Skill audit report
Mlx audit report.
Machine Learning eXchange (MLX). Data and AI Assets Catalog and Execution Engine
OpenAgentSkill Trust Score
Stars, maintenance, license, docs, install safety, permission surface, 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
INFO62
220 GitHub stars
Stars/forks activity
INFO62
220 stars, 54 forks; issue activity unavailable in current metadata
Recent maintenance
INFO62
9mo since push
License clarity
PASS86
Apache-2.0
README/SKILL.md completeness
PASS90
Metadata includes enough usage and workflow context
Dependency/runtime risk
PASS90
no major dependency risk hints in public metadata
Install availability
PASS92
npx skills add claimed-framework/mlx
Install command safety
PASS92
standard package or runtime install path
Permission surface
PASS86
filesystem or document access
Repository evidence
PASS86
https://github.com/claimed-framework/mlx
Review status
PASS88
AI review data available
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install and adoption review
Install path
92
npx skills add claimed-framework/mlx
Repository
88
https://github.com/claimed-framework/mlx
License
86
Apache-2.0
Maintenance
62
9mo since push
AI review
88
Approved with no listed issues
README/SKILL.md completeness
90
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
86
filesystem or document access
Stars/forks activity
62
220 stars, 54 forks; issue activity unavailable in current metadata
Adoption
68
220 GitHub stars
Warnings
- Quality score needs review
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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