based on karpathy https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f
概览
- New session. AI remembers nothing. You spend 20 minutes re-explaining. - "What did we decide about the pricing formula?" Buried in a 3-hour chat log. - Five docs in Notion, three in Google Drive, two Excel files on desktop. Which one is current? This repo fixes that. Not with another knowledge management theory post, but with a drop-in engineering practice that works across AI platforms. Baseline from Andrej Karpathy's LLM Wiki pattern: the correct way to use LLMs is not Q&A, it's . 1. — Don't just answer. Write conclusions into wiki pages. 2. — Every decision goes back to the wiki. Every single one. 3. — Under ~100 docs, LLM reads directly. No vector DB needed. 4. — The engine is LLM + filesystem + markdown. 5. — Your wiki of decisions and formulas is worth more than the code it generates. Works with Claude Code, Codex, Cursor, Windsurf. Generates 20 files: wiki structure, manifests, validation scripts, CI workflow, and configs for 4 AI platforms. - [ ] docs/wiki/log.
README
以下为原文 README 节选,完整内容请查看原文。
LLM Wiki
You’ve been here before:
- New session. AI remembers nothing. You spend 20 minutes re-explaining.
- “What did we decide about the pricing formula?” Buried in a 3-hour chat log.
- Five docs in Notion, three in Google Drive, two Excel files on desktop. Which one is current?
This repo fixes that. Not with another knowledge management theory post, but with a drop-in engineering practice that works across AI platforms.
The Idea
Baseline from Andrej Karpathy’s LLM Wiki pattern: the correct way to use LLMs is not Q&A, it’s compilation.
raw/ (PDFs, Excel, client docs — immutable source material)
↓ LLM compiles
wiki/ (markdown — current consensus, continuously updated)
↓ LLM generates
code/ (the execution layer — a compiled artifact, not the truth)
Five rules:
- Compile-first — Don’t just answer. Write conclusions into wiki pages.
- Writeback is mandatory — Every decision goes back to the wiki. Every single one.
- Wiki before RAG — Under ~100 docs, LLM reads directly. No vector DB needed.
- Obsidian is replaceable, the paradigm is not — The engine is LLM + filesystem + markdown.
- Ideas outrank Code — Your wiki of decisions and formulas is worth more than the code it generates.
Works With Any AI
| Platform | Config File | Status |
|---|---|---|
| Claude Code | CLAUDE.md |
Full template in UNIVERSAL.md |
| Codex | AGENTS.md |
Native skill + template |
| Cursor | .cursorrules |
Template in UNIVERSAL.md |
| Windsurf | .windsurfrules |
Template in UNIVERSAL.md |
| ChatGPT | Manual paste | Workflow in UNIVERSAL.md |
Just send your AI this link and tell it to read UNIVERSAL.md:
Read https://github.com/Ss1024sS/LLM-wiki/blob/main/UNIVERSAL.md and set up the knowledge system for this project.
What’s In This Repo
LLM-wiki/
├── UNIVERSAL.md # Start here. Setup guide for any AI platform.
├── docs/
│ └── knowledge-system-playbook.md # Full rationale (Chinese + English), provenance roadmap
├── examples/
│ └── demo-project/ # What a bootstrapped project looks like after 3 sessions
│ ├── CLAUDE.md / AGENTS.md / .cursorrules / .windsurfrules
│ ├── docs/wiki/ # 5 wiki pages with realistic content
│ └── manifests/raw_sources.csv
├── skills/
│ └── knowledge-system-bootstrap/ # Codex skill for automated scaffolding
│ └── scripts/bootstrap_knowledge_system.py # Generates 20 files in one command
└── scripts/
└── install-codex-skill.sh
Quick Start
Option A: Tell your AI to do it
Read https://github.com/Ss1024sS/LLM-wiki/blob/main/UNIVERSAL.md and set up the knowledge system for this project.
Works with Claude Code, Codex, Cursor, Windsurf.
Option B: Run the bootstrap script
git clone https://github.com/Ss1024sS/LLM-wiki.git
推荐工具
换一个关键词,或者移除筛选条件。
安装
npx skillfish add juitem/llm-wiki