A professional Python scaffolding for developing tool-enabled AI agents that leverage Anthropic Claude Tool Use features via Openclaw Skills.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install testhy
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 testhy 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 Claude Skill Framework?
The Claude Skill Framework is a high-performance architecture designed for developers building modular tools for AI agents. By utilizing the native Tool Use capabilities of Anthropic Claude, this framework allows you to create specialized Openclaw Skills that can be invoked through natural language. It adheres to the design principles of high cohesion and low coupling, enabling developers to focus strictly on the logic of their specific Openclaw Skills while the framework handles complex tasks like dialogue context management and tool discovery.
This framework acts as a bridge between high-level language models and low-level system execution. It provides a standardized way to define, register, and dispatch Openclaw Skills, making it an ideal choice for building robust CLI tools or API-driven AI assistants that need to interact with the real world.
Claude Skill Framework Use Cases
- Creating custom local automation tools that Claude can trigger using Openclaw Skills.
- Developing modular plugins for AI coding assistants to extend their default capabilities.
- Building enterprise-grade AI agents that require strict schema validation for tool execution.
- Prototyping complex agentic workflows where multiple Openclaw Skills must be orchestrated in a single session.
How Claude Skill Framework Works
- The user interacts with the system through the Interaction Layer (CLI or API), sending a natural language prompt.
- The Agent Layer retrieves the conversation history and prepares the payload for the Claude API, incorporating available Openclaw Skills.
- The Registry & Dispatcher layer provides the LLM with the JSON Schema for all registered tools, ensuring the model knows how to call them.
- Claude analyzes the prompt and returns a Tool Use request if a specific function is required from the set of Openclaw Skills.
- The Dispatcher maps the LLM instruction to a specific Python function and executes it within the Skills Layer.
- The execution result is returned to the agent, which then synthesizes a final natural language response for the user.
Claude Skill Framework Setup
To get started with building your own Openclaw Skills, follow these installation steps:
# Clone the framework repository
git clone https://github.com/example/claude-skills-framework.git
cd claude-skills-framework
# Install dependencies
pip install -r requirements.txt
# Configure your API credentials
export ANTHROPIC_API_KEY='your_key_here'
# Run the main entry point
python main.py
Claude Skill Framework Data Schema & Taxonomy
The framework maintains a strict taxonomy for managing Openclaw Skills to ensure reliability and ease of discovery:
| Component | Type | Responsibility |
|---|---|---|
| Skill Base | Abstract Class | Defines the interface and requirements for all Openclaw Skills. |
| Registry | Metadata Store | Manages the mapping between tool names and Python execution logic. |
| Tool Schema | JSON Schema | Dynamically generated definitions used by the LLM to understand Openclaw Skills. |
| Skill Implementation | Python Module | The individual business logic contained within the skills/ directory. |
Claude Skill Framework Advanced Features
- Automatic JSON Schema generation based on Python type hints for all Openclaw Skills.
- Pluggable architecture that supports adding new capabilities without modifying the core agent logic.
- Support for multi-step tool execution where Claude can call multiple Openclaw Skills in sequence to solve a problem.
- Simplified context management that keeps the LLM focused on the current task while maintaining relevant tool history.
SKILL.md
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