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pydantic

Guide Pydantic V2 model creation for API request and response validation.

2|Updated Jan 23, 2026
Search Tags:#pydantic#validation#data integrity#type system#serialization#api models
One-click install
npx skills add https://github.com/linguistic76/skuel --skill pydantic-linguistic76
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pydantic
Source: https://github.com/linguistic76/skuel/tree/main/app/.claude/skills/pydantic
Command: npx skills add https://github.com/linguistic76/skuel --skill pydantic-linguistic76

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides expert guidance on using Pydantic V2 for robust data validation, serialization, and API request/response modeling, ensuring data integrity and clear type systems