code-documentation

You can automatically generate documentation by analyzing source code signatures and tests to produce inline docstrings, API references, and project READMEs. This process scans modules and functions to insert or update human-readable docs and validate parameter names against code.

Supported documentation formats include Google-style, NumPy, and Sphinx Python docstrings, JSDoc and TypeDoc for JavaScript/TypeScript, Javadoc for Java, Godoc for Go, Rust doc comments, and YARD for Ruby, alongside standard Markdown READMEs and API references.

Yes, you can scan an entire monorepo to inventory public modules, classes, and functions. The analysis identifies stale or missing documentation across single files and libraries, then inserts or updates inline docs and standalone markdown to ensure accuracy.

Automated code analysis ensures accuracy by validating parameter names and types against function signatures and cross-referencing types. It infers behavior from tests, validates usage examples, and checks that all references match the actual source code.

The best way to add Google-style docstrings is to use an automated inventory tool that scans the src/ directory for public functions, analyzes their signatures, and inserts the appropriate Python docstrings while generating a project README for onboarding and IDE assistance.