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Context7 Efficient

  • 15 installs
  • 1 repo stars
  • Updated January 27, 2026
  • bilalmk/todo_correct

context7-efficient is a Claude Code skill that fetches library documentation via the Context7 MCP server and filters it through a shell pipeline to cut token usage.

About

This skill fetches library and framework documentation through the Context7 MCP server using a shell pipeline. The pipeline keeps the full documentation response inside a subprocess and returns only filtered code examples, API signatures, and key notes to the model. A developer uses it when they need syntax reference or code examples for libraries like React, Next.js, Prisma, or Express while writing code. It reduces token usage by filtering documentation before it enters the model context.

  • Fetches library docs via Context7 MCP through a shell pipeline that filters output before it reaches the model
  • Claims ~77% token reduction (205 vs 934 tokens per query)
  • Ships fetch-docs.sh orchestrator plus code-block, signature, and notes extractors

Context7 Efficient by the numbers

  • 15 all-time installs (skills.sh)
  • Ranked #1,080 of 1,879 Documentation skills by installs in the Skillselion catalog
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
At a glance

context7-efficient capabilities & compatibility

Capabilities
documentation · token optimization
Works with
openai
Use cases
documentation · token optimization
Runs
Runs locally
Pricing
Free
From the docs

What context7-efficient says it does

Token-efficient library documentation fetcher using Context7 MCP with 86.8% token savings through intelligent shell pipeline filtering.
SKILL.md
Fetch library documentation with automatic 77% token reduction via shell pipeline.
SKILL.md
npx skills add https://github.com/bilalmk/todo_correct --skill context7-efficient

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Listed on Skillselion
Installs15
repo stars1
Last updatedJanuary 27, 2026
Repositorybilalmk/todo_correct

What it does

Fetch filtered library documentation and code examples for a framework while writing code, without loading full docs into context.

Who is it for?

Looking up code examples or API syntax for a specific library while coding.

When should I use this skill?

The user asks about library documentation, needs code examples, or wants API usage patterns for a framework.

What you get

Returns only code examples, API signatures, and key notes for a requested library and topic.

By the numbers

  • Claims 77% token reduction (205 vs 934 tokens per query)
  • Lists 9 common library IDs

Files

SKILL.mdMarkdownGitHub ↗

Context7 Efficient Documentation Fetcher

Fetch library documentation with automatic 77% token reduction via shell pipeline.

Quick Start

Always use the token-efficient shell pipeline:

# Automatic library resolution + filtering
bash scripts/fetch-docs.sh --library <library-name> --topic <topic>

# Examples:
bash scripts/fetch-docs.sh --library react --topic useState
bash scripts/fetch-docs.sh --library nextjs --topic routing
bash scripts/fetch-docs.sh --library prisma --topic queries

Result: Returns ~205 tokens instead of ~934 tokens (77% savings).

Standard Workflow

For any documentation request, follow this workflow:

1. Identify Library and Topic

Extract from user query:

  • Library: React, Next.js, Prisma, Express, etc.
  • Topic: Specific feature (hooks, routing, queries, etc.)

2. Fetch with Shell Pipeline

bash scripts/fetch-docs.sh --library <library> --topic <topic> --verbose

The --verbose flag shows token savings statistics.

3. Use Filtered Output

The script automatically:

  • Fetches full documentation (934 tokens, stays in subprocess)
  • Filters to code examples + API signatures + key notes
  • Returns only essential content (205 tokens to Claude)

Parameters

Basic Usage

bash scripts/fetch-docs.sh [OPTIONS]

Required (pick one):

  • --library <name> - Library name (e.g., "react", "nextjs")
  • --library-id <id> - Direct Context7 ID (faster, skips resolution)

Optional:

  • --topic <topic> - Specific feature to focus on
  • --mode <code|info> - code for examples (default), info for concepts
  • --page <1-10> - Pagination for more results
  • --verbose - Show token savings statistics

Mode Selection

Code Mode (default): Returns code examples + API signatures

--mode code

Info Mode: Returns conceptual explanations + fewer examples

--mode info

Common Library IDs

Use --library-id for faster lookup (skips resolution):

React:      /reactjs/react.dev
Next.js:    /vercel/next.js
Express:    /expressjs/express
Prisma:     /prisma/docs
MongoDB:    /mongodb/docs
Fastify:    /fastify/fastify
NestJS:     /nestjs/docs
Vue.js:     /vuejs/docs
Svelte:     /sveltejs/site

Workflow Patterns

Pattern 1: Quick Code Examples

User asks: "Show me React useState examples"

bash scripts/fetch-docs.sh --library react --topic useState --verbose

Returns: 5 code examples + API signatures + notes (~205 tokens)

Pattern 2: Learning New Library

User asks: "How do I get started with Prisma?"

# Step 1: Get overview
bash scripts/fetch-docs.sh --library prisma --topic "getting started" --mode info

# Step 2: Get code examples
bash scripts/fetch-docs.sh --library prisma --topic queries --mode code

Pattern 3: Specific Feature Lookup

User asks: "How does Next.js routing work?"

bash scripts/fetch-docs.sh --library-id /vercel/next.js --topic routing

Using --library-id is faster when you know the exact ID.

Pattern 4: Deep Exploration

User needs comprehensive information:

# Page 1: Basic examples
bash scripts/fetch-docs.sh --library react --topic hooks --page 1

# Page 2: Advanced patterns
bash scripts/fetch-docs.sh --library react --topic hooks --page 2

Token Efficiency

How it works:

1. fetch-docs.sh calls fetch-raw.sh (which uses mcp-client.py) 2. Full response (934 tokens) stays in subprocess memory 3. Shell filters (awk/grep/sed) extract essentials (0 LLM tokens used) 4. Returns filtered output (205 tokens) to Claude

Savings:

  • Direct MCP: 934 tokens per query
  • This approach: 205 tokens per query
  • 77% reduction

Do NOT use `mcp-client.py` directly - it bypasses filtering and wastes tokens.

Advanced: Library Resolution

If library name fails, try variations:

# Try different formats
--library "next.js"    # with dot
--library "nextjs"     # without dot
--library "next"       # short form

# Or search manually
bash scripts/fetch-docs.sh --library "your-library" --verbose
# Check output for suggested library IDs

Troubleshooting

IssueSolution
Library not foundTry name variations or use broader search term
No resultsUse --mode info or broader topic
Need more examplesIncrease page: --page 2
Want full contextUse --mode info for explanations

References

For detailed Context7 MCP tool documentation, see:

  • references/context7-tools.md - Complete tool reference

Implementation Notes

Components (for reference only, use fetch-docs.sh):

  • mcp-client.py - Universal MCP client (foundation)
  • fetch-raw.sh - MCP wrapper
  • extract-code-blocks.sh - Code example filter (awk)
  • extract-signatures.sh - API signature filter (awk)
  • extract-notes.sh - Important notes filter (grep)
  • fetch-docs.sh - Main orchestrator (ALWAYS USE THIS)

Architecture: Shell pipeline processes documentation in subprocess, keeping full response out of Claude's context. Only filtered essentials enter the LLM context, achieving 77% token savings with 100% functionality preserved.

Based on Anthropic's "Code Execution with MCP" blog post.

Related skills

Documentationdocsintegrations

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