skill quality control

Shipping unreliable or messy AI agent skills leads to broken workflows and security risks

This tool audits Skill packages against a strict engineering rubric to ensure they are production-ready.

What it does

• Quality Auditing: Runs deterministic checks on SKILL.md files to score engineering maturity.
• Safety Gating: Identifies blocking issues and secret leaks without exposing sensitive values.
• Bilingual Reporting: Generates standalone HTML and JSON reports in English or Chinese.
• Repository Scanning: Crawls directories to audit entire libraries, providing score distributions and averages.
• Evidence Verification: Validates skill capabilities against schema-bound evidence to prevent false claims.
• Baseline Comparison: Tracks quality deltas across iteration cycles to prevent regressions.

How it works

1. Initialize Point the tool at a single skill directory or a full repository root.
2. Configure Set the profile, maturity targets, and locale for the audit.
3. Execute Run the python-based auditor to generate the deterministic quality report.
4. Anonymize Use optional flags to strip private paths and skill names for external sharing.

Frameworks & tools

Built for Python environments. Uses localized HTML templates and JSON schemas to validate SKILL.md files and package structures.

Why this beats prompting it yourself

Manual reviews are subjective and miss hidden engineering gaps. This skill applies a rigid, repeatable rubric and evidence contract that treats skill quality as a measurable software metric rather than a vibe.

Use cases

• Auditing a third-party skill library before internal deployment.
• Enforcing engineering standards in a CI/CD pipeline for AI agents.
• Generating anonymous quality reports for client deliverables.
• Benchmarking skill improvements across multiple development iterations.

Known limitations

Does not evaluate business logic, strategy, or the actual performance of the AI model. Requires a structured skill package with a SKILL.md file to function.