← 开源
lewislulu

llm-wiki-skill

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

AI EngineeringGive agent knowledgeBuild agent workflow (SDK)TypeScript
在 GitHub 打开
增长势头
+-124 小时新增 Star-0.2%
654
Star
105
Fork
+-1
本周
2
贡献者
创建于 2026-04-05 · 更新于 2026-10-16 · 今日第 19145 名
主要开发者
lulurelaxlewislulu

lewislulu/llm-wiki-skill 排名第 19,145,有 654 个星标。

同类别比较

lewislulu/llm-wiki-skill 排名第 19,145,此处有 8 个同类条目;joaoh82/rustunnel 排名第 19,143。

同类别比较
名称排名指标
thellimist/clihub19,137
语言:
Go
星标数:
666
分叉数:
39
简介:
Turn any MCP server into CLI
hanlulong/econ-writing-skill19,139
语言:
Python
星标数:
663
分叉数:
107
简介:
Agent Skill that transforms AI assistants into expert economics paper writers. Synthesizes 50+ guides by Cochrane, McCloskey, Shapiro, Head, Bellemare, Goldin, Kremer. Compatible with Claude Code and OpenAI Codex.
qixucen/atom19,141
语言:
Python
星标数:
660
分叉数:
58
简介:
[NeurIPS 2025] Atom of Thoughts for Markov LLM Test-Time Scaling
joaoh82/rustunnel19,143
语言:
Rust
星标数:
658
分叉数:
49
简介:
Self-hosted, secure tunnel server in Rust. Expose local HTTP/HTTPS/TCP/UDP services to the public internet via TLS-encrypted WebSocket. Open-source, pay-as-you-go managed option, MCP server for AI agents.
gemini-cli-extensions/workspace19,150
语言:
TypeScript
星标数:
646
分叉数:
107
简介:
Access Google Workspace when using Gemini CLI
carlos-algms/agentic.nvim19,155
语言:
Lua
星标数:
640
分叉数:
62
简介:
Agentic Chat Interface directly in Neovim with ACP providers from Claude-Code, Gemini, Codex, OpenCode, and Cursor-agent
GMGNAI/gmgn-skills19,165
语言:
Python
星标数:
614
分叉数:
105
augmentedstartups/AS-One19,166
语言:
Python
星标数:
607
分叉数:
106
简介:
Easy & Modular Computer Vision Detectors, Trackers & SAM - Run YOLOv9,v8,v7,v6,v5,R,X in under 10 lines of code.
关键参数与规格
关键参数与规格
参数值
语言TypeScript
类别AI Engineering
子类别Give agent knowledge,Build agent workflow (SDK)
关键词Document understanding, Learning from user feedback, Memory management
星标数654
分叉数105
贡献者2
创建日期2026-04-05
更新日期2026-10-16
1 天星标数-1
7 天星标数-1
排名19,145
上次排名15,239
排名变化-3,906
7 天星标排名17,772
上次 7 天星标排名15,000
7 天星标排名变化-2,772
总星标排名15,685
上次总星标排名15,625
总星标排名变化-60
README 摘录

llm-wiki **An OpenClaw / Codex Agent Skill for building Karpathy-style LLM knowledge bases. 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 , , , , and pass makes the wiki richer. 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. 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 ( ) so audit files written from Obsidian and the web viewer are byte-identical in shape. Install Then reference it in your agent config, or simply paste into

your agent context.

常见问题

记录了多少星标和分叉?

lewislulu/llm-wiki-skill 有 654 个星标和 105 个分叉。

仓库排名是多少?

lewislulu/llm-wiki-skill 排名第 19,145。

记录的语言和类别是什么?

lewislulu/llm-wiki-skill 有 654 个星标,语言为 TypeScript,类别为 AI Engineering。

哪个仓库排名最接近?

lewislulu/llm-wiki-skill 与 joaoh82/rustunnel 比较,后者排名第 19,143,有 658 个星标。

README

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

Related work

License

MIT

排名相邻
同类项目