Skill audit report
Awesome LLM Eval Audit report.
Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on LLMs. 一个由工具、基准/数据、演示、排行榜和大模型等组成的精选列表，主要面向基础大模型评测，旨在探求生成式AI的技术边界.
OpenAgentSkill Trust Score
OpenAgentSkill Trust Score
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
INFO76
642 GitHub stars
Stars/forks activity
INFO71
642 stars, 76 forks; issue activity unavailable in current metadata
Recent maintenance
INFO62
8mo since push
License clarity
PASS86
MIT
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 onejune2018/Awesome-LLM-Eval
Install command safety
INFO68
dynamic command execution, standard package or runtime install path
Permission surface
PASS86
filesystem or document access
Repository evidence
PASS86
https://github.com/onejune2018/Awesome-LLM-Eval
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 onejune2018/Awesome-LLM-Eval
Repository
88
https://github.com/onejune2018/Awesome-LLM-Eval
License
86
MIT
Maintenance
62
8mo 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
68
dynamic command execution, standard package or runtime install path
Permission surface
86
filesystem or document access
Stars/forks activity
71
642 stars, 76 forks; issue activity unavailable in current metadata
Adoption
88
642 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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