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Get Started Free →yaojingang/yao-meta-skill
Create/improve/evaluate agent skills from workflows, prompts, SOPs, scripts.
Passed 19 of 22 test cases with the skill, and 8 of 22 without it.
and 13 more in the full report
13 fixed7 passed either way2 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
What is the maximum number of core-fork clarification questions allowed in a single turn during initial setup?
| Checked | Without skill | With skill |
|---|---|---|
| Limits core-fork questions to a maximum of two per round | Missed | Passed |
The instructions your model reads when the skill loads.
description using the lightest mode.SKILL.md lean: guidance in references/, logic in scripts/, evidence in reports/.Scaffold: exploratory; Production: team; Library: shared; Governed: release-critical.repeated use + reusable output contract; skip one-offs.--self.3-5 useful external/user/local references.description early; route edits need trigger_eval.py; release gates follow risk.Playbooks: Target Safety, Intent, Skill IR, Output Eval, Review Studio.
Activation: scripts/yao.py check-update --notice --self; show notice_text if requested; continue. “更新”: scripts/yao.py self-update --self --yes.
For production/library/governed releases, run Skill IR, compiler, trigger/output eval, Atlas, conformance, trust, package/install, upgrade, drift, waiver, Review Studio.
Governed/file-backed packages: label input_files a file-backed fixture; include owner, review cadence, output contract, rollback boundary; require trust report and reports/output_quality_scorecard.md; mark unavailable telemetry/approvals/metrics/benchmarks as missing evidence; never fabricate evidence.
Preserve labels literally when they apply: file-backed fixture, input_files, output contract, rollback boundary, trust report, reports/output_quality_scorecard.md, missing evidence.
test cases passed, vs 8 / 22 without
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