rcode-document-project

To generate documentation for an existing codebase for LLM context, you scan the repository to classify its structure, capturing the stack and architecture to produce structured, AI-ready docs like project overviews and development guidance.

The best way to document a brownfield repository is scanning it to capture source-tree mapping, architecture summaries, and documentation gaps, which creates a full documentation set for both human onboarding and LLM processing.

Yes, you can control the scan depth when analyzing a large codebase. The process supports scan-depth selection and resumable state tracking, allowing you to manage multi-part repositories and perform deep-dive analysis for specific modules.

Yes, repository scanning works for generating API and data-model documentation. Alongside architecture summaries and source-tree mapping, it produces optional API or data-model documentation artifacts as part of the overall documentation set.

To refresh outdated architecture documentation after a codebase change, you rescan the repository to validate and regenerate the structured documentation artifacts, ensuring the project overview and development guidance accurately reflect the updated state.

Codebase analysis requires resumable state tracking for multi-part repositories to handle large or complex structures without losing progress, allowing the scan to pause and resume while accurately aggregating architecture summaries and source-tree mappings.