An interactive learning skill that connects users to hands-on Python programming courses and browser-based labs on the LabEx platform.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install learn-python
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install learn-python using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The learn-python skill facilitates a direct connection between learners and the LabEx interactive catalog. By utilizing Openclaw Skills, this tool enables AI agents to recommend highly relevant, hands-on content tailored to the user's specific goals. It prioritizes practical application, ensuring that users spend more time coding in real environments rather than reading passive documentation.
This skill is specifically optimized to navigate the Python learning path, providing a structured approach from basic syntax to advanced topics. It bypasses generic career advice to deliver immediate, actionable lab URLs that users can open in their browser to begin practicing instantly.
To enable this skill within your environment, ensure you have access to the LabEx MCP server. You can integrate it by configuring your agent to use the following base routes:
# List all available Python courses
GET https://mcp.labex.io/learn/python/courses
# List labs for a specific course
GET https://mcp.labex.io/learn/<course-alias>/labs
No specific credentials or virtual machine configurations are required to browse and recommend the public catalog.
The skill interacts with the LabEx public catalog via structured API responses. Key data elements include:
| Attribute | Description |
|---|---|
| course-alias | Unique identifier for specific Python courses used in API expansion |
| lab-url | Publicly accessible URL (https://labex.io/labs/...) for interactive learning |
| course-list | Collection of available Python learning paths and high-level metadata |
| lab-metadata | Details on lab difficulty, specific topics covered, and prerequisites |
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