deepinit

To generate hierarchical AGENTS.md documentation, the Skill scans your repository structure and creates directory-level guidance files with parent-child navigation links. It analyzes each directory's role and outputs organized, AI-readable operating instructions for nested modules.

Yes, you can update directory-level documentation while preserving manual notes. The regeneration workflow detects structural changes, validates parent references, and safeguards manually authored sections so your custom guidance persists across documentation refreshes.

Hierarchical AGENTS.md documentation provides AI agents with structured, directory-level operating instructions during repository analysis. It maps nested folders, establishes parent-child navigation, and ensures agents understand each module's role and conventions within the evolving project.

You keep AI agent operating instructions synchronized by running the documentation refresh workflow, which performs change detection on your repository structure and updates the AGENTS.md files. This ensures directory mapping and parent references accurately reflect new or modified folders.

Yes, this approach works for nested module mapping in large repositories by applying directory analysis and parent reference validation across the entire codebase. It scales hierarchical AGENTS.md generation to trace complex, deeply nested folder structures without losing organizational context.

During AGENTS.md regeneration, the validation workflow checks for broken references and missing files in your repository structure. It identifies these issues so you can resolve directory mapping errors and ensure parent-child navigation links remain accurate and traceable.