by cablate · LLM Plugin · ★ 149
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. 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 | 149 |
| Forks | 27 |
| Language | Shell |
| Category | LLM Plugin |
| Quality Score | 66/100 |
| Open Issues | 7 |
| Last Updated | 2026-04-20 |
| Created | 2026-04-16 |
| Platforms | cli |
| Est. Tokens | ~6k |
llm-atomic-wiki Built on top of Andrej Karpathy's LLM Wiki. All credit to him for the pattern — this repo is what I learned by running it end-to-end, plus four small additions that helped at scale. 584 posts · 8,668 replies · 630 atoms · 83 wiki pages · 11 branches The repo gives you the framework — methodology, schema, scripts, folder structure. Fork it and run it on your own materials.
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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: