llm-wiki

Karpathy-style LLM knowledge base Agent Skill for OpenClaw/Codex. Experimental — will iterate over time.

安装方式
CLI
npx skills add https://github.com/lewislulu/llm-wiki-skill --skill llm-wiki

使用 CLI 安装这个技能,并在你的工作区中直接复用对应的 SKILL.md 工作流。

最后更新于 4/23/2026

llm-wiki

An OpenClaw / Codex Agent Skill for building Karpathy-style LLM knowledge bases.

Experimental skill — will iterate over time.
Please send your feedbacks in github issues.

Inspired by Andrej Karpathy's llm-wiki Gist and the community's work building on it.

What this is

Instead of RAG (re-retrieving raw docs on every query), this pattern has the LLM compile raw sources into a persistent, cross-linked Markdown wiki. Every compile, ingest, query, lint, and audit pass makes the wiki richer. Knowledge compounds over time.

  • You own: sourcing raw material, asking good questions, steering direction, filing feedback on things the AI got wrong.
  • LLM owns: all writing, cross-referencing, filing, bookkeeping, and acting on your feedback.

The skill comes with two companion tools in this repo:

  • plugins/obsidian-audit/ — an Obsidian plugin: select text in any page, leave a comment with severity, the comment is written into audit/ as an anchored markdown file.
  • web/ — a local Node.js preview server: renders the wiki with mermaid, KaTeX, and wikilinks, lets you select + file feedback from the browser, and shows open audits per page.

Both tools share a single TypeScript library (audit-shared/) so audit files written from Obsidian and the web viewer are byte-identical in shape.

Install

# Copy the skill into your agent's skills directory
cp -r llm-wiki/ ~/.claude/skills/llm-wiki/
# or for Codex
cp -r llm-wiki/ ~/.codex/skills/llm-wiki/

Then reference it in your agent config, or simply paste llm-wiki/SKILL.md into your agent context.

Quick start

# 1. Scaffold a new wiki
python3 llm-wiki/scripts/scaffold.py ~/my-wiki "My Research Topic"

# 2. Add a source
cp my-article.md ~/my-wiki/raw/articles/

# 3. Tell your agent: "ingest raw/articles/my-article.md"

# 4. Ask questions: "what does the wiki say about X?"

# 5. Run lint periodically
python3 llm-wiki/scripts/lint_wiki.py ~/my-wiki

# 6. File a comment from the web viewer or Obsidian plugin, then process it
python3 llm-wiki/scripts/audit_review.py ~/my-wiki --open
# then tell the agent: "audit: process the open comments"

Repo contents

llm-wiki-skill/
├── llm-wiki/                    ← The skill
│   ├── SKILL.md                 ← Main skill file (read by agent)
│   ├── references/
│   │   ├── schema-guide.md      ← CLAUDE.md schema template
│   │   ├── article-guide.md     ← Article writing (divide & conquer, mermaid, KaTeX)
│   │   ├── log-guide.md         ← log/ folder convention
│   │   ├── audit-guide.md       ← audit file format + processing workflow
│   │   └── tooling-tips.md      ← Obsidian, qmd, plugin + web
│   └── scripts/
│       ├── scaffold.py          ← Bootstrap new wiki directory
│       ├── lint_wiki.py         ← 7-pass health check (links, audit, log shape)
│       └── audit_review.py      ← Group open/resolved audits by target
├── audit-shared/                ← Shared TypeScript library
│   └── src/{schema,anchor,id,serialize,index}.ts
├── plugins/obsidian-audit/      ← Obsidian plugin — file audit from vault
└── web/                         ← Local Node.js preview + feedback server
    ├── server/                  ← Express + markdown-it + KaTeX + wikilinks
    └── client/                  ← Vanilla-TS SPA with mermaid + selection popover

Running the web viewer

# one-time setup (builds audit-shared, installs deps, bundles client)
cd audit-shared && npm install && npm run build && cd ..
cd web && npm install && npm run build && cd ..

# start the server against a wiki
cd web
npm start -- --wiki "/path/to/your/wiki-root" --port 4175
# open http://127.0.0.1:4175

Building the Obsidian plugin

cd audit-shared && npm install && npm run build && cd ..
cd plugins/obsidian-audit
npm install
npm run build
npm run link -- "/path/to/your/Obsidian vault"
# Enable 'LLM Wiki Audit' in Obsidian → Settings → Community plugins.

Use cases

  • Research deep-dive — reading papers/articles on a topic over weeks
  • Personal wiki — Farzapedia-style: journal entries compiled into personal encyclopedia
  • Team knowledge base — fed by Slack threads, meeting notes, docs
  • Reading companion — building a rich companion wiki as you read a book

License

MIT