Context7 Auto Research Skill

This skill automatically fetches current documentation from Context7 API when detecting library/framework-related queries, ensuring responses use up-to-date information instead of potentially outdated training data.

Automatic Activation Triggers

This skill should activate proactively when the user's message contains:

Implementation Queries (实现相关)

•"如何实现" / "怎么写" / "怎么做"
•"How do I..." / "How to..." / "How can I..."
•"Show me how to..." / "Write code for..."

Configuration & Setup (配置相关)

•"配置" / "设置" / "安装"
•"configure" / "setup" / "install"
•"初始化" / "initialize"

Documentation Requests (文档相关)

•"文档" / "参考" / "API"
•"documentation" / "docs" / "reference"
•"查看" / "look up"

Library/Framework Mentions (库/框架提及)

•React, Vue, Angular, Svelte, Solid
•Next.js, Nuxt, Remix, Astro
•Express, Fastify, Koa, Hono
•Prisma, Drizzle, TypeORM
•Supabase, Firebase, Clerk
•Tailwind, shadcn/ui, Radix
•Any npm package or GitHub repository

Code Generation Requests (代码生成)

•"生成代码" / "写一个" / "创建"
•"generate" / "create" / "build"
•"implement" / "add feature"

Research Process

When triggered, follow this workflow:

Step 1: Extract Library Information

Identify the library/framework from the user's query:

•Library name (e.g., "react", "next.js", "prisma")
•Version if specified (e.g., "React 19", "Next.js 15")
•Specific feature/API mentioned (e.g., "useEffect", "middleware", "relations")

Step 2: Search for Library

Use Task tool to call context7-fetcher sub-skill:

code

Task parameters:
- subagent_type: Bash
- description: "Search Context7 for library"
- prompt: node .claude/skills/context7-auto-research/context7-api.js search "<library-name>" "<user-query>"

Example:

code

Task: Search for Next.js
Prompt: node .claude/skills/context7-auto-research/context7-api.js search "next.js" "How to configure middleware in Next.js 15"

Response format:

json

{
  "libraries": [
    {
      "id": "/vercel/next.js",
      "name": "Next.js",
      "description": "The React Framework",
      "trustScore": 95,
      "versions": ["v15.1.8", "v14.2.0", "v13.5.0"]
    }
  ]
}

Why use Task tool?

•Uses context: fork from context7-fetcher sub-skill
•Avoids carrying conversation history to API calls
•Reduces Token consumption

Step 3: Select Best Match

From search results, choose the library based on:

•Exact name match to user's query
•Highest trust score (indicates quality/popularity)
•Version match if user specified (e.g., "Next.js 15" → prefer v15.x)
•Official packages over community forks

Step 4: Fetch Documentation

Use Task tool to call context7-fetcher sub-skill:

code

Task parameters:
- subagent_type: Bash
- description: "Fetch documentation from Context7"
- prompt: node .claude/skills/context7-auto-research/context7-api.js context "<library-id>" "<specific-query>"

Example:

code

Task: Fetch Next.js middleware docs
Prompt: node .claude/skills/context7-auto-research/context7-api.js context "/vercel/next.js" "middleware configuration"

Response format:

json

{
  "results": [
    {
      "title": "Middleware",
      "content": "Middleware allows you to run code before a request is completed...",
      "source": "docs/app/building-your-application/routing/middleware.md",
      "relevance": 0.95
    }
  ]
}

Why use Task tool?

•Independent context for API calls
•No conversation history overhead
•Faster execution

Step 5: Integrate into Response

Use the fetched documentation to:

•Answer accurately with current information
•Include code examples from the docs
•Cite version when relevant
•Provide context about the feature/API

Helper Script Usage

The context7-api.js script provides two commands:

Search Library

bash

node context7-api.js search <libraryName> <query>

•Returns matching libraries with metadata
•Use for initial library resolution

Get Context

bash

node context7-api.js context <libraryId> <query>

•Returns relevant documentation snippets
•Use after selecting a library

Environment Setup

The script supports two ways to configure the API key:

Option 1: .env File (Recommended)

Create a .env file in the skill directory:

bash

# In .claude/skills/context7-auto-research/.env
CONTEXT7_API_KEY=your_api_key_here

You can copy from the example:

bash

cp .env.example .env
# Then edit .env with your actual API key

Option 2: Environment Variable

bash

export CONTEXT7_API_KEY="your-api-key"

Priority: Environment variable > .env file

Get API Key: Visit context7.com/dashboard to register and obtain your API key.

