@micuintus/llm-wiki

@micuintus/llm-wiki

As minimal as Pi.

Karpathy's LLM Wiki pattern as a skill. One file. ~6 KB. No dependencies, no CLI, no web server, no Obsidian plugin.

The user curates sources. The LLM does the bookkeeping — summarizing, cross-linking, flagging contradictions. Knowledge compounds instead of being re-derived from raw chunks on every query.

Install

Pi (npm):

pi install npm:@micuintus/llm-wiki

Pi (GitHub):

pi install git:github.com/micuintus/llm-wiki

Everyone else:

# Copy the skill directory (SKILL.md + its lazy-loaded recipes). Start ingesting.
curl -L https://github.com/micuintus/llm-wiki/archive/refs/heads/main.tar.gz \
  | tar -xz --strip-components=1 -C ~/your-agent/skills/ llm-wiki-main/llm-wiki

That lands ~/your-agent/skills/llm-wiki/SKILL.md with its references/ alongside, so the lazy-loaded recipes resolve. (SKILL.md is the only always-loaded file — the rest is read on demand.)

What you get

One file: SKILL.md. Everything else is lazy-loaded — referenced by path in the skill text, never loaded until you actually need it.

Agent session recipes (lazy-loaded):

• Pi sessions — JSONL trees with fork detection

• Claude Code / opencode / Gemini CLI — transcripts, SQLite, JSON

Code-source recipe (lazy-loaded) — grep+cite-by-line conventions for when the source is a repository rather than prose.

Web chat ingestion — Claude.ai, ChatGPT, Gemini via CDP. A separate package: ingest-web-chat (pi install npm:@micuintus/ingest-web-chat).

The lazy-loaded subskills sleep forever until you wake them. (Don't forget to kiss these sleeping beauties.)

Use cases

• Reading a book — chapter summaries, character/concept pages, theme threads, all interlinked.

• Researching a topic across many papers — concept pages cite multiple sources; contradictions surface instead of getting lost.

• Mapping an ecosystem (e.g., the Pi extension landscape) — taxonomies, comparisons, primitive mappings.

• Documenting your own project — decisions, architecture, bugs, open questions, all linked through one schema.

Why this exists

Most wiki implementations ship a CLI, a web server, a static site generator, and 2,000 tests. That's fine if you want infrastructure. This is for people who want a pattern — a convention the agent already understands — and nothing else.

Conventions live in the skill text, not in code. The wiki itself is plain markdown on disk — if you ever switch agents or skills, your data travels with you unchanged.

Related work

The Karpathy gist has spawned a diverse ecosystem. Every implementation below shares the same three-layer foundation (raw sources → compiled wiki → schema); they differ in shape (how the agent consumes the pattern) and weight (how much tooling ships alongside the skill).

What makes @micuintus/llm-wiki different: it is a pure skill — a single SKILL.md (~6 KB) plus lazy-loaded reference files that any agent (Pi, Claude Code, Codex, Cursor) can read directly. No runtime dependencies, no generated code, no CLI to install, no web server to run, no Obsidian plugin to manage. Conventions are enforced by the skill text itself, not by code. If you want the lightest possible commitment to the pattern — copy one file, start ingesting — this is it. Other implementations add value in exchange for weight: IDE plugins, static-site generators, code-based guardrails, or self-driving agents. Pick the shape that matches your workflow.

Changelog

See CHANGELOG.md.

License

MIT