Pydantic AI skill for building and managing Agent Skills. Enables AI agents to progressively discover, load, and execute specialized tasks. Supports filesystem and programmatic skills. Integrates with Claude agents.
git clone https://github.com/DougTrajano/pydantic-ai-skills.gitpydantic-ai-skills is a standardized framework for creating and managing Agent Skills within Pydantic AI, following the open Agent Skills specification maintained by Anthropic. It enables AI agents to progressively discover and load modular collections of instructions, scripts, and tools for domain-specific tasks, reducing token usage through lazy loading. The package supports both filesystem-based skills defined as Markdown directories and programmatic skills created with Python decorators or dataclasses. It integrates directly with Pydantic AI agents through the SkillsCapability or SkillsToolset APIs, providing type-safe skill definitions with automatic validation and built-in security features like path traversal prevention.
[{"step":1,"action":"Identify the skill to load","details":"Use the skill name (e.g., 'file_processor', 'data_validator') or specify a filesystem path to a custom skill. For Claude agents, ensure the skill is compatible with the agent's environment."},{"step":2,"action":"Define execution parameters","details":"Provide required parameters such as input files, output formats, or chunk sizes. Use clear and specific values to avoid ambiguity."},{"step":3,"action":"Execute the skill","details":"Run the skill using the Pydantic AI manager. Monitor the execution steps for real-time feedback. For filesystem skills, ensure the path is accessible."},{"step":4,"action":"Validate the output","details":"Review the structured output for success status, execution metrics, and any errors. Use the output for further processing or integration with other tools."},{"step":5,"action":"Integrate with agents (optional)","details":"If using a Claude agent, pass the execution context and results back to the agent for further action or reporting. Ensure the agent has access to the output files."}]
Building research assistants that dynamically load domain-specific skills like arXiv search or web research
Creating multi-skill agents that selectively load instructions based on task relevance
Developing modular AI applications with reusable skill packages across projects
Implementing progressive disclosure patterns to optimize token usage in agent prompts
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/DougTrajano/pydantic-ai-skillsCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Act as a Pydantic AI skill manager. Load the following skill: [SKILL_NAME]. Execute it with the parameters: [PARAMETERS]. If the skill requires filesystem access, use the path: [FILESYSTEM_PATH]. If the skill needs to integrate with a Claude agent, include the agent's context: [AGENT_CONTEXT]. Return the output in a structured format with clear execution steps and results.
```json
{
"skill_name": "file_processor",
"execution_steps": [
{
"step": 1,
"action": "Loading skill from filesystem",
"status": "success",
"details": {
"path": "/skills/file_processor.py",
"dependencies": ["pandas>=1.5.0", "numpy>=1.23.0"]
}
},
{
"step": 2,
"action": "Validating skill parameters",
"status": "success",
"details": {
"input_file": "/data/input.csv",
"output_format": "json",
"chunk_size": 1000
}
},
{
"step": 3,
"action": "Executing skill logic",
"status": "success",
"details": {
"records_processed": 5000,
"processing_time": "2.3s",
"output_file": "/data/output.json"
}
},
{
"step": 4,
"action": "Generating execution report",
"status": "success",
"details": {
"total_skills_loaded": 1,
"successful_executions": 1,
"failed_executions": 0,
"validation_errors": 0
}
}
],
"final_output": {
"status": "completed",
"result": "File processed successfully. Output saved to /data/output.json",
"metrics": {
"processing_speed": "2173.91 records/sec",
"memory_usage": "128MB"
}
}
}
```IronCalc is a spreadsheet engine and ecosystem
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