Pydantic

What it does

This skill leverages Pydantic, a Python library used for data validation and parsing. It allows AI agents to define schemas for structured data, validate incoming data against those schemas, and parse data into Python objects with type safety. This ensures data integrity and simplifies downstream processing within the agent's workflow.

When to use it

• Data Validation: When receiving user input or external API responses that need to be validated before further processing (e.g., validating a JSON payload for an order).
• Configuration Management: To enforce specific data structures and types when loading configuration files, preventing errors due to incorrect formatting.
• API Integration: When interacting with APIs that return structured data, ensuring the agent correctly interprets and handles the responses.
• Data Transformation: To reliably transform raw data into a consistent format for analysis or other tasks.

Key capabilities

• Data validation using Pydantic schemas
• Parsing of data into Python objects based on defined schemas
• Type safety enforcement during data processing
• Schema definition and management

Example prompts

• "Validate this JSON payload against the 'Order' schema: { ... }"
• "Parse this string as a 'User' object using the User schema."
• “Create a Pydantic model for an invoice with fields for date, amount, and customer.”

Tips & gotchas

• Requires Python 3.7 or higher to be installed in the agent’s environment.
• The agent needs access to the relevant Pydantic schemas; ensure these are provided or accessible.