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LLM Wiki

Brain app

First committed by jackwener

Published in jackwener/llm-wiki. Publisher page

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What it is

Agent-native LLM Wiki — AI-maintained knowledge base with Obsidian compatibility
— the description by jackwener

AutoGineer overview, from the brain’s own files

LLM Wiki is a CLI-driven framework designed to transform raw documents into a structured, interconnected knowledge base. Unlike traditional RAG systems that re-process raw data, this tool uses AI agents to compile information into persistent wiki pages. It provides a standardized vault structure, including bootstrap files for AI agents and operation-specific skills for ingesting, querying, linting, and researching. The system is designed to be human-readable via Obsidian, while remaining fully operational for AI agents through slash commands.

How it works

Memory model: LLM wiki

AutoGineer overview, from the brain’s own files

The system initializes a local vault containing a wiki directory for structured content and a sources directory for raw documents. It uses agent-specific bootstrap files (CLAUDE.md and AGENTS.md) to guide AI agents in managing the vault. Four core skills (ingest, query, lint, research) are installed into the agent's workspace, allowing the agent to read sources, update wiki pages, perform health checks, and conduct external research. A CLI tool manages the vault, provides graph analysis, and supports optional vector search via DB9 integration.

Folders
FolderRoleFiles
skillsagent-instructions4
srcsource-code14
testtests7

Requirements

Runtimes: Node.js >=20

Databases: PostgreSQL

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Google Antigravity, Kiro, OpenCode, Amp, Roo Code, Cline, JetBrains Junie, Devin (ex-Windsurf)

Set it up

  1. Not scanned by AutoGineerAutoGineer could not scan everything it runs. https://www.npmjs.com/package/@jackwener/llm-wiki
  2. First committed by jackwener. Published in jackwener/llm-wiki. Licence: Apache License 2.0.

Usage

AutoGineer overview, from the brain’s own files

  • Initialize a new wiki vault in a local directory.
  • Use AI agents to ingest raw documents into structured wiki pages.
  • Query the wiki to synthesize answers and compound knowledge.
  • Perform automated health checks on the wiki graph.
  • Conduct external research and integrate findings into the wiki.
  • Analyze the wikilink graph to identify clusters, hubs, and orphans.
“/ingest sources/project-notes.md”
The agent reads the source file, extracts key entities, and updates or creates corresponding wiki pages with wikilinks.
“/query "What is the current status of the API migration?"”
The agent searches the wiki, synthesizes an answer based on existing pages, and writes back new insights to the wiki.
“/lint”
The agent performs a health check to identify broken links, orphans, and contradictions, and auto-fixes safe issues.
“/research "latest trends in edge computing"”
The agent searches the web, saves relevant sources, ingests them into the wiki, and synthesizes a report.

Documentation by jackwener

LLM Wiki

Agent-native persistent knowledge management — compile knowledge once, query forever.

Based on Andrej Karpathy's LLM Wiki pattern.

What is this?

LLM Wiki is a CLI tool + AI Agent skill system that maintains an evolving, interconnected Markdown knowledge base. Instead of traditional RAG (re-deriving answers from raw documents each time), LLM Wiki compiles knowledge into structured wiki pages that AI agents maintain and grow over time.

Key principle: The tool itself doesn't call LLMs. It provides skill files that let any AI agent (Claude Code, Codex, etc.) operate the wiki. Obsidian is the human interface — no self-built GUI.

Quick Start

# Install globally
npm install -g @jackwener/llm-wiki

# Initialize a new wiki vault
mkdir my-wiki && cd my-wiki
llm-wiki init

# Now use your AI agent:
#   /ingest sources/some-article.md
#   /query "What do we know about X?"
#   /lint
#   /research "deep dive on Y"

llm-wiki init is the only setup command — it creates the vault files, the agent bootstrap files (CLAUDE.md, AGENTS.md), and installs the four bundled operation skills into .claude/skills/ and .agents/skills/ in one step.

After upgrading the package, refresh the installed skill files with:

llm-wiki skill install

Vault Structure

my-wiki/
├── CLAUDE.md              # Agent bootstrap for Claude Code (auto-loaded)
├── AGENTS.md              # Agent bootstrap for Codex (auto-loaded)
├── wiki-purpose.md        # Wiki scope and audience
├── wiki-schema.md         # Page types, naming conventions, frontmatter rules
├── wiki-log.md            # Append-only operation log
├── wiki/                  # AI-maintained wiki pages (Obsidian-compatible)
├── sources/               # Raw, immutable source documents
│   └── YYYY-MM-DD/        # Date-based storage
├── .claude/
│   └── skills/
│       ├── ingest/SKILL.md    # Source → wiki pages
│       ├── query/SKILL.md     # Evidence-grounded wiki answers
│       ├── lint/SKILL.md      # Wiki health checks
│       └── research/SKILL.md  # External research → wiki
├── .agents/
│   └── skills/
│       ├── ingest/SKILL.md
│       ├── query/SKILL.md
│       ├── lint/SKILL.md
│       └── research/SKILL.md
└── .llm-wiki/
    ├── config.toml        # Vault configuration
    └── sync-state.json    # Incremental sync tracking

llm-wiki init generates every file above in one step.

Agent Bootstrap

LLM Wiki uses a two-file pattern so any AI agent can operate the vault out of the box, with no manual setup beyond llm-wiki init:

1. Entry files — CLAUDE.md and AGENTS.md (vault root)

Short bootstrap documents that are auto-loaded on every session start — Claude Code reads CLAUDE.md, Codex reads AGENTS.md. They tell the agent:

  • this workspace is an LLM Wiki vault

  • where to find wiki-purpose.md and wiki-schema.md

  • which /ingest, /query, /lint, /research commands are available

  • which of the four operation skills to load (ingest, query, lint, or research)

  • a short CLI cheat-sheet and the core operating rules

Because they are auto-loaded, they are intentionally small — a few dozen lines — to keep session-start context cheap.

2. Operation skills — .claude/skills/{ingest,query,lint,research}/SKILL.md and .agents/skills/{ingest,query,lint,research}/SKILL.md

Each operation has a focused playbook, loaded on demand when the matching command is invoked. The skills contain the relevant procedure, page schemas, frontmatter rules, and invariants (for example, source immutability and the wiki-log.md + sync tail rule). Every skill follows the Agent Skills specification (skills/<name>/SKILL.md with YAML frontmatter), so spec-compliant agents (Claude Code, Codex, Amp, and others) discover them independently. All four are installed into both platform directories for a shared vault setup.

Upgrading. llm-wiki init is the only setup command — it writes both entry files and installs the four skills. After upgrading the npm package, run llm-wiki skill install to refresh the skill files. Your edits to CLAUDE.md / AGENTS.md are preserved across reinstalls.

Upgrading to the four-skill layout. Run llm-wiki skill install to add the operation skills. If you previously installed the legacy monolithic skill, remove it afterwards so its broad trigger does not overlap the four focused skills:

rm -rf .claude/skills/llm-wiki .agents/skills/llm-wiki

Operations

The skill exposes four operations, each invoked as a slash command:

OperationUsageWhat it does
ingest/ingest <path>Read source → extract entities → create/update wiki pages with [[wikilinks]]
query/query <question>Search wiki → synthesize answer → write back valuable insights (knowledge compounding)
lint/lintHealth check: broken links, orphans, contradictions, stale content → auto-fix safe issues
research/research <topic>Go beyond wiki: search web → save sources → ingest → synthesize report

CLI Commands

CommandDescription
llm-wiki init [dir]Initialize a new wiki vault
llm-wiki search <query>BM25 keyword search (+ DB9 vector search if configured)
llm-wiki graph [--json]Analyze wikilink graph: communities, hubs, orphans, wanted pages
llm-wiki statusWiki statistics and health summary
llm-wiki sync [--dry-run]Track changes (mtime + SHA256), sync embeddings to DB9
llm-wiki skill installInstall all skills to your AI agent workspace
llm-wiki skill listList available skills
llm-wiki skill show <name>Print skill content to stdout

BM25 keyword search with CJK bigram tokenization (Chinese/Japanese/Korean support).

When DB9 is configured, search becomes hybrid: BM25 + vector similarity, merged via Reciprocal Rank Fusion (RRF, K=60).

llm-wiki search "distributed consensus"
llm-wiki search "分布式共识" -n 5
llm-wiki search "raft algorithm" --bm25-only

Graph Analysis

Analyzes the [[wikilink]] graph to find structure in your knowledge:

  • Communities — Topic clusters detected via label propagation

  • Hub pages — Most connected pages (high incoming + outgoing links)

  • Orphan pages — Pages with no incoming links

  • Wanted pages — Pages linked but not yet created

llm-wiki graph          # Human-readable output
llm-wiki graph --json   # Machine-readable for programmatic use

DB9 Integration (Optional)

DB9 adds vector search and cloud sync:

  • Server-side embeddings via embedding(text)::vector(1024) — no local model needed

  • HNSW vector index for semantic similarity search

  • Reverse source lookup: "which wiki pages reference this source?"

Enable by adding to .llm-wiki/config.toml:

[db9]
url = "your-db9-connection-string"

Then run llm-wiki sync to upload embeddings.

Obsidian Compatibility

The wiki/ directory is a standard Obsidian vault:

  • YAML frontmatter

  • [[wikilink]] cross-references

  • Open directly in Obsidian for browsing, graph view, and editing

Configuration

.llm-wiki/config.toml:

[vault]
name = "My Wiki"
language = "en"

# Optional: DB9 for vector search + cloud sync
# [db9]
# url = "your-db9-connection-string"

Tech Stack

License

Apache-2.0

LICENSE

Shown as plain text.

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34 files · 31 not served here

Advantages

  • Compiles knowledge into structured pages rather than re-deriving answers from raw documents, leading to knowledge compounding. evidence: README.md
  • Provides native Obsidian compatibility for human browsing, graph visualization, and manual editing. evidence: README.md

Limitations

  • Requires manual removal of legacy skill files if upgrading from a monolithic skill layout. evidence: README.md
  • Vector search and cloud sync capabilities require an external DB9 configuration. evidence: README.md

Security

Risk level: No risk found

This brain runs

Agent instructions that run or fetch something (5)

  • skills/ingest/SKILL.md: Run `llm-wiki sync` after the changes.
  • skills/lint/SKILL.md: Run `llm-wiki sync` when changes were made.
  • skills/query/SKILL.md: Link it to the contributing pages, append a `query` entry to `wiki-log.md`, and run `llm-wiki sync`.
  • skills/research/SKILL.md: Run the `query` workflow first to determine what is known and define a precise research question.
  • skills/research/SKILL.md: Then append a `research` entry to `wiki-log.md` and run `llm-wiki sync`.

Attribution and licence

First committed by
GitHub avatar of jackwenerjackwener
Publisher
jakevin (Community)
Source
jackwener/llm-wiki · commit cd54666
Licence
Apache License 2.0 (files served here)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
jackwener

AutoGineer is not affiliated with or endorsed by jackwener.

Install from source

31 of its files are not served here, so there is no ZIP. jackwener/llm-wiki at cd54666