gds-document-project

Analyze existing codebases and generate structured project documentation for AI context.

6|Updated Aug 29, 2026
One-click install
npx skills add https://github.com/HordRicJr/Akomagni --skill gds-document-project-hordricjr
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: gds-document-project
Source: https://github.com/HordRicJr/Akomagni/tree/main/bmad-core/.agents/skills/gds-document-project
Command: npx skills add https://github.com/HordRicJr/Akomagni --skill gds-document-project-hordricjr

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Brownfield projects often lack up-to-date documentation, making it hard for developers and AI agents to understand the existing system before planning new features. This Skill scans an existing codebase and produces a complete, navigable documentation set. ## Core Features & Use Cases - Project Scanning and Classification: Detects project type (web, mobile, backend, CLI, library, and more), technology stack, and monolith vs. multi-part structure using a documentation-requirements matrix. - Three Scan Levels: Quick (pattern-based, no source reading), Deep (reads critical directories), and Exhaustive (reads all source files), with batching and a resumable state file (project-scan-report.json). - Deep-Dive Mode: Performs exhaustive file-by-file analysis of a chosen folder, feature, or API group, producing dependency graphs, data flow analysis, and implementation guidance. - Use Case: Point the workflow at a legacy repository to generate index.md, architecture docs, API contracts, data models, and development guides that are immediately usable for a brownfield PRD workflow. ## Quick Start Ask the AI to document the project by saying "document project" or "generate docs" for the current repository.

Frequently Asked Questions about gds-document-project

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

FAQPage Schema
How do I generate documentation for an existing codebase?

Run the document-project workflow and choose a scan level: quick for pattern-based analysis, deep for reading critical directories, or exhaustive for reading all source files. It writes index.md, architecture, API contracts, and development guides to the output folder.

What is the difference between quick, deep, and exhaustive scans?

Quick scan uses only patterns, configs, and manifests without reading source files. Deep scan reads files in critical directories based on the detected project type. Exhaustive scan reads every source file, excluding node_modules, dist, and build folders.

Can I resume an interrupted project documentation scan?

Yes. The workflow maintains a project-scan-report.json state file updated after each step. If a state file less than 24 hours old exists, you are prompted to resume from the last completed step; older state files are archived automatically.

Does it support monorepos or multi-part projects?

Yes. It detects client/server and similar multi-part structures, documents each part separately with part-suffixed files, and generates an integration-architecture.md plus a machine-readable project-parts.json describing how the parts communicate.

What is deep-dive mode in project documentation?

Deep-dive mode exhaustively reads every file in a chosen folder, feature, or API group. It produces a complete file inventory with exports and signatures, a dependency graph, data flow analysis, testing coverage, and modification guidance.

When should I not use the document-project workflow?

It is designed for brownfield projects with existing code. For greenfield projects with only planning artifacts, it warns you and asks before continuing, since there is little source code to analyze.