One-click install
npx skills add https://github.com/roerohan/skills --skill index-knowledge-roerohan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: index-knowledge
Source: https://github.com/roerohan/skills/tree/main/index-knowledge
Command: npx skills add https://github.com/roerohan/skills --skill index-knowledge-roerohan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Codebases often lack structured, hierarchical knowledge documentation, forcing AI agents and developers to sift through irrelevant context or miss critical project-specific conventions, anti-patterns, and architectural details when working on specific modules.

Core Features & Use Cases

  • Hierarchical AGENTS.md Generation: Creates root-level and subdirectory-level AGENTS.md files tailored to each part of the codebase, avoiding redundant generic content.
  • Complexity-Scoped Documentation: Uses a scoring matrix to only document high-complexity or distinct-domain directories, so low-complexity areas are covered by parent docs without bloat.
  • Use Case: For a large monorepo with 10+ modules, this skill automatically generates a root knowledge base plus per-module docs, letting agents instantly access relevant context for any task without loading the entire codebase context every time.

Quick Start

Use the index-knowledge skill to generate a complete hierarchical AGENTS.md knowledge base for your current codebase.

Frequently Asked Questions about index-knowledge

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate an AGENTS.md knowledge base for my codebase?▼

To generate an AGENTS.md knowledge base, use a skill that produces hierarchical, scoped documentation for AI agents and developers. It creates root-level and subdirectory-level files covering project structure, conventions, anti-patterns, and code maps without redundant generic content.

What is the best way to document a monorepo with multiple modules for AI agents?▼

Documenting a monorepo for AI agents requires hierarchical knowledge bases that provide scoped context for specific modules. This approach generates a root knowledge base plus per-module docs, letting agents instantly access relevant context without loading the entire codebase context every time.

How does complexity-scoped codebase documentation avoid content bloat?▼

Complexity-scoped codebase documentation uses a scoring matrix to only document high-complexity or distinct-domain directories. Low-complexity areas are covered by parent docs, ensuring the generated AGENTS.md files contain no generic or irrelevant content and preventing documentation bloat.

Can I auto-generate codebase onboarding documentation for nested directories?▼

Yes, you can auto-generate codebase onboarding documentation for nested directories. The process analyzes the repository structure and produces non-redundant AGENTS.md files at the root and high-complexity subdirectories, capturing project-specific conventions and architectural details.

Why do AI agents need scoped context from hierarchical codebase documentation?▼

AI agents need scoped context because codebases often lack structured knowledge documentation, forcing agents to sift through irrelevant context or miss critical project-specific conventions. Hierarchical AGENTS.md files provide structured, scoped context for any task within specific modules.