pydantic

Validate and coerce Python data using type hints and Pydantic models.

14|1|Updated Mar 9, 2026
One-click install
npx skills add https://github.com/CodeAtCode/oss-ai-skills --skill pydantic-codeatcode
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: pydantic
Source: https://github.com/CodeAtCode/oss-ai-skills/tree/main/frameworks/pydantic
Command: npx skills add https://github.com/CodeAtCode/oss-ai-skills --skill pydantic-codeatcode

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data validation and settings management for Python applications using type hints, with fast performance and JSON Schema generation.

Core Features & Use Cases

  • Data validation using Python type annotations
  • Automatic data coercion and serialization
  • JSON Schema generation and settings management from environment variables
  • Fast performance with Pydantic v2 and flexible model composition

Quick Start

Define a BaseModel and validate data with model_validate.

Frequently Asked Questions about pydantic

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate API input data using Python type hints?

You can validate API input data using Python type hints by defining a Pydantic BaseModel and calling model_validate. This approach automatically coerces incoming data to match your declared types at runtime.

What is the best way to manage application settings from environment variables?

The best way to manage application settings from environment variables is using Pydantic settings management. It automatically parses and validates environment variables directly into type-safe Python models.

Can I generate JSON Schema from Python type annotations automatically?

Yes, you can generate JSON Schema from Python type annotations automatically. Pydantic models natively support JSON Schema generation, allowing you to export structural validation rules for API documentation.

Does data validation with Pydantic work for high-performance parsing?

Data validation with Pydantic is designed for high-performance parsing. Pydantic v2 delivers fast runtime validation and coercion, making it suitable for high-throughput data processing across services.

How do I coerce and serialize nested data structures in Python?

To coerce and serialize nested data structures in Python, define a BaseModel with nested type hints. Pydantic automatically handles complex data coercion and provides built-in serialization methods.

Why do I need runtime data validation if I already use static type hints?

You need runtime data validation because static type hints are not enforced during execution. Pydantic ensures that untrusted external data, such as API payloads, strictly matches your models at runtime.