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when detecting library/framework-related queries, ensuring responses use up-to-date information instead of potentially outdated training data.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:

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:

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:

{ "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:

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:

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

Response format:

{ "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

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

• Returns matching libraries with metadata

• Use for initial library resolution

Get Context

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:

In .claude/skills/context7-auto-research/.env

CONTEXT7_API_KEY=your_api_key_here

You can copy from the example:

cp .env.example .env

Then edit .env with your actual API key

Option 2: Environment Variable

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

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