fetching-library-docs - SKILL.md Agent Skill

name: fetching-library-docs

description: |

Token-efficient library API documentation fetcher using Context7 MCP with 77% token savings.

Fetches code examples, API references, and usage patterns for published libraries (React,

Next.js, Prisma, etc). Use when users ask "how do I use X library", need code examples,

want API syntax, or are learning a framework's official API. Triggers: "Show me React hooks",

"Prisma query syntax", "Next.js routing API". NOT for exploring repo internals/source code

(use researching-with-deepwiki) or local files.

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:

fetch-docs.sh calls fetch-raw.sh (which uses mcp-client.py)
Full response (934 tokens) stays in subprocess memory
Shell filters (awk/grep/sed) extract essentials (0 LLM tokens used)
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

Verification

Run: python3 scripts/verify.py

Expected: ✓ fetch-docs.sh ready

If Verification Fails

Run diagnostic: ls -la scripts/fetch-docs.sh
Check: Script exists and is executable
Fix: chmod +x scripts/fetch-docs.sh
Stop and report if still failing - do not proceed with downstream steps

Troubleshooting

| Issue | Solution |

|-------|----------|

| Library not found | Try name variations or use broader search term |

| No results | Use --mode info or broader topic |

| Need more examples | Increase page: --page 2 |

| Want full context | Use --mode info for explanations |

| Permission denied | Run: chmod +x scripts/*.sh |

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.