1. Start from a raw workflow note.
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2. Turn it into a skill package with `SKILL.md`, `agents/interface.yaml`, and only the folders the workflow actually needs.
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3. Validate the trigger description with `evals/trigger_cases.json`.
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4. Export compatibility artifacts for the clients you care about.
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5. Compare the result against the examples in `examples/`.
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Minimum commands:
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```bash
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python3 scripts/trigger_eval.py --description-file evals/improved_description.txt --cases evals/trigger_cases.json
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python3 scripts/run_description_optimization_suite.py
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python3 scripts/judge_blind_eval.py --description-file SKILL.md --cases evals/blind_holdout/trigger_cases.json --semantic-config evals/semantic_config.json
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python3 scripts/context_sizer.py .
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python3 scripts/resource_boundary_check.py .
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python3 scripts/governance_check.py . --require-manifest
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python3 scripts/cross_packager.py . --platform openai --platform claude --platform generic --expectations evals/packaging_expectations.json --zip
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python3 tests/verify_packager_failures.py
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```
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Or run everything together:
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```bash
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make test
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```
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Unified authoring flow:
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```bash
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python3 scripts/yao.py init my-skill --description "Describe what the skill does."
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python3 scripts/yao.py validate my-skill
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python3 scripts/yao.py workspace-flow --target root --label first-pass
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python3 scripts/yao.py review --target root
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python3 scripts/yao.py release-snapshot --target root --label release-candidate
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python3 scripts/yao.py package . --platform openai --platform claude --platform generic --output-dir dist --zip
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python3 scripts/yao.py test
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```
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## Results
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The homepage panel below is generated from the current eval suite so the family-level outcome is visible without opening raw JSON.
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<!-- BEGIN:EVAL_RESULTS -->
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- regression corpus: `66` prompts across `21` families
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- aggregate result: `0` false positives, `0` false negatives, average precision `1.0`, average recall `1.0`
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- suite status:
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| Suite | Cases | FP | FN | Precision | Recall |
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| --- | ---: | ---: | ---: | ---: | ---: |
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| train | 31 | 0 | 0 | 1.0 | 1.0 |
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| dev | 22 | 0 | 0 | 1.0 | 1.0 |
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| holdout | 13 | 0 | 0 | 1.0 | 1.0 |
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| Family | Cases | Pass Rate |
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| --- | ---: | ---: |
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| `brainstorm_only` | 2 | 1.0 |
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| `brainstorm_vs_build` | 1 | 1.0 |
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| `complex_multi_asset` | 3 | 1.0 |
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| `document_export_vs_agent_skill` | 4 | 1.0 |
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| `document_only` | 3 | 1.0 |
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| `explain_not_package` | 1 | 1.0 |
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| `explain_only` | 5 | 1.0 |
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| `future_outline_vs_build` | 4 | 1.0 |
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| `iterate_existing_skill` | 5 | 1.0 |
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| `long_context_document_only` | 3 | 1.0 |
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| `long_context_near_neighbor` | 3 | 1.0 |
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| `long_context_summary_only` | 2 | 1.0 |
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| `long_context_trigger` | 4 | 1.0 |
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| `meta_skill_creation` | 1 | 1.0 |
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| `one_off_vs_reusable` | 2 | 1.0 |
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| `package_for_team` | 2 | 1.0 |
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| `paraphrase_trigger` | 5 | 1.0 |
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| `partial_scaffold_not_full_skill` | 4 | 1.0 |
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| `summary_only` | 3 | 1.0 |
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| `translate_only` | 4 | 1.0 |
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| `workflow_to_skill` | 5 | 1.0 |
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Full reports: [reports/eval_suite.json](reports/eval_suite.json) and [reports/family_summary.md](reports/family_summary.md)
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<!-- END:EVAL_RESULTS -->
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- packaging validation: `openai`, `claude`, and `generic` targets pass contract checks
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- portability score: `100/100` with neutral activation, execution, trust, and degradation metadata preserved across all exported targets
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- description optimization suite: root, team frontend review, and governed incident command pass blind and adversarial holdout gates; governed incident command still carries one visible holdout miss, and adversarial calibration plus family drift are now tracked separately
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- judge-backed blind eval: root, team frontend review, and governed incident command now pass an independent rubric judge on blind holdout prompts
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- packaging failure fixtures: invalid metadata, invalid YAML, and unsupported targets fail as expected
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- failure library regressions: anti-pattern families pass automated checks
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- governance and resource-boundary checks are part of the default test path
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- root governance maturity score: `90/100`; governed benchmark example: `95/100`
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- context budgets: root `971/1000`, complex benchmark `790/1000`, governed benchmark `760/1000`
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- quality density: root `133.9`, complex benchmark `164.6`, governed benchmark `171.1`
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- regression milestones are tracked in [reports/regression_history.md](reports/regression_history.md)
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- description drift history is tracked in [reports/description_drift_history.md](reports/description_drift_history.md)
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- route confusion is tracked in [reports/route_scorecard.md](reports/route_scorecard.md)
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- promotion evidence is summarized in [reports/iteration_ledger.md](reports/iteration_ledger.md)
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- promotion decisions are published in [reports/promotion_decisions.md](reports/promotion_decisions.md)
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- candidate lifecycle states are published in [reports/candidate_registry.md](reports/candidate_registry.md)
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- context budget summaries are tracked in [reports/context_budget.md](reports/context_budget.md)
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- portability status is tracked in [reports/portability_score.md](reports/portability_score.md)
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## Current Strengths
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In the latest weighted review shared with the project, Yao scored strongest in the dimensions that define a production-grade meta-skill system:
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- **Method completeness `9.8`**: the repository now has a formal doctrine for skill engineering, gate selection, non-skill decisions, lifecycle governance, and resource boundaries.
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- **Engineering toolchain `9.8`**: authoring, validation, packaging, reporting, promotion checks, and CI are wired into one operational toolchain rather than scattered scripts.
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- **Governance, maintenance, and safety `9.8`**: important skills can carry lifecycle state, review cadence, maturity score, trust boundaries, and promotion evidence.
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- **Evaluation loop `9.7`**: trigger quality is checked with train/dev/holdout, blind holdout, adversarial holdout, judge-backed blind eval, drift history, and promotion gates.
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- **Portability and packaging `9.6`**: the source stays neutral while adapters, degradation rules, and packaging contracts preserve reusable semantics across target environments.
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- **Trigger and boundary design `9.5`**: route confusion, anti-pattern regressions, and promotion policy make trigger quality an auditable routing problem instead of a loose prompt-writing exercise.
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- **Context efficiency `9.4`**: the entrypoint stays compact, context budgets are tiered, and quality density is tracked instead of only raw token counts.
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The overall direction is deliberate: keep the entrypoint light, make the evaluation loop strict, and treat governance as a first-class part of skill quality.
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## Why Yao
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- **Lightweight**: the entrypoint stays compact, context budgets are explicit, and extra structure is added only when it pays for itself.
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- **Rigorous**: trigger quality is checked with family regressions, blind holdout, adversarial holdout, route confusion, judge-backed blind eval, and promotion gates.
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- **Governed**: important skills are treated as maintainable assets with lifecycle state, maturity expectations, ownership, and review cadence.
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- **Portable**: source metadata stays neutral while adapters, degradation rules, and packaging contracts preserve reusable semantics across environments.
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## What It Does
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This project helps you create, refactor, evaluate, and package skills as durable capability bundles rather than one-off prompts.
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The design logic is simple:
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1. Capture the real recurring job behind the user's request.
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2. Set a clean skill boundary so one package does one coherent job.
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3. Optimize the trigger description before over-writing the body.
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4. Keep the main skill file small and move details into references or scripts.
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5. Add quality gates only when they pay for themselves.
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6. Export compatibility artifacts only for the clients you actually need.
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## Method Doctrine
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The repository now treats method as a first-class asset instead of scattered guidance.
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- [Skill Engineering Method](references/skill-engineering-method.md)
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- [Skill Archetypes](references/skill-archetypes.md)
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- [Gate Selection](references/gate-selection.md)
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- [Non-Skill Decision Tree](references/non-skill-decision-tree.md)
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- [Regression Cause Taxonomy](references/regression-cause-taxonomy.md)
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- [Human Review Template](references/human-review-template.md)
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## Why It Exists
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Most teams keep valuable operating knowledge scattered across chats, personal prompts, oral habits, and undocumented workflows. This project converts that hidden process knowledge into:
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- discoverable skill packages
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- repeatable execution flows
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- lower-context instructions
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- reusable team assets
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- compatibility-ready distributions
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## Repository Structure
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```text
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yao-meta-skill/
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├── SKILL.md
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├── README.md
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├── LICENSE
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├── .gitignore
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├── agents/
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│ └── interface.yaml
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├── evals/
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├── examples/
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├── references/
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├── scripts/
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└── templates/
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```
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## Core Components
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### `SKILL.md`
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The main skill entrypoint. It defines the trigger surface, operating modes, compact workflow, and output contract.
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### `agents/interface.yaml`
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The neutral metadata source of truth. It stores display and compatibility metadata without locking the source tree to one vendor-specific path.
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### `references/`
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Long-form material that should not bloat the main skill file. This includes design rules, evaluation guidance, compatibility strategy, and quality rubrics.
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### `scripts/`
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Utility scripts that make the meta-skill operational:
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- `trigger_eval.py`: evaluates trigger descriptions with semantic intent concepts, explicit exclusions, and near-neighbor prompts
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- `run_eval_suite.py`: runs train/dev/holdout trigger suites, reports family-level regressions, and fails if aggregate regressions appear
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- `optimize_description.py`: generates candidate descriptions, scores them on dev, visible holdout, blind holdout, and adversarial holdout suites, then reports calibration and family health
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- `judge_blind_eval.py`: applies an independent rubric judge to blind-holdout prompts so blind acceptance is not backed only by the main threshold scorer
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- `run_description_optimization_suite.py`: runs description optimization across the root skill and governed examples, then writes reusable reports and optional drift snapshots with calibration and family summaries
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- `promotion_checker.py`: applies promotion policy to current description candidates, writes promotion decisions, builds candidate registries, and emits iteration bundles with review stubs
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- `create_iteration_snapshot.py`: freezes the current promotion decision into a versioned release snapshot with review, route, and context evidence
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- `yao.py`: unified authoring CLI that exposes init, validate, optimize-description, promote-check, review, release-snapshot, workspace-flow, report, package, and test as one entrypoint
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- `render_description_drift_history.py`: turns description-optimization snapshots into a readable drift-history report
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- `build_confusion_matrix.py`: scores route confusion across tracked sibling skills and `no_route` cases, then writes a route scorecard and optional milestone snapshot
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- `render_iteration_ledger.py`: compresses regression milestones, description optimization drift, and route scorecards into one iteration-facing ledger
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- `context_sizer.py`: estimates context weight and warns when the initial load gets too large
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- `resource_boundary_check.py`: audits whether detail is split across `SKILL.md`, `references/`, `scripts/`, `assets/`, and `evals/` appropriately
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- `governance_check.py`: validates owner, review cadence, lifecycle stage, and maturity metadata
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- `render_context_reports.py`: generates root and example context-budget reports plus a shared context summary
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- `render_regression_history.py`: turns milestone snapshots into a readable regression history report
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- `cross_packager.py`: builds client-specific export artifacts with explicit platform contracts and validation
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- `render_portability_report.py`: scores cross-environment portability from neutral metadata, degradation rules, and consumer validation coverage
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- `init_skill.py`, `lint_skill.py`, `validate_skill.py`, `diff_eval.py`: minimal authoring toolchain
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### `evals/`
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Reusable trigger and packaging checks, including baseline and improved descriptions for comparison plus the root semantic configuration that drives description optimization.
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This directory also contains route confusion fixtures and promotion policy rules for deciding when a route is promotable.
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### `examples/`
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End-to-end examples showing raw workflow input, design summary, final generated skill shape, and targeted description-optimization packs where route wording is tuned against example-specific dev and holdout cases.
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### `.github/workflows/test.yml`
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Continuous integration entrypoint that runs the full local regression suite on push and pull request.
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## Validation Notes
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- Trigger evaluation now uses a local semantic-intent model with explicit positive concepts, exclusion concepts, and boundary-case reporting.
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- The sample trigger report now covers a larger positive, negative, and near-neighbor set rather than a tiny demo set.
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- Train/dev/holdout trigger suites now separate iterative tuning from final verification.
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- Description optimization now uses dev for ranking, visible holdout for non-regression, blind holdout for acceptance, and adversarial holdout for harder route-collision checks without feeding the ranking loop.
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- Judge-backed blind eval now adds a rubric-based second opinion for blind prompts, so blind acceptance is not decided by one scorer alone.
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- Description drift history now records adversarial calibration gaps and family coverage, so routing changes can be judged on confidence and family stability rather than raw error counts alone.
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- Route confusion is now tracked explicitly across the root meta-skill, frontend review skill, governed incident skill, and `no_route` cases, so route theft is visible instead of implicit.
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- Promotion policy now requires visible holdout, blind holdout, adversarial holdout, and route confusion to stay clean before a description should be considered promotable.
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- Promotion checking now emits explicit decisions, candidate lifecycle states, iteration bundles, and human-review stubs rather than leaving promotion as a prose-only step.
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- Promotion decisions now distinguish “no candidate beat current” from “current still has residual route risk,” so iteration can be audited without forcing every issue into a false block.
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- Packaging validation now uses explicit contracts and YAML parsing, but it is still a lightweight local validation layer rather than a full platform integration suite.
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- `evals/failure-cases.md` captures known weak spots that should remain part of regression checks.
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- `failures/` captures reusable anti-pattern writeups and machine-runnable failure cases for routing, packaging, and authoring failures.
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- `tests/verify_packager_failures.py` checks that invalid metadata, invalid YAML, and unsupported targets fail clearly.
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- Governance metadata and resource-boundary rules now have runnable checks instead of staying as prose only.
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- Governance checks now emit a maturity score so governed assets can be compared instead of only pass/fail checked.
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- Description optimization drift history is now versioned separately from the main trigger regression history so routing improvements are visible over time.
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- Iteration evidence now records why a candidate was kept, blocked, or promotable via a shared regression-cause taxonomy and bundle artifacts.
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- Declared maturity tiers are checked against recommended minimum governance scores, so `production`, `library`, and `governed` assets can be compared without forcing every strong example into the same label.
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- Context budgets are now tiered and explicit, so a governed skill can still choose a stricter `production`-sized initial-load budget.
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- Resource-boundary checks now detect decorative directories and compute a local quality-density signal instead of only checking raw token counts.
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### `templates/`
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Starter templates for simple and more advanced skill packages.
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## How To Use
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### 1. Use the skill directly
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Invoke `yao-meta-skill` when you want to:
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- create a new skill
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- improve an existing skill
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- add evals to a skill
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- convert a workflow into a reusable package
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- prepare a skill for wider team adoption
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### 2. Generate a new skill package
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The typical flow is:
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1. describe the workflow or capability
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2. identify trigger phrases and outputs
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3. choose scaffold, production, or library mode
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4. generate the package
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5. run the sizing and trigger checks if needed
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6. export target-specific compatibility artifacts
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### 3. Export compatibility artifacts
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Examples:
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```bash
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python3 scripts/cross_packager.py ./yao-meta-skill --platform openai --platform claude --expectations evals/packaging_expectations.json --zip
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python3 scripts/context_sizer.py ./yao-meta-skill
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python3 scripts/resource_boundary_check.py ./yao-meta-skill
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python3 scripts/governance_check.py ./yao-meta-skill --require-manifest
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python3 scripts/trigger_eval.py --description-file evals/improved_description.txt --cases evals/trigger_cases.json --baseline-description-file evals/baseline_description.txt
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```
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## Advantages
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- **Method-first, not prompt-first**: skill creation is treated as a formal engineering workflow with archetypes, gate selection, and non-skill decisions.
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- **Trigger-aware by design**: descriptions are optimized with route confusion, blind holdout, adversarial families, and promotion policy instead of one-shot intuition.
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- **Lightweight at the entrypoint**: `SKILL.md` stays compact while references, scripts, and evals are only added when they pay for themselves.
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- **Toolchain-backed**: initialization, validation, optimization, reporting, packaging, and testing are available through one unified CLI and CI path.
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- **Governed as an asset**: important skills can carry ownership, lifecycle state, maturity expectations, and review cadence.
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- **Portable by default**: source metadata stays neutral while adapters and degradation rules preserve compatibility across target environments.
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- **Evidence-rich**: route scorecards, regression history, context budgets, portability scores, and promotion decisions are published as artifacts instead of hidden implementation detail.
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## Best Fit
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This project is best for:
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- agent builders
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- internal tooling teams
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- prompt engineers moving toward structured skills
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- organizations building reusable skill libraries
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## Documentation
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| Language | Entry |
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| --- | --- |
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| English | [README.md](README.md) |
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| 中文 | [docs/README.zh-CN.md](docs/README.zh-CN.md) |
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| 日本語 | [docs/README.ja-JP.md](docs/README.ja-JP.md) |
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| Français | [docs/README.fr-FR.md](docs/README.fr-FR.md) |
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| Русский | [docs/README.ru-RU.md](docs/README.ru-RU.md) |
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## Examples And Evals
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- Examples: [examples/README.md](examples/README.md)
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- Evals: [evals/README.md](evals/README.md)
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- Failure library: [failures/README.md](failures/README.md)
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- Failure regression check: [verify_failure_regressions.py](tests/verify_failure_regressions.py)
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- Regression history: [reports/regression_history.md](reports/regression_history.md)
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- Root governance score: [reports/governance_score.json](reports/governance_score.json)
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- Packaging contracts: [references/packaging-contracts.md](references/packaging-contracts.md)
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- Governance model: [references/governance.md](references/governance.md)
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- Resource boundary spec: [references/resource-boundaries.md](references/resource-boundaries.md)
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- Platform capability matrix: [references/platform-capability-matrix.md](references/platform-capability-matrix.md)
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- Failure fixtures: [tests/fixtures](tests/fixtures)
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- Adapter snapshots: [tests/snapshots](tests/snapshots)
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- Evolution example: [examples/evolution-frontend-review/README.md](examples/evolution-frontend-review/README.md)
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- Governed example: [examples/governed-incident-command/design-summary.md](examples/governed-incident-command/design-summary.md)
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- Governed example score: [examples/governed-incident-command/generated-skill/reports/governance_score.json](examples/governed-incident-command/generated-skill/reports/governance_score.json)
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## License
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MIT. See [LICENSE](LICENSE).
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