by cirolini · LLM Plugin · ★ 371
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Flagged: reads sensitive env vars. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 371 |
| Forks | 70 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 75/100 |
| Last Updated | 2026-09-21 |
| Created | 2023-01-25 |
| Platforms | python |
| Est. Tokens | ~16k |
GenAI Code Review A GitHub Action that reviews pull request diffs with an LLM and posts inline comments. It works with OpenAI, Anthropic, Google Gemini, or any OpenAI-compatible server. Unlike most such tools, it is built around a comment budget: it posts only the few findings most worth a human's attention, and tells you what it held back. Quick start One workflow file. Nothing else.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: