Skill audit report
ML NOTE audit report.
:orange_book:慢慢整理所学的机器学习算法，并根据自己所理解的样子叙述出来。(注重数学推导)
OpenAgentSkill Trust Score
Stars, maintenance, license, docs, dependency risk, and installability.
The Trust Score is OpenAgentSkill's adoption layer. It is designed to help an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
INFO76
676 GitHub stars
Recent maintenance
FAIL22
4y since push
License clarity
PASS86
MIT
README/SKILL.md completeness
INFO74
Public metadata needs stronger README/SKILL.md context
Dependency risk
PASS90
no major dependency risk hints in public metadata
Install availability
PASS92
npx skills add yhangf/ML-NOTE
Repository evidence
PASS86
https://github.com/yhangf/ML-NOTE
Review status
PASS88
AI review data available
Checks
Install and adoption review
Install path
92
npx skills add yhangf/ML-NOTE
Repository
88
https://github.com/yhangf/ML-NOTE
License
86
MIT
Maintenance
20
4y since push
AI review
88
Approved with no listed issues
README/SKILL.md completeness
84
Usable description available
Dependency risk
90
no major dependency risk hints in public metadata
Adoption
88
676 GitHub stars
Warnings
- Repository appears stale
- Repository looks stale
- Quality score needs review
- Recent maintenance: 4y since push
Method
This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.
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