This project provides an "evidence atlas" for AI-generated code, addressing the challenge of rapidly evolving agent-authored repositories where documentation and code understanding drift apart. It aims to keep guides, progress logs, and context synchronized with the source code, benefiting developers and researchers working with AI coding agents by preserving the reasoning layer within the repository itself.

How It Works

Repo-Docs operates via a "Repo-Docs Loop," a conservative process that syncs understanding with code changes. When a user asks a question or an agent modifies the repository, the system checks for understanding updates. This can lead to updates in the README, walkthroughs of real runs, change logs, module explanations, reference pages for commands and schemas, and agent-specific documentation (e.g., AGENTS.md). The core principle is "Behavior before inventory," prioritizing walkthroughs of real workflows over mere file listings, and ensuring that documentation lives alongside the source code it describes.

Quick Start & Requirements

Installation can be initiated via a natural-language request to a coding agent (e.g., "Install the repo-docs skill from this project: https://github.com/YurunChen/repo-docs-skills"). Alternatively, command-line installation is supported via curl -fsSL https://github.com/YurunChen/repo-docs-skills/raw/main/install.sh | bash (Linux/macOS) or irm https://github.com/YurunChen/repo-docs-skills/raw/main/install.ps1 | iex (Windows PowerShell). The script can install the skill into default agent directories or a specified target. Usage involves invoking the repo-docs skill within a repository. No specific hardware or software prerequisites beyond a compatible shell environment and a coding agent setup are explicitly mentioned, though its utility is tied to AI-driven development workflows.

Highlighted Details

• Generates structured documentation including walkthroughs, concept pages, reference materials, glossaries, and change logs.
• Implements a "Repo-Docs Loop" for continuous synchronization between code and documentation.
• Employs a "Quality Bar" emphasizing behavior over inventory, reader handling over locators, and keeping evidence visible.
• Offers different operational modes (Seed, Build, Sync, Cleanup, Question refinement) tailored to repository maturity and user needs.

Maintenance & Community

The project is developed by the AI4GC Lab at Zhejiang University. No specific community channels (like Discord or Slack) or direct links to roadmaps or active contributor lists beyond the lab affiliation are provided in the README.

Licensing & Compatibility

The README does not explicitly state the project's license. This omission presents a significant adoption blocker, as the terms for use, modification, and distribution are unclear, potentially restricting commercial use or integration into closed-source projects.

Limitations & Caveats

The project's primary focus is on AI-generated code, and its effectiveness may vary for repositories with different development paradigms. While the "conservative" update loop aims for precision, it might not capture every minor code change instantly. The lack of a stated license is a critical caveat for any potential adopter.