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juitem/llm-wiki

Developer tools

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3 stars 품질 48 트렌드 10MIT

based on karpathy https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f

개요

- 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

원문 README의 일부입니다. 원문 보기.

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:

  1. Compile-first — Don’t just answer. Write conclusions into wiki pages.
  2. Writeback is mandatory — Every decision goes back to the wiki. Every single one.
  3. Wiki before RAG — Under ~100 docs, LLM reads directly. No vector DB needed.
  4. Obsidian is replaceable, the paradigm is not — The engine is LLM + filesystem + markdown.
  5. 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
GitHub에서 전체 README 보기

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