Turn any LLM CLI into a disciplined wiki maintainer. You curate sources and ask questions. The LLM reads, files, cross-references, flags contradictions, and keeps a living synthesis current. Knowledge compounds instead of being re-derived by RAG on every query.

The idea in one paragraph

Most LLM+docs workflows are RAG: retrieve fragments at query time, synthesize from scratch, forget. The wiki is compounding. The LLM reads each source once and integrates it into a persistent, interlinked Obsidian vault — updating entity pages, revising concept pages, flagging contradictions, and strengthening the synthesis. The wiki is the compiled artifact; RAG is the just-in-time retrieval. This plugin gives the LLM the discipline (SKILL.md), the delegation (sub-agents), the triggers (slash commands), and the bookkeeping (Python tools) to do the job.

What's in the box

Piece	What it does
SKILL.md	Master skill doc — architecture, workflows, iron rules, cross-tool compat. Has `context: fork` so other skills can chain into it.
3 sub-agents	`wiki-ingestor`, `wiki-librarian`, `wiki-linter`
5 slash commands	`/wiki-init`, `/wiki-ingest`, `/wiki-query`, `/wiki-lint`, `/wiki-log`
8 Python tools	Standard library only: `init_vault`, `ingest_source`, `update_index`, `append_log`, `wiki_search` (BM25), `lint_wiki`, `graph_analyzer`, `export_marp`
8 reference docs	Schema, page formats, ingest/query/lint workflows, Obsidian setup, cross-tool setup, Memex principles
Vault templates	`CLAUDE.md`, `AGENTS.md`, `.cursorrules`, `index.md`, `log.md`, plus 5 page templates (entity, concept, source, comparison, synthesis)
Example vault	A small worked example on "LLM interpretability"

Quick start

# 1. Initialize a vault
python scripts/init_vault.py --path ~/vaults/research --topic "LLM interpretability" --tool all

# 2. Open in Obsidian
open -a Obsidian ~/vaults/research

# 3. Drop a source into raw/ and ingest
cp ~/Downloads/paper.pdf ~/vaults/research/raw/papers/
cd ~/vaults/research
# in Claude Code:
> /wiki-ingest raw/papers/paper.pdf

# 4. Ask questions
> /wiki-query "what does the paper say about sparse features?"

# 5. Health check
> /wiki-lint

Cross-tool compatibility

The scripts are pure Python stdlib — they run anywhere. Only the schema loader changes per tool:

Tool	Loader file
Claude Code	`CLAUDE.md`
Codex CLI (OpenAI)	`AGENTS.md`
Cursor (modern)	`AGENTS.md`
Cursor (legacy)	`.cursorrules`
Antigravity (Google)	`AGENTS.md`
OpenCode / Pi	`AGENTS.md`
Gemini CLI	`AGENTS.md`

init_vault.py --tool all installs all three. You can run multiple CLIs against the same vault.

See references/cross-tool-setup.md for per-tool instructions.

Architecture

<vault>/
├── raw/                    # IMMUTABLE sources (you own)
├── wiki/                   # LLM-owned knowledge base
│   ├── index.md            # content catalog
│   ├── log.md              # append-only timeline
│   ├── entities/           # people, orgs, places, products
│   ├── concepts/           # ideas, theories, frameworks
│   ├── sources/            # one summary per ingested source
│   ├── comparisons/        # cross-source analyses
│   └── synthesis/          # high-level overviews and theses
├── CLAUDE.md               # schema for Claude Code
├── AGENTS.md               # schema for Codex/Cursor/Antigravity
└── .cursorrules            # (optional) legacy Cursor

Iron rule: The LLM never edits raw/. All writes go to wiki/.

Three operations

Ingest — Read a source, discuss with user, write summary page, update 5-15 cross-referenced pages, update index, log it
Query — Read index first, drill into 3-10 pages, synthesize answer with inline citations, offer to file back as a new page
Lint — Mechanical + semantic health check; surface contradictions, orphans, stale claims, cross-reference gaps

Why not just RAG?

Plain RAG	LLM Wiki
Rediscover knowledge each query	Knowledge accumulates
Cross-references re-computed every time	Cross-references pre-written and maintained
Contradictions surface only if you ask	Contradictions flagged during ingest
Exploration disappears into chat history	Good answers re-filed as new pages
Scales by embeddings infrastructure	Scales by markdown + `index.md` + optional local search

The wiki and RAG aren't opposites — RAG can sit on top of the wiki once you outgrow index-first search.

Status

v1.0.0 — initial release. SKILL + 3 agents + 5 commands + 8 scripts + 8 references + full vault templates + example vault.

License

MIT.

Karpathy's original gist — the pattern this plugin implements
Vannevar Bush, "As We May Think" (1945) — the Memex
qmd — local hybrid search over markdown (pair with this when the wiki outgrows index.md)

README.md

llm-wiki