Fetch current API documentation, version-specific guides, and verified code examples directly within your AI agent workflow.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install context7-docs
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install context7-docs using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Context7 Documentation Lookup?
Context7 is a sophisticated documentation retrieval tool designed to bridge the gap between an AI agent's static training data and the rapidly evolving software ecosystem. By integrating this skill into Openclaw Skills, developers can ensure their AI assistants have access to the latest API references, setup guides, and migration instructions for frameworks like Next.js, React, and Supabase.
This skill eliminates the risk of AI hallucinations associated with outdated library versions by providing real-time data from verified sources. It allows agents to act as expert technical consultants who always have the manual for the latest release in hand, significantly improving the reliability of generated code and configuration advice.
Context7 Documentation Lookup Use Cases
- Retrieving the latest API methods for fast-moving frameworks when training data is likely stale.
- Fetching version-specific code examples, such as differentiating between Next.js 13 and 14 patterns.
- Verifying whether a specific configuration or migration path is still officially recommended.
- Automating the lookup of setup guides during the initialization of new projects or toolchains.
- Searching for high-quality code snippets based on benchmark scores and source reputation.
How Context7 Documentation Lookup Works
- The AI agent identifies a need for external documentation based on a user's specific technical query.
- The resolve-library script is executed to find the correct library ID and available versions from the Context7 database.
- The agent evaluates the returned search results using metrics like benchmark scores, source reputation, and snippet counts.
- A specific library ID is selected, and the query-docs script is called to fetch the relevant context and code examples.
- The retrieved documentation is processed and injected into the agent's context window to provide an accurate, up-to-date response.
Context7 Documentation Lookup Setup
To integrate this skill into your Openclaw Skills environment, ensure your system has curl and jq installed.
# Set your API key for higher rate limits
export CONTEXT7_API_KEY="your-key-here"
# Test the library resolution script
bash scripts/resolve-library.sh --query "authentication" --library-name "next.js"
You can obtain a free API key at context7.com/dashboard to facilitate seamless documentation fetching.
Context7 Documentation Lookup Data Schema & Taxonomy
The skill processes structured data from the Context7 API to ensure high-relevance retrieval. The following metadata taxonomy is used to organize library data:
| Metadata Field | Description |
|---|---|
| Library ID | The unique identifier used for querying (e.g., /vercel/next.js) |
| Benchmark Score | A quality indicator out of 100 for the documentation set |
| Source Reputation | Credibility rating (High, Medium, Low) |
| Code Snippets | Number of available verified code examples |
| Version ID | Specific path for versioned documentation (e.g., /v14.3.0) |
Context7 Documentation Lookup Advanced Features
- Version-specific targeting to avoid legacy code suggestions and ensure compatibility.
- Multi-step validation logic that picks the best documentation source based on benchmark scores.
- Direct API integration support via curl for high-performance, script-based workflows.
- Source reputation filtering to prioritize official or high-authority documentation repositories.
- Intelligent query refinement to optimize the relevance of documentation chunks returned to the AI.
SKILL.md
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