Can I limit how many subdirectories are documented?

Yes, you can use the --max-depth flag to limit the directory search depth (default is 3), preventing excessive documentation in deeply nested projects.

What is an AGENTS.md file?

AGENTS.md files are localized documentation files that provide specific context, rules, and navigation tips for AI agents working within a particular directory or module.

How does the complexity scoring work?

The skill evaluates factors like file count, code-to-comment ratios, symbol density, and reference centrality to decide if a subdirectory warrants its own AGENTS.md file.

Does init-deep overwrite my existing documentation?

By default, it operates in update mode, modifying existing files while preserving context. You can use the --create-new flag to regenerate everything from scratch.

Hierarchical Codebase Context Generator

Name: Hierarchical Codebase Context Generator
Author: UtsavBalar1231

byUtsavBalar1231

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학습 및 문서화

Automatically generates structured documentation and hierarchical context files to help AI agents navigate and understand complex codebases.

The init-deep skill automates the creation of a multi-layered knowledge base for your project by generating complexity-aware AGENTS.md files. By deploying a fleet of background explore agents to analyze directory structures, coding patterns, and architectural entry points, it builds a hierarchical map of your code. It uses a sophisticated scoring matrix to determine which subdirectories require localized guidance, ensuring Claude has immediate access to specific conventions, anti-patterns, and module-level details. This significantly reduces AI hallucinations and improves development velocity in large, multi-language, or unfamiliar repositories.

주요 기능

01Concurrent codebase analysis using specialized background explore agents

02Automated discovery of project-specific conventions and forbidden anti-patterns

03Integration with LSP tools for symbol density and reference mapping

041 GitHub stars

05Complexity-scored directory selection for hierarchical documentation placement

06Parallel generation of root and subdirectory-level knowledge bases

사용 사례

01Optimizing AI agent performance by providing localized context for specific modules

02Standardizing project-specific coding rules and architecture patterns across a team

03Onboarding to large, unfamiliar repositories with deeply nested structures

주요 기능

01Concurrent codebase analysis using specialized background explore agents

02Automated discovery of project-specific conventions and forbidden anti-patterns

03Integration with LSP tools for symbol density and reference mapping

041 GitHub stars

05Complexity-scored directory selection for hierarchical documentation placement

06Parallel generation of root and subdirectory-level knowledge bases

사용 사례

01Optimizing AI agent performance by providing localized context for specific modules

02Standardizing project-specific coding rules and architecture patterns across a team

03Onboarding to large, unfamiliar repositories with deeply nested structures