pydantic

Implement Pydantic v2 data validation and serialization with ConfigDict.

11|2|Updated Feb 18, 2026

Search Tags:#pydantic #python #validation #best-practices #data-validation #pydantic-v2

Ready to run

npx skills add https://github.com/the-perfect-developer/the-perfect-opencode --skill pydantic-the-perfect-developer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pydantic
Source: https://github.com/the-perfect-developer/the-perfect-opencode/tree/main/.opencode/skills/pydantic
Command: npx skills add https://github.com/the-perfect-developer/the-perfect-opencode --skill pydantic-the-perfect-developer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Pydantic v2 provides a rigorous framework for data validation, serialization, and configuration in Python. This skill guides developers to adopt idiomatic, performant patterns that reduce runtime errors and boilerplate when modeling, validating, and transforming data.

Core Features & Use Cases

Idiomatic BaseModel definitions with model_config using ConfigDict to control validation and serialization
Field validators, computed fields, and model-level validators for complex data integrity
ORM integration, serialization customization, and performance-focused patterns (e.g., TypeAdapter usage)

Quick Start

Validate a sample payload using a minimal Pydantic v2 model and explain the recommended model_config patterns.

Frequently Asked Questions about pydantic

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

FAQPage Schema

How do I define data validation models using Pydantic v2?▼

Use BaseModel definitions with model_config and ConfigDict to control validation and serialization rules. This pattern enforces strict type validation for Python data models and API schemas while reducing runtime errors.

What is the best way to add custom field validators in Pydantic v2?▼

The best way to add field validators in Pydantic v2 is using the built-in field validator decorators. This ensures complex data integrity by validating inputs before serialization without writing boilerplate validation logic.

How do I configure serialization and validation behavior in Pydantic v2?▼

Configure serialization and validation behavior in Pydantic v2 by defining a ConfigDict within your model_config. This allows you to customize ORM integration, control serialization options, and apply performance-focused patterns like TypeAdapter.

Can I use computed fields for data transformation in Python data models?▼

Yes, you can use computed fields in Python data models to dynamically calculate values during serialization. Pydantic v2 supports computed fields alongside model-level validators to handle complex data transformation and integrity checks cleanly.

Does Pydantic v2 work with ORM integrations for API schemas?▼

Pydantic v2 works seamlessly with ORM integrations to map database models directly to API schemas. You can use ConfigDict to control this mapping and ensure strict type validation across your data boundary.