Skill audit report
Analyze ARIS usage logs and propose optimizations to SKILL.md files, reviewer prompts, and workflow defaults. Outer-loop harness optimization inspired by Meta-Harness (Lee et al., 2026). Use when user says \"优化技能\", \"meta optimize\", \"improve skills\", \"分析使用记录\", or wants to optimize ARIS's own harness components based on accumulated experience.
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
PASS100
16K GitHub stars
Stars/forks activity
PASS97
16K stars, 1.4K forks; issue activity unavailable in current metadata
Recent maintenance
PASS100
6d since push
License clarity
PASS86
MIT
README/SKILL.md completeness
PASS86
Metadata includes enough usage and workflow context
Dependency/runtime risk
INFO72
command execution surface
Install availability
PASS92
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill meta-optimize
Install command safety
PASS92
standard package or runtime install path
Permission surface
INFO62
shell or command execution, filesystem or document access
Repository evidence
PASS86
https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/meta-optimize
Review status
WARN47.45
AI review approval is missing
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill meta-optimize
Repository
88
https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/meta-optimize
License
86
MIT
Maintenance
100
6d since push
AI review
55
Review approval is missing
README/SKILL.md completeness
86
Usable description available
Dependency risk
72
command execution surface
Install command safety
92
standard package or runtime install path
Permission surface
62
shell or command execution, filesystem or document access
Stars/forks activity
97
16K stars, 1.4K forks; issue activity unavailable in current metadata
Adoption
88
16K 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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