llm-wiki-starter: Create an LLM Wiki knowledge base in one command — based on Andrej Karpathy's LLM Wiki pattern

by eleven-net-cn · AI Tool · ★ 95

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install llm-wiki-starter

npx skills add eleven-net-cn/llm-wiki-starter -g

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 72/100

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.

llm-wikillm-wiki-karpathyllm-wiki-starter

Quick Facts

Stars95
Forks17
LanguageShell
CategoryAI Tool
LicenseMIT
Quality Score72/100
Last Updated2026-08-10
Created2026-04-24
Platformscli
Est. Tokens~12k

About llm-wiki-starter

English | 简体中文 llm-wiki-starter One command to scaffold an Andrej Karpathy's LLM Wiki AI knowledge base. Auto-installs Claude Code + Obsidian + recommended plugins (Skills & Plugins & Theme & Shortcuts), so AI can continuously build and maintain your personal knowledge system. Compatible with Claude Code, Codex, Copilot, Gemini CLI, OpenCode, and other mainstream AI agents out of the box.

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Looking for a llm-wiki-starter alternative? If you're comparing llm-wiki-starter with other ai 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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How this security grade is produced

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:

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