doc-torn is a project designed to enhance the documentation skills of AI coding agents, ensuring that documentation is consistently structured and in sync with the corresponding code. This repository establishes a hierarchical documentation framework (L0 → L1 → L2 → L3) along with a clear dependency matrix among various features.

Key Skills

• Structured Documentation: Manages the core lifecycle from initialization to updates, adhering to the L0-L3 hierarchy.
• Doc-Driven Exploration: Promotes a documentation-first approach, encouraging thorough reading of documents before engaging with the code.
• Documentation Consistency: Conducts comprehensive audits of all documentation against the codebase, with capabilities to auto-fix discrepancies.

Tool Overview

• doc-torn-scan: A Go binary tool that enables iterative documentation processes on a feature-by-feature basis. It is utilized for tree scanning, scaffold generation, and meta-document creation.

Usage

The capabilities of doc-torn are leveraged based on specific tasks:

Documentation Hierarchy (L0 → L3)

The project adopts a structured documentation hierarchy that separates concerns by abstraction level, facilitating a tailored reading experience for different stakeholders:

This layered approach ensures that users can access only the information pertinent to their role, from high-level overviews to in-depth analyses.

Repository Structure

The following structure is maintained:

skills/
  structured-documentation/
  doc-driven-exploration/
  documentation-consistency/
tools/
  doc-torn-scan/
examples/
  AGENTS.md
  hooks/

Overall, doc-torn equips AI coding agents with the essential skills for maintaining high-quality, accurate documentation that evolves with the development lifecycle, streamlining the programming workflow and enhancing collaboration.