bmad-document-project

Name: bmad-document-project
Availability: InStock
Author: GenEd

Document existing software projects into structured markdown for AI context.

Updated Mar 30, 2026

Search Tags:#workflow #architecture #documentation #brownfield #ai-context #project-analysis #source-tree

Ready to run

npx skills add https://github.com/GenEducation/GenedUIProject --skill bmad-document-project-geneducation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bmad-document-project
Source: https://github.com/GenEducation/GenedUIProject/tree/main/.gemini/skills/bmad-document-project
Command: npx skills add https://github.com/GenEducation/GenedUIProject --skill bmad-document-project-geneducation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill turns an existing software repository into structured documentation that helps AI agents understand the codebase, its architecture, and how to work safely within it.

Core Features & Use Cases

Project Classification: Detects whether a repository is a monolith, monorepo, or multi-part system and identifies the main technology stack.
Documentation Generation: Produces project overviews, source tree analysis, architecture docs, development guides, and supporting references.
Resumable Analysis: Uses workflow state tracking so a long project scan can be paused, resumed, or rerun with consistent results.
Use Case: A team inherits a brownfield Next.js app and needs AI-ready documentation before adding new features or planning refactors.

Quick Start

Ask the skill to document the current project so it can generate the repository index, architecture summary, source tree, and development guidance.

Frequently Asked Questions about bmad-document-project

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

FAQPage Schema

How do I generate architecture documentation for a brownfield codebase?▼

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.

What is AI-ready project documentation and why do I need it for an existing repository?▼

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.

Can I pause and resume a deep source tree analysis on a large monorepo?▼

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.

Does project documentation generation work for both monolith and monorepo architectures?▼

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.

What is the best way to create development and deployment guides from an existing codebase?▼

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.

What are the limitations of automated project documentation for AI context?▼

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.