llm-wiki: Build Karpathy's LLM Wiki with Claude Code. L1/L2 cache architecture. Logseq + Obsidian support

by MehmetGoekce · Claude Skill · ★ 147

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

How to install llm-wiki

git clone https://github.com/MehmetGoekce/llm-wiki.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 70/100

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.

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.

aiclaude-codedeveloper-toolskarpathyknowledge-managementllmlogseqobsidianproductivitywiki

Quick Facts

Stars147
Forks24
LanguageShell
CategoryClaude Skill
LicenseMIT
Quality Score70/100
Open Issues2
Last Updated2026-10-11
Created2026-04-08
Platformsclaude-code, cli
Est. Tokens~14k

About llm-wiki

llm-wiki Build Karpathy's LLM Wiki with Claude Code. Two-layer cache architecture (L1/L2). Supports Logseq and Obsidian. What is this? ![Wiki grap

Compatible Skills

These tools work well together with llm-wiki for enhanced workflows:

  • llm-wiki-skill — semantic(0.60)+complementary+same_lang+similar_pop+shared_platform (66%)
  • swarmvault — semantic(0.46)+complementary+rare_topics+similar_pop+shared_platform (65%)
  • llm-wiki — semantic(0.21)+complementary+rare_topics+same_lang+similar_pop+shared_platform (62%)
  • llm-wiki — semantic(0.47)+complementary+rare_topics+similar_pop+shared_platform (60%)

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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:

View on GitHub → Browse Claude Skill tools