by ddsyasas · Agent Tool · ★ 61
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npm install -g @syasas/llm-wiki
git clone https://github.com/ddsyasas/llm-wiki.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 | 61 |
| Forks | 20 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67/100 |
| Open Issues | 3 |
| Last Updated | 2026-06-04 |
| Created | 2026-05-23 |
| Platforms | node |
| Est. Tokens | ~21k |
LLM Wiki A personal Wikipedia an LLM maintains for you. Drop in articles, papers, notes, PDFs, or URLs — an agent compiles them into a cross-linked markdown wiki you fully own. Knowledge compounds: each new source makes every page richer, not just one new page longer.
Looking for a llm-wiki alternative? If you're comparing llm-wiki with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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A fast, open-source markdown editor. Graph view, plugin API, themes, Git/WebDAV sync, AI MCP — built on Tauri
A git template for building your own LLM-powered personal wiki. Training period, metadata standard, lint syste
Turn your markdown vault into a compounding knowledge wiki (Karpathy inspired). Six agent skills - knowledge g
Local-first AI learning workspace — ask, note, review and create around your own materials. Wiki KB, Agents, S
Memoriki - LLM Wiki + MemPalace. Personal knowledge base with real memory.
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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: