bmad-document-project

Generate AI context for brownfield projects by running an automated document-project workflow that discovers project structure, infers tech stacks, and outputs architecture, data models, and API contracts. It provides a navigable master index for rapid AI integration.

Automated project documentation supports monoliths, monorepos, and multi-part architectures. It automatically detects project boundaries, enumerates parts, and consolidates existing docs into consistent, AI-ready outputs across the entire structure.

Infer tech stacks and enumerate project parts through automated project detection during the documentation workflow. The scan classifies components across the repository and maps integration points into a structured master index.

Start automating project documentation by providing a target repository path and running the document-project workflow. The process handles discovery, classification, and generation of data models and API contracts without manual configuration.

Project analysis supports quick, deep, or exhaustive scan depths depending on your needs. You choose the scan level to control how thoroughly the workflow maps architecture, data models, and API contracts for the AI context output.

Consolidating existing docs ensures consistent, navigable AI-ready outputs by merging scattered information. AI-assisted consolidation captures structure, tech stacks, and integration points into a unified master index for accurate project context.