pydantic_skill

Overview

This skill provides Pydantic-driven models and validation helpers for Python backends. I focus on type-driven validation, safe serialization, and JSON Schema generation to make request/response handling and configuration robust. The goal is predictable data shapes, clear errors, and easy integration with frameworks like FastAPI.

How this skill works

I define schemas as Python classes that inherit from BaseModel and use type hints to validate and coerce incoming data. Field and model validators let you add custom checks and cross-field logic, while model_dump and model_dump_json produce clean serialized output. BaseSettings support environment-driven configuration and Pydantic can emit JSON Schema for documentation or contract validation.

When to use it

• Validating and normalizing API request and response payloads.
• Defining clear data contracts for services and background jobs.
• Generating JSON Schema for docs, client generation, or validation tooling.
• Loading typed configuration from environment variables or files.
• Protecting endpoints from untrusted or malformed input.

Best practices

• Prefer explicit, typed fields and avoid loosely typed Any for core models.
• Keep validators minimal and fast; perform heavy work outside model validation.
• Use strict types when you need to prevent silent coercion (e.g., StrictInt).
• Use default_factory for mutable defaults to avoid shared state.
• Emit JSON Schema for public contracts and use model_dump() with by_alias when needed.

Example use cases

• Define request/response schemas for FastAPI endpoints and rely on automatic validation.
• Validate incoming webhook payloads, normalize fields, and reject invalid requests with clear errors.
• Create environment-backed settings via BaseSettings for 12-factor apps.
• Serialize models to JSON for APIs or persistence using model_dump_json().
• Generate JSON Schema to drive frontend form validation or API clients.