Skill audit report
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OpenAgentSkill Trust Score
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
WARN48
85 GitHub stars
Stars/forks activity
WARN48
85 stars, 10 forks; issue activity unavailable in current metadata
Recent maintenance
PASS100
1d since push
License clarity
PASS86
MIT
README/SKILL.md completeness
INFO70
Public metadata needs stronger README/SKILL.md context
Dependency/runtime risk
PASS90
no major dependency risk hints in public metadata
Install availability
PASS92
npx skills add pymc-labs/pymc-modeling --skill arviz-diagnostics
Install command safety
PASS92
standard package or runtime install path
Permission surface
PASS100
no high-risk permission surface in public metadata
Repository evidence
PASS86
https://github.com/pymc-labs/pymc-modeling/tree/main/skills/arviz-diagnostics
Review status
WARN46
AI review approval is missing
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add pymc-labs/pymc-modeling --skill arviz-diagnostics
Repository
88
https://github.com/pymc-labs/pymc-modeling/tree/main/skills/arviz-diagnostics
License
86
MIT
Maintenance
100
1d since push
AI review
55
Review approval is missing
README/SKILL.md completeness
84
Usable description available
Dependency risk
90
no major dependency risk hints in public metadata
Install command safety
92
standard package or runtime install path
Permission surface
100
no high-risk permission surface in public metadata
Stars/forks activity
48
85 stars, 10 forks; issue activity unavailable in current metadata
Adoption
68
85 GitHub stars
Warnings
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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