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npx skills add https://github.com/nashsu/llm_wiki_skill.git --skill llm-wiki
git clone https://github.com/nashsu/llm_wiki_skill.gitTaken from the project's README on GitHub. Check the security audit before running it.
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 | 148 |
| Forks | 23 |
| Category | AI Tool |
| Quality Score | 59/100 |
| Open Issues | 1 |
| Last Updated | 2026-05-19 |
| Created | 2026-05-19 |
| Est. Tokens | ~3k |
llm-wiki skill Lets an AI agent (Claude Code, Codex, etc.) query the user's locally-running LLM Wiki desktop app over its built-in HTTP API. This skill is documentation only. The API is a standard HTTP+JSON contract — call it with , , , or whatever HTTP tool your environment already has. There is no client library, no SDK, no compile step.
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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: