What problem does it solve? Large codebases lack structured, navigable documentation for AI agents and developers, forcing repeated rediscovery of project structure, conventions, and entry points. ## Core Features & Use Cases - Hierarchical AGENTS.md Generation: Creates a root knowledge base plus subdirectory files only where complexity scoring (file count, symbol density, reference centrality) justifies them. - Concurrent Discovery: Runs bash structural analysis, LSP symbol/reference mapping, and dynamically scaled exploration agents in parallel based on project size. - Update or Regenerate Modes: Reads existing AGENTS.md files to preserve context, then updates in place or rebuilds from scratch with --create-new. - Use Case: Point it at a monorepo with multiple languages and deep nesting; it measures scale, spawns proportional exploration agents, and produces a deduplicated documentation tree with code maps and conventions. ## Quick Start Run /init-deep in your project root to analyze the codebase and generate a hierarchical AGENTS.md knowledge base.