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.

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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.

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Works With Any AI

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.

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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

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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

# Preview first (dry run)
python3 LLM-wiki/skills/knowledge-system-bootstrap/scripts/bootstrap_knowledge_system.py /path/to/your-project "My Project" --dry-run

# Then run for real
python3 LLM-wiki/skills/knowledge-system-bootstrap/scripts/bootstrap_knowledge_system.py /path/to/your-project "My Project"

Generates 20 files: wiki structure, manifests, validation scripts, CI workflow, and configs for 4 AI platforms.

Option C: Install as Codex skill

Use $skill-installer to install https://github.com/Ss1024sS/LLM-wiki/tree/main/skills/knowledge-system-bootstrap

Or manually:

bash scripts/install-codex-skill.sh

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How Do I Know It’s Working?

After 3 sessions, check:

• [ ] docs/wiki/log.md has 3 entries
• [ ] docs/wiki/current-status.md reflects actual project state
• [ ] AI can answer “what did we do last time?” without you re-explaining
• [ ] Decisions from session 1 are still accessible in session 3

See examples/demo-project/ for what a healthy wiki looks like after a few sessions.

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Don’t Do These Things

• Don’t put raw PDFs/XLSX into Git. That’s a binary junk pile, not version control.
• Don’t treat chat history as memory. It evaporates.
• Don’t build a RAG pipeline before your raw docs are even organized.
• Don’t change code without updating the wiki. That’s incomplete work.

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Read More

• UNIVERSAL.md — Setup guide + migration path + FAQ + templates for every AI
• docs/knowledge-system-playbook.md — Full rationale (CN+EN), GitHub/raw split, provenance roadmap
• examples/demo-project/ — What a bootstrapped project looks like after 3 sessions

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License

MIT. Use it, fork it, adapt it.

Credits

Paradigm from Andrej Karpathy. Engineering practice by Ss1024sS.