bmad-document-project

To generate AI context documentation for an existing codebase, trigger an automated workflow that analyzes repository structure, detects tech stacks, and produces per-part reports with a master index. This ensures structured artifacts suitable for review and iteration.

Brownfield project documentation provides centralized, AI-contextual records for existing codebases. You need it when initiating brownfield PRD workflows that lack consistent documentation, enabling faster product decisions and AI context generation.

Yes, this workflow supports multi-part repositories and federated architectures by detecting distinct parts, inferring their tech stacks, and generating tailored per-part reports alongside a comprehensive master index.

You can create a master index and project overview by running an automated discovery process that analyzes repository structure and source-tree, automatically generating comprehensive architecture documentation and per-part reports.

Yes, the documentation workflow ensures resumable state, allowing you to pause and resume the analysis of large repositories. It maintains clear outputs and structured artifacts throughout the process.

The best way to perform deep-dive documentation is using an automated workflow that generates optional deep-dive reports for targeted areas, ensuring structured artifacts that support resumable state and iterative review.