llmdoc is a structured documentation system designed to help AI models and developers understand codebases through markdown files located in an llmdoc/ directory.

How does this help with Claude Code performance?

By providing a structured summary of the project architecture first, it gives Claude the necessary context to make better coding decisions without wasting context window space on irrelevant source files.

What happens if the project doesn't have an llmdoc directory?

The skill will detect the absence of the directory and suggest running an initialization command like /tr:initDoc to set up the documentation structure.

Does this skill read my actual source code?

No, this specific skill focuses on reading the documentation files within the llmdoc directory to provide a high-level summary, which is faster and more context-efficient than reading raw source code.

LLMDoc Explorer

Name: LLMDoc Explorer
Author: TokenRollAI

byTokenRollAI

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367

콘텐츠 관리

Accelerates project onboarding by extracting architectural insights and key concepts from llmdoc documentation without scanning source code.

소개

The read-doc skill integrates with the llmdoc documentation system to provide an immediate, high-level understanding of a project's structure and purpose. By systematically scanning documentation indices, overview files, and architectural guides, it allows Claude to grasp complex system designs and workflows without the overhead of reading individual source files. This tool is essential for developers joining new projects or switching contexts, ensuring they have the most relevant context delivered in a structured summary and reducing the time spent manually navigating file hierarchies.

주요 기능

Deep scanning of project-specific guides and workflows
Comprehensive summary generation for rapid onboarding
367 GitHub stars
Automated llmdoc system discovery and validation
Documentation-first context building to reduce token overhead
Structured indexing of project architecture and design patterns

사용 사례

Quickly onboarding onto a large, unfamiliar codebase
Reviewing system architecture before implementing new features
Identifying existing workflows and developer guides within a repository

소개

주요 기능

Deep scanning of project-specific guides and workflows
Comprehensive summary generation for rapid onboarding
367 GitHub stars
Automated llmdoc system discovery and validation
Documentation-first context building to reduce token overhead
Structured indexing of project architecture and design patterns

사용 사례

Quickly onboarding onto a large, unfamiliar codebase
Reviewing system architecture before implementing new features
Identifying existing workflows and developer guides within a repository