baissarienterprises-llm-wiki 0.1.1

Knowledge base operating system — AI agents compile raw sources into persistent, cross-linked Markdown wikis

LLM Wiki Monorepo

pip install baissarienterprises-llm-wiki

A production-grade knowledge base operating system. AI agents compile raw sources into persistent, cross-linked Markdown wikis. Knowledge compounds over time. No database. No API lock-in. Just files.

Instead of re-retrieving raw documents on every query (RAG), this system compiles sources into a living wiki that agents maintain automatically. Clone anywhere, run with any AI agent, pull on any machine.

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Features

Area	Capabilities
Ingestion	Two-stage chain-of-thought ingest with SHA256 caching. Multi-step agent loop. Batch processing. Deep research (web search → fetch → ingest → synthesize).
Quality	15-pass automated lint: dead links, orphans, frontmatter validation, contradictions, source drift, page size, log rotation. New: stale page detection when raw sources change.
Graph	Knowledge graph engine (TypeScript): Louvain community detection, 4-signal relevance model, surprising connections, knowledge gaps. Pure Python fallback.
Backup	Snapshot, restore, integrity verification, automatic pruning. One-command --auto for safe state.
Link Suggestions	Entity extraction from frontmatter/headings/bold terms. 4-signal scoring. Automatic wikilink insertion (--apply).
Search	BM25 full-text search (TypeScript, zero deps). Web viewer search bar with ranked results. Graph search.
MCP Server	8 stdio tools for programmatic access. New: serve multiple wikis from one server (--projects). Integrates with Claude Desktop, Codex, Cursor.
Templates	19 domain-specific wiki scaffolds with consistent schemas. Research, codebase, finance, ML, cybersecurity, medicine, and more.
Web Viewer	Local preview with KaTeX, mermaid, wikilink resolution. New: search bar, graph insights panel.
Browser Extension	Chrome web clipper with Readability + Turndown. New: auto-trigger ingest after clip.
CI/CD	GitHub Actions: Python syntax checks, TypeScript builds, full integration test (scaffold → lint → graph build → insights).

Quick Start

# Install from PyPI
pip install baissarienterprises-llm-wiki

# Or install from source (for development)
git clone https://github.com/JeanBaissari/llm-wiki-monorepo.git
cd llm-wiki-monorepo
bash install.sh

# Scaffold a wiki
llm-wiki scaffold ~/my-wiki "My Research" --template research

# Ingest a source (two-step agent loop)
llm-wiki ingest ~/my-wiki raw/articles/my-source.md

# Check quality
llm-wiki lint ~/my-wiki

# Discover connections
llm-wiki insights ~/my-wiki

# Start MCP server (for Claude Desktop, Codex, etc.)
llm-wiki serve ~/my-wiki

Architecture

wiki/ directory  ← shared state (Markdown files)
     │
      ├── Agent Skill + Python Scripts   → 12 scripts: scaffold, ingest, lint,
      │                                     insights, backup, link-suggest,
      │                                     deep-research, audit, benchmark,
      │                                     migrate-log, test-e2e, test-blocks
     ├── MCP Server (stdio)             → programmatic access, 8 tools,
     │                                     single or multi-wiki mode
     ├── Graph Engine (Node.js)         → relevance model, Louvain, insights
     ├── Web Viewer + Obsidian Plugin   → human browsing + feedback
     ├── Browser Extension              → web clipping + auto-ingest
     └── templates/                     → 19 domain schemas

Packages

Package	Language	Purpose
skill/	Python + Markdown	Agent skill (8 operations) + 12 Python scripts + 11 reference docs
mcp-server/	TypeScript	MCP server — 8 tools, single or multi-wiki mode
graph-engine/	TypeScript	Knowledge graph — relevance, Louvain communities, insights
templates/	Markdown + JSON	19 domain-specific project templates (audited, consistent)
web-viewer/	TypeScript	Preview server with search + graph insights panel
extension/	JavaScript	Chrome web clipper with auto-ingest
audit-shared/	TypeScript	Shared audit file format library
plugins/obsidian-audit/	TypeScript	Obsidian plugin — file feedback from vault

Templates (19 domains)

research codebase finance algorithmic-trading cybersecurity machine-learning prompt-engineering copywriting marketing design-systems architecture crypto commodities decompilers medicine developer-tools personal-growth reading business

Every template provides: PURPOSE.md (scope + goals), SCHEMA.md → CLAUDE.md (page types, conventions, frontmatter, cross-referencing, contradiction handling), extra-dirs.json (domain directories).

Documentation

File	What it covers
README.md	You are here
QUICKGUIDE.md	Every command with real examples
AGENTS.md	Architecture, conventions, build/test commands
INDEX.md	Complete file tree with descriptions
VERSIONING.md	Semantic versioning policy and release process
PURPOSE.md	Why this system exists
skill/references/	11 detailed reference guides

Requirements

• Python 3.10+ — for all skill scripts
• Node.js 18+ — for MCP server, graph engine, web viewer
• npm — for package management

License

MIT