bmad-document-project

To generate architecture documentation for a brownfield codebase, use this Skill to scan the repository and produce structured markdown artifacts including project overviews, architecture summaries, source tree analysis, and development guides.

AI-ready project documentation is structured markdown analyzing a brownfield repository's architecture, source tree, APIs, and deployment logic so AI agents can understand and work safely within the codebase.

Yes, you can pause and resume a deep source tree analysis on a large monorepo because the workflow uses resumable state tracking, ensuring consistent results when scanning is paused, resumed, or rerun.

Yes, project documentation generation works for both monolith and monorepo architectures by automatically detecting the repository type and identifying the main technology stack during the initial project classification phase.

The best way to create development and deployment guides from an existing codebase is to run an exhaustive scan mode that validates the repository and generates markdown artifacts covering API analysis, deployment configurations, and development guidance.

Automated project documentation for AI context requires resumable state tracking and validation to maintain accuracy, meaning extremely large or highly dynamic repositories may require multiple reruns to generate fully consistent markdown artifacts.