Why use this instead of a standard MCP client?

Standard clients return full, unfiltered documentation that consumes massive amounts of context; this skill reduces that footprint by over 77% per request.

Which libraries are supported?

It supports a wide range of popular frameworks including React, Next.js, Prisma, Express, NestJS, Vue, Fastify, and many others available via Context7.

Can I use this for conceptual learning?

Yes, the skill features an '--mode info' flag specifically designed to return conceptual explanations and architectural patterns rather than just code.

How does this skill save tokens?

It uses a shell pipeline involving awk, grep, and sed to filter raw documentation within a subprocess, ensuring only essential code and API data are sent to the LLM.

Context7 Efficient Documentation Fetcher

Name: Context7 Efficient Documentation Fetcher
Author: alijilani-dev

byalijilani-dev

Learning & Documentation

Fetches library documentation and code examples with 77% token efficiency using intelligent shell-based filtering.

About

This skill optimizes how Claude retrieves technical documentation from the Context7 MCP by utilizing a specialized shell pipeline to filter raw API responses. By extracting only essential code examples, API signatures, and key notes while discarding unnecessary metadata in a subprocess, it achieves a massive reduction in token usage. This allows developers to maintain a larger project context while still accessing comprehensive documentation for frameworks like React, Next.js, Prisma, and Express.

Key Features

Intelligent token reduction with up to 86.8% savings
Switchable modes for conceptual info or code-heavy examples
0 GitHub stars
Filtered extraction of code examples and API signatures
Direct library ID lookup for low-latency resolution
Multi-language support for JavaScript, Python, Go, and Rust

Use Cases

Quickly fetching syntax for React hooks or Next.js routing without bloating conversation context.
Learning new frameworks by retrieving curated 'Getting Started' guides and best practices.
Troubleshooting library-specific issues using targeted documentation lookups for API signatures.

About

Key Features

Intelligent token reduction with up to 86.8% savings
Switchable modes for conceptual info or code-heavy examples
0 GitHub stars
Filtered extraction of code examples and API signatures
Direct library ID lookup for low-latency resolution
Multi-language support for JavaScript, Python, Go, and Rust

Use Cases

Quickly fetching syntax for React hooks or Next.js routing without bloating conversation context.
Learning new frameworks by retrieving curated 'Getting Started' guides and best practices.
Troubleshooting library-specific issues using targeted documentation lookups for API signatures.