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git clone https://github.com/shannhk/llm-wikid.git my-wikiTaken 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 | 421 |
| Forks | 40 |
| Language | Shell |
| Category | AI Tool |
| Quality Score | 62/100 |
| Open Issues | 2 |
| Last Updated | 2026-08-24 |
| Created | 2026-04-09 |
| Platforms | claude-code, cli |
| Est. Tokens | ~14k |
LLM Wikid An AI-maintained knowledge base that lives in Obsidian. Based on Karpathy's LLM Wiki pattern. You dump raw sources into a folder. An AI agent reads them, compiles structured wiki pages with cross-references, runs bias checks, and maintains a master index. Every question you ask gets filed back in. The wiki compounds the more you use it.
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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: