Describe the workflow, prompt set, or repeated task you want to turn into a skill.
Use yao-meta-skill to generate or improve the package in scaffold, production, or library mode.
Run context_sizer.py, trigger_eval.py, and cross_packager.py as needed to validate and export the result.

5-Minute Workflow

Start from a raw workflow note.
Turn it into a skill package with SKILL.md, agents/interface.yaml, and only the folders the workflow actually needs.
Validate the trigger description with evals/trigger_cases.json.
Export compatibility artifacts for the clients you care about.
Compare the result against the examples in examples/.

Minimum commands:

python3 scripts/trigger_eval.py --description-file evals/improved_description.txt --cases evals/trigger_cases.json
python3 scripts/context_sizer.py .
python3 scripts/cross_packager.py . --platform openai --platform claude --platform generic --expectations evals/packaging_expectations.json --zip
python3 tests/verify_packager_failures.py

Or run everything together:

make test

Results

The homepage panel below is generated from the current eval suite so the family-level outcome is visible without opening raw JSON.

regression corpus: 66 prompts across 21 families
aggregate result: 0 false positives, 0 false negatives, average precision 1.0, average recall 1.0
suite status:

Suite	Cases	Precision	Recall
train	31	1.0	1.0
dev	22	1.0	1.0
holdout	13	1.0	1.0

Family	Cases	Pass Rate
`brainstorm_only`	2	1.0
`brainstorm_vs_build`	1	1.0
`complex_multi_asset`	3	1.0
`document_export_vs_agent_skill`	4	1.0
`document_only`	3	1.0
`explain_not_package`	1	1.0
`explain_only`	5	1.0
`future_outline_vs_build`	4	1.0
`iterate_existing_skill`	5	1.0
`long_context_document_only`	3	1.0
`long_context_near_neighbor`	3	1.0
`long_context_summary_only`	2	1.0
`long_context_trigger`	4	1.0
`meta_skill_creation`	1	1.0
`one_off_vs_reusable`	2	1.0
`package_for_team`	2	1.0
`paraphrase_trigger`	5	1.0
`partial_scaffold_not_full_skill`	4	1.0
`summary_only`	3	1.0
`translate_only`	4	1.0
`workflow_to_skill`	5	1.0

Full reports: reports/eval_suite.json and reports/family_summary.md

packaging validation: openai, claude, and generic targets pass contract checks
packaging failure fixtures: invalid metadata, invalid YAML, and unsupported targets fail as expected
failure library regressions: anti-pattern families pass automated checks

What It Does

This project helps you create, refactor, evaluate, and package skills as durable capability bundles rather than one-off prompts.

The design logic is simple:

Capture the real recurring job behind the user's request.
Set a clean skill boundary so one package does one coherent job.
Optimize the trigger description before over-writing the body.
Keep the main skill file small and move details into references or scripts.
Add quality gates only when they pay for themselves.
Export compatibility artifacts only for the clients you actually need.

Why It Exists

Most teams keep valuable operating knowledge scattered across chats, personal prompts, oral habits, and undocumented workflows. This project converts that hidden process knowledge into:

discoverable skill packages
repeatable execution flows
lower-context instructions
reusable team assets
compatibility-ready distributions

Repository Structure

yao-meta-skill/
├── SKILL.md
├── README.md
├── LICENSE
├── .gitignore
├── agents/
│   └── interface.yaml
├── evals/
├── examples/
├── references/
├── scripts/
└── templates/

Core Components

`SKILL.md`

The main skill entrypoint. It defines the trigger surface, operating modes, compact workflow, and output contract.

`agents/interface.yaml`

The neutral metadata source of truth. It stores display and compatibility metadata without locking the source tree to one vendor-specific path.

`references/`

Long-form material that should not bloat the main skill file. This includes design rules, evaluation guidance, compatibility strategy, and quality rubrics.

`scripts/`

Utility scripts that make the meta-skill operational:

trigger_eval.py: evaluates trigger descriptions with semantic intent concepts, explicit exclusions, and near-neighbor prompts
run_eval_suite.py: runs train/dev/holdout trigger suites, reports family-level regressions, and fails if aggregate regressions appear
context_sizer.py: estimates context weight and warns when the initial load gets too large
cross_packager.py: builds client-specific export artifacts with explicit platform contracts and validation
init_skill.py, lint_skill.py, validate_skill.py, diff_eval.py: minimal authoring toolchain

`evals/`

Reusable trigger and packaging checks, including baseline and improved descriptions for comparison.

`examples/`

End-to-end examples showing raw workflow input, design summary, and final generated skill shape, including one thicker complex benchmark and one evolution chain.

`.github/workflows/test.yml`

Continuous integration entrypoint that runs the full local regression suite on push and pull request.

Validation Notes

Trigger evaluation now uses a local semantic-intent model with explicit positive concepts, exclusion concepts, and boundary-case reporting.
The sample trigger report now covers a larger positive, negative, and near-neighbor set rather than a tiny demo set.
Train/dev/holdout trigger suites now separate iterative tuning from final verification.
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.
evals/failure-cases.md captures known weak spots that should remain part of regression checks.
failures/ captures reusable anti-pattern writeups and machine-runnable failure cases for routing, packaging, and authoring failures.
tests/verify_packager_failures.py checks that invalid metadata, invalid YAML, and unsupported targets fail clearly.

`templates/`

Starter templates for simple and more advanced skill packages.

How To Use

1. Use the skill directly

Invoke yao-meta-skill when you want to:

create a new skill
improve an existing skill
add evals to a skill
convert a workflow into a reusable package
prepare a skill for wider team adoption

2. Generate a new skill package

The typical flow is:

describe the workflow or capability
identify trigger phrases and outputs
choose scaffold, production, or library mode
generate the package
run the sizing and trigger checks if needed
export target-specific compatibility artifacts

3. Export compatibility artifacts

Examples:

python3 scripts/cross_packager.py ./yao-meta-skill --platform openai --platform claude --expectations evals/packaging_expectations.json --zip
python3 scripts/context_sizer.py ./yao-meta-skill
python3 scripts/trigger_eval.py --description-file evals/improved_description.txt --cases evals/trigger_cases.json --baseline-description-file evals/baseline_description.txt

Advantages

Neutral by default: source files stay vendor-neutral, while adapters are generated only when needed.
Context efficient: the project explicitly pushes detail out of the main skill file.
Evaluation-aware: trigger and sizing checks are built into the workflow.
Reusable: the output is a package, not just a paragraph of prompt text.
Portable: compatibility is handled through packaging rather than duplicating source files for every client.

Best Fit

This project is best for:

agent builders
internal tooling teams
prompt engineers moving toward structured skills
organizations building reusable skill libraries

Documentation

Language	Entry
English	README.md
中文	docs/README.zh-CN.md
日本語	docs/README.ja-JP.md
Français	docs/README.fr-FR.md
Русский	docs/README.ru-RU.md

Examples And Evals

Examples: examples/README.md
Evals: evals/README.md
Failure library: failures/README.md
Failure regression check: verify_failure_regressions.py
Packaging contracts: references/packaging-contracts.md
Platform capability matrix: references/platform-capability-matrix.md
Failure fixtures: tests/fixtures
Adapter snapshots: tests/snapshots
Evolution example: examples/evolution-frontend-review/README.md

License

MIT. See LICENSE.