If not set, the API will use public rate limits (lower quota).

Best Practices

Query Specificity

•Pass the full user question as the query parameter for better relevance
•Include specific feature names (e.g., "useEffect cleanup" vs just "useEffect")

Version Awareness

•When users mention versions, use version-specific library IDs
•Example: /vercel/next.js/v15.1.8 instead of /vercel/next.js

Error Handling

•If library search returns no results, inform user and suggest alternatives
•If API fails, fall back to training data but mention it may be outdated
•Handle rate limits gracefully (429 errors)

Response Quality

•Don't dump entire documentation - extract relevant parts
•Combine multiple doc snippets if needed for complete answer
•Always include practical code examples

Example Workflows

Example 1: React Hook Question

User: "How do I use useEffect to fetch data in React 19?"

Skill Actions:

•Detect trigger: "How do I use" + "useEffect" + "React 19"
•Search: node context7-api.js search "react" "useEffect fetch data"
•Select: /facebook/react/v19.0.0 (version match)
•Fetch: node context7-api.js context "/facebook/react/v19.0.0" "useEffect data fetching"
•Respond with current React 19 patterns (e.g., using use() hook if applicable)

Example 2: Next.js Configuration

User: "配置 Next.js 15 的中间件"

Skill Actions:

•Detect trigger: "配置" + "Next.js 15" + "中间件"
•Search: node context7-api.js search "next.js" "middleware configuration"
•Select: /vercel/next.js/v15.1.8
•Fetch: node context7-api.js context "/vercel/next.js/v15.1.8" "middleware"
•Respond with Next.js 15 middleware setup

Example 3: Prisma Relations

User: "Show me how to define one-to-many relations in Prisma"

Skill Actions:

•Detect trigger: "Show me how" + "Prisma"
•Search: node context7-api.js search "prisma" "one-to-many relations"
•Select: /prisma/prisma (highest trust score)
•Fetch: node context7-api.js context "/prisma/prisma" "one-to-many relations"
•Respond with Prisma schema examples

Architecture: Context Separation

Why Split into Two Skills?

This skill adopts a two-phase architecture:

•
Main Skill (context7-auto-research) - Needs conversation context:
- •Detect trigger keywords in user message
- •Extract user query intent
- •Select best matching library (version, name, trust score)
- •Integrate documentation into response
•
Sub-Skill (context7-fetcher) - Independent context (context: fork):
- •Execute API calls to Context7
- •Pure HTTP requests, no conversation history needed
- •Reduce Token consumption

Benefits

Aspect	Main Skill	Sub-Skill
Context	Full conversation	Fork (independent)
Purpose	Intent analysis	API execution
Token usage	Higher	Lower
Execution	Sequential	Can be parallel

Call Flow

code

User Query → Main Skill (detect + analyze)
                ↓
           Task Tool → Sub-Skill (API search)
                ↓
           Main Skill (select best match)
                ↓
           Task Tool → Sub-Skill (API fetch docs)
                ↓
           Main Skill (integrate + respond)

Integration with Existing Skills

This skill complements the existing documentation-lookup skill:

•auto-research: Proactive, automatic activation
•documentation-lookup: Manual, user-invoked via /context7:docs

Both can coexist - use auto-research for seamless UX, documentation-lookup for explicit queries.

Performance Considerations

•Cache responses: Documentation changes infrequently
•Parallel requests: If user asks about multiple libraries, fetch in parallel using multiple Task calls
•Timeout handling: Set reasonable timeouts (5-10s) for API calls
•Fallback strategy: If API unavailable, use training data with disclaimer
•Context efficiency: Sub-skill uses fork context to minimize Token consumption

Limitations

•Requires internet connection for API access
•Subject to Context7 API rate limits
•May not have documentation for very new or obscure libraries
•Documentation quality depends on source repository structure