bmad-document-project

Analyze brownfield codebases and generate structured documentation artifacts.

Updated Mar 16, 2026

Search Tags:#software engineering #code analysis #documentation #AI context #project documentation #brownfield

Ready to run

npx skills add https://github.com/deltatree/wissensmanagement --skill bmad-document-project-deltatree

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bmad-document-project
Source: https://github.com/deltatree/wissensmanagement/tree/main/_bmad/bmm/workflows/bmad-document-project
Command: npx skills add https://github.com/deltatree/wissensmanagement --skill bmad-document-project-deltatree

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the comprehensive documentation of brownfield software projects, transforming complex codebases into easily understandable documentation for AI agents and human developers.

Core Features & Use Cases

Automated Project Analysis: Scans codebases to identify project structure, technology stack, architecture, and dependencies.
Comprehensive Documentation Generation: Creates detailed documents including architecture, source tree analysis, API contracts, and development guides.
AI Context Preparation: Generates documentation specifically formatted for AI consumption, enabling better understanding and interaction with the codebase.
Use Case: When onboarding a new developer or planning a major feature for a legacy system, use this Skill to quickly generate up-to-date documentation that explains the project's architecture, key components, and how to get started.

Quick Start

Use the bmad-document-project skill to document the current project.

Frequently Asked Questions about bmad-document-project

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I document an existing brownfield project for AI context?▼

To document a brownfield project for AI context, you need to perform deep code analysis to generate structured artifacts covering architecture, source tree, APIs, and development workflows. This process automates transforming complex codebases into understandable documentation for AI agents and human developers.

What is the best way to generate architecture and API documentation for a legacy codebase?▼

The best way to generate architecture and API documentation for a legacy codebase is to use an automated code analysis tool that scans the project structure, technology stack, and dependencies. This produces detailed reports on API contracts and architecture specifically formatted for AI consumption.

Can I run a targeted deep-dive analysis into specific areas of my source tree?▼

Yes, you can run a targeted deep-dive analysis into specific areas of your source tree. The documentation process supports initial scans, full rescans, and focused deep-dives to generate comprehensive reports on particular project sections without rescanning the entire codebase.

Does automated project documentation work for onboarding new developers to complex software?▼

Automated project documentation works effectively for onboarding new developers to complex software by generating up-to-date guides that explain project architecture, key components, and getting started workflows. It transforms intricate brownfield codebases into easily understandable development references.

What technology stack and dependency information do I need before scanning a codebase?▼

You do not need to manually provide technology stack or dependency information before scanning a codebase. The automated analysis process identifies the project structure, technology stack, architecture, and dependencies directly from the existing source code during the initial scan.

Why does my AI agent struggle to understand my existing codebase without structured documentation?▼

Your AI agent struggles to understand existing codebases without structured documentation because raw code lacks the architectural context and workflow explanations AI requires. Generating structured documentation artifacts like source tree analysis and API contracts provides the necessary AI context for accurate interaction.