How does this skill help save tokens?

It implements markdown subsection extraction, which isolates specific parts of a document relevant to your query, reducing the amount of text sent to the LLM context by up to 90%.

Does it support natural language search?

Yes, the skill includes natural language query capabilities that search through content, metadata, and tags to find the most relevant documentation for your intent.

Can I use this skill offline?

Yes, once the documentation has been scraped and indexed locally, you can search, resolve, and read the documentation without an active internet connection.

How do I update the documentation to the latest version?

You can run the scrape-docs script or slash command to fetch the latest updates from Cursor's official llms.txt sources and rebuild the local index.

What is the purpose of doc_id resolution?

Doc_id resolution allows the skill to find documentation even if file paths or names change on the remote server, providing a stable reference for your development workflows.

Cursor Documentation Librarian

Name: Cursor Documentation Librarian
Author: melodic-software

bymelodic-software

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学習とドキュメンテーション

Manages and searches official Cursor documentation to provide a local, token-optimized source of truth for AI development tasks.

概要

The Cursor Documentation Skill acts as a specialized librarian and local repository for the entire Cursor ecosystem. It automates the scraping of documentation from llms.txt sources, provides advanced search capabilities via keywords, tags, and natural language, and maintains a resilient local index. By extracting specific markdown subsections and utilizing hash-based drift detection, it ensures Claude has access to the most up-to-date Cursor features while minimizing token consumption and preventing link rot.

主な機能

Advanced discovery via keyword, category, and natural language search
1 GitHub stars
Token-optimized subsection extraction for reduced context window usage
Automated documentation scraping from official llms.txt sources
Hash-based drift detection and 404 checking for index maintenance
Resilient doc_id resolution for stable internal documentation references

ユースケース

Maintaining an offline-accessible, searchable index of the Cursor IDE documentation
Quickly resolving Cursor configuration settings or feature usage directly within the terminal
Optimizing prompt context by injecting only relevant document fragments instead of full files

概要

主な機能

Advanced discovery via keyword, category, and natural language search
1 GitHub stars
Token-optimized subsection extraction for reduced context window usage
Automated documentation scraping from official llms.txt sources
Hash-based drift detection and 404 checking for index maintenance
Resilient doc_id resolution for stable internal documentation references

ユースケース

Maintaining an offline-accessible, searchable index of the Cursor IDE documentation
Quickly resolving Cursor configuration settings or feature usage directly within the terminal
Optimizing prompt context by injecting only relevant document fragments instead of full files