graph-wiki

A digital brain you grow with your AI agent.

It remembers what you figure out, connects it to what you already know,
and answers when you ask.

English | 简体中文

You solve a hard problem on a Tuesday. Three months later, in a different repo, you solve it again from scratch — because the answer lived in a chat log you'll never find.

This fixes that. Point it at a folder, tell your agent what to remember, and it compiles what you learn into interconnected markdown you own. The pattern comes from Andrej Karpathy's LLM Wiki gist: compile knowledge once and keep it current, instead of asking an LLM the same questions forever or re-running RAG every time.

Your second brain. Your AI agent is how you grow it.

Every skill here is a markdown file that any agent — Claude Code, Cursor, Codex, Windsurf, Gemini CLI, and many more — reads and runs. Deterministic local helpers handle hashing and bounded text ranges; there is no hosted runtime, API key, or vendor lock-in.

60 seconds

pip install graph-wiki-skills
cd /path/to/your/project
graph-wiki setup --vault ~/brain

Setup installs skills only in the current project. Open that project in your agent and say "set up my wiki".

Prefer not to touch a terminal? Give your agent this and it'll do the whole thing:

https://github.com/wenxueliu/graph-wiki — set up my wiki

Other paths — git clone, Skills CLI, multiple vaults → Installation

What you actually do

Feed it. Local Markdown, plain-text, and reStructuredText documents are ingested through a resumable, context-bounded pipeline. Agent histories and web research use their dedicated skills.

/wiki-folder-ingest ~/research
/wiki-update                        # distill the repo you're standing in
/wiki-capture                       # save this conversation
/wiki-history-ingest claude         # mine everything you've ever asked Claude

Ask it. Answers come back with [[wikilink]] citations, not vibes.

/wiki-query what do I know about rate limiting?
/wiki-narrate MCP security          # a cited briefing on a topic
/wiki-digest week                   # what did I learn this week?

Find that session you can't name.

graph-wiki sessions-build
graph-wiki sessions-query "the auth bug with the weird retry loop"

Keep it honest. The vault gets messy on its own; these clean it.

/wiki-lint            # broken links, orphans, contradictions
/wiki-dedup           # "RSC" and "React Server Components" are one page now
/cross-linker         # weave new pages into the graph
/wiki-status          # what's ingested, what's pending, where the hubs are

All 50 skills → Skills Reference

See it

Open the vault in Obsidian and hit the graph view (Cmd/Ctrl+P → "Open graph view"). Say "color my graph" and it tints nodes by tag, category, or visibility.

Or export the whole graph to graph.json, GraphML (Gephi/yEd), Neo4j Cypher, or a self-contained interactive graph.html.

Why this and not a notes folder

• It compiles, it doesn't accumulate. New knowledge merges into existing pages. Contradictions get flagged. Nothing gets duplicated.
• It only reads what changed. A manifest tracks every source ingested, so the second run processes the delta — not your whole library again.
• You can tell knowledge from guessing. Every claim is tagged extracted, ^[inferred], or ^[ambiguous], and lint flags pages drifting into speculation.
• Queries stay cheap as it grows. Titles, tags, and summaries get read before page bodies. 20 pages or 2000, roughly the same cost.
• It's yours. Plain markdown in a folder. Push it to a private repo, open it in Obsidian, grep it, delete it. No service, no lock-in, nothing leaves your machine.
• Works where you already work. One bundled .skills/ directory, linked into each selected agent under your project.

More → Architecture

Documentation

These docs are available in English and Simplified Chinese.

Contributing

This is early. The skills work, but there's room to make the brain smarter — better cross-referencing, sharper deduplication, bigger vaults, new ingest sources. If you have a workflow that could be a skill, PRs are welcome.

License

MIT