Skill Quality Reviewer
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About this skill
What It Solves
Many SKILL.md files only expose problems after release: a too-broad or too-narrow description can misfire, missing examples make model behavior unstable, and mixed resources make review noisy. skill-reviewer moves this check earlier by reading the target Skill's SKILL.md, directory structure, and related assets, then producing a review report against a fixed checklist.
How It Works
- Collects files: reads
SKILL.md, listsscripts/,references/,assets/, and reads relevant resources. - Loads standards: uses 100 base points and 30 bonus points from
references/checklist.md. - Scores item by item: highlights only non-perfect checks with targeted explanations.
- Outputs a report: dimension table, bonus score, anti-patterns, improvement items, and summary.
- Optionally generates test cases: positive, negative, and boundary scenarios, written to
evaluation/test-cases.md.
Boundaries
It is best for pre-release review and code-style improvement, not for replacing model capability testing or business acceptance. Scoring depends on checklist.md and examples.md; for highly specialized domains, humans should still judge whether deductions are too strict or too loose.
Use Cases
- Before release, review the description and steps and produce an improvement report.
- When a team shares a skill folder, check structure, assets, and checklist scores.
- After editing an existing skill, compare base and bonus scores to find deductions.
- Before launch, generate positive, negative, and boundary cases and write them to a test file.
Best For
- Agent engineer maintaining shared skills who wants to catch unstable triggers and structure issues before release.
- Full-stack engineer managing a team skill repo who needs consistent checklist scores and improvement items.
- AI product engineer packaging agent assets who needs review reports and test cases saved into the repo.
- Engineering owner taking over legacy skills who wants quick base, bonus, and anti-pattern findings.
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