Extracts structured specifications and implementation guides from source code to generate comprehensive CLAUDE.md and IMPLEMENTS.md files.
This skill automates the reverse-engineering of codebases into a standardized documentation format, producing high-level specifications (CLAUDE.md) and detailed implementation plans (IMPLEMENTS.md). By analyzing source code directory-by-directory in a leaf-first order, it captures the purpose, exports, and architectural logic of a project without overwhelming the AI context. It is essential for developers inheriting legacy code, performing system audits, or preparing a codebase for AI-assisted development by establishing a clear source of truth for the project's 'what' and 'how'.
Key Features
010 GitHub stars
02Context-efficient processing: Uses optimized jq queries and compressed agent responses to minimize token usage on large projects.
03Leaf-first traversal: Processes subdirectories first to ensure parent documentation accurately reflects child components.
04Automated reverse engineering: Extracts exports, behaviors, domain context, and state management patterns directly from source.
05Integrated schema validation: Includes built-in CLI checks to ensure generated documentation follows standardized structural requirements.
06Dual-document generation: Produces WHAT (CLAUDE.md) and HOW (IMPLEMENTS.md) specifications for every directory.
Use Cases
01Onboarding AI agents to a new repository by creating a structured map of the codebase.
02Documenting legacy codebases that lack clear architectural specifications or API references.
03Reverse-engineering complex binary logic into human-readable implementation guides and domain context.