A documentation retrieval skill that connects AI agents to the Context7 API for real-time library specifications and best practices.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install context7-api
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-api 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 Fetcher?
The Context7 Documentation Fetcher is a specialized tool within the Openclaw Skills ecosystem designed to bridge the gap between an AI's training data and the latest library releases. By leveraging the Context7 API, this skill allows developers and AI agents to retrieve precise, up-to-date documentation for any modern library or framework, including React, Next.js, and Supabase.
This integration ensures that coding agents operate with maximum technical accuracy, preventing the implementation of deprecated APIs or outdated patterns. Whether you are debugging a version-specific issue or exploring a new library for the first time, this Openclaw Skills implementation provides the live technical context needed to write production-ready code without leaving your development environment.
Context7 Documentation Fetcher Use Cases
- Implementing features that rely on third-party packages or complex external libraries like Supabase or Next.js.
- Debugging library-specific issues that occurred after the AI model's training data cutoff.
- Verifying API signatures and implementation patterns for modern frameworks.
- Searching for specific documentation segments such as authentication, middleware, or row-level security.
- Ensuring high-quality code generation by providing the most recent library best practices as part of Openclaw Skills workflows.
How Context7 Documentation Fetcher Works
- The AI agent or developer initiates a search for a specific library name to retrieve its metadata.
- The skill identifies the unique library ID from the search results (e.g., /vercel/next.js).
- A specific query is sent to the Context7 API using that ID to fetch relevant documentation snippets.
- The skill returns formatted technical context directly into the agent's workflow to inform code generation.
Context7 Documentation Fetcher Setup
To install the Context7 documentation tool for Openclaw Skills, ensure the scripts are placed in your local skills directory and Python 3 is available.
# Navigate to your skills directory
cd ~/.claude/skills/context7
# Search for a library to get its ID
python3 scripts/context7.py search "next.js"
# Fetch documentation for a specific topic
python3 scripts/context7.py context "/vercel/next.js" "app router middleware"
Context7 Documentation Fetcher Data Schema & Taxonomy
The skill processes documentation data using a structured search and fetch workflow. Data is organized according to the following taxonomy:
| Component | Type | Description |
|---|---|---|
| library-id | String | The unique identifier (slug) used by Context7 to track a repository. |
| query | String | The specific technical topic or API pattern being researched. |
| format | Enum | The output format, supporting both txt and md (Markdown). |
| tokens | Integer | A limit parameter to control the length of the retrieved documentation. |
Context7 Documentation Fetcher Advanced Features
- Custom output formatting allowing toggles between plain text and Markdown for better readability in different IDEs.
- Token management using the --tokens flag to optimize context window usage within Openclaw Skills.
- Proactive documentation fetching for libraries released or updated beyond the 2023/2024 training cutoffs.
- Deep search capabilities that allow for specific queries within a library's documentation tree.
SKILL.md
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