What is the purpose of the init-deep skill?

It maps your project's structure and generates hierarchical documentation files to guide AI agents through your specific codebase conventions and rules.

Can I regenerate the documentation from scratch?

Yes, using the --create-new flag will read existing context, remove old files, and regenerate the entire hierarchy from a fresh analysis.

Does this work with large repositories?

Absolutely; the skill dynamically spawns additional exploration agents based on file count and project depth to ensure thorough coverage regardless of scale.

How does it decide where to place AGENTS.md files?

It uses a scoring matrix based on file count, subdirectory depth, code ratio, and symbol density to identify critical domain boundaries that require documentation.

Hierarchical Agent Documentation

Name: Hierarchical Agent Documentation
Author: UtsavBalar1231

byUtsavBalar1231

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Aprendizaje y Documentación

Generates hierarchical, complexity-scored AGENTS.md files to provide deep context and domain-specific guidance for AI agents.

The init-deep skill is a high-performance orchestration tool designed to map out complex codebases by generating specialized documentation for AI agents. By utilizing a multi-agent discovery process and bash-based structural analysis, it creates a network of AGENTS.md files at the root and within key subdirectories based on a weighted complexity score. This ensures that Claude and other agents have immediate access to technical conventions, entry points, and project-specific anti-patterns, significantly improving the accuracy and reliability of AI-driven development in large-scale or non-standard repositories.

Características Principales

01Automated complexity scoring for optimal documentation placement

02Parallel generation of root and subdirectory knowledge bases

03Multi-agent concurrent project discovery and pattern analysis

04Detection of technical conventions and explicit anti-patterns

051 GitHub stars

06Dynamic scaling of exploration agents based on repository size

Casos de Uso

01Standardizing project guardrails by documenting forbidden patterns across a monorepo

02Onboarding AI agents to massive legacy codebases with unique structural patterns

03Enhancing AI context retrieval by creating a domain-specific hierarchy of knowledge files

Características Principales

01Automated complexity scoring for optimal documentation placement

02Parallel generation of root and subdirectory knowledge bases

03Multi-agent concurrent project discovery and pattern analysis

04Detection of technical conventions and explicit anti-patterns

051 GitHub stars

06Dynamic scaling of exploration agents based on repository size

Casos de Uso

01Standardizing project guardrails by documenting forbidden patterns across a monorepo

02Onboarding AI agents to massive legacy codebases with unique structural patterns

03Enhancing AI context retrieval by creating a domain-specific hierarchy of knowledge files