LLM: Documentation driven exploration for big codebase
The doc-torn project offers structured documentation skills for AI coding agents to ensure documentation remains synchronized with code. It features a hierarchical documentation system and tools for documentation-driven exploration and consistency audits. The repository includes installation instructions and usage guidelines for integrating these skills into coding workflows.
- ▪The project provides skills for maintaining structured documentation in sync with code.
- ▪It follows a four-level documentation hierarchy to cater to different reader needs.
- ▪The doc-torn-scan tool allows for iterative documentation audits and updates.
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doc-torn Project that provides structured documentation skills for AI coding agents. This repository contains skills that maintain structured documentation always in sync with the code, following a hierarchy (L0 → L1 → L2 → L3) with an explicit dependency matrix between features. Skills: structured-documentation — core lifecycle: init + update, L0→L3 hierarchy doc-driven-exploration — documentation-driven exploration: read docs before touching code documentation-consistency — full audit of all docs against code with auto-fix Tool: doc-torn-scan — Go binary for iterative feature-by-feature documentation (tree scan, scaffold generation, meta-doc generation) Installation Prerequisites Go 1.23+ — to build doc-torn-scan Git — to clone hooks and track doc changes One-shot Install (all…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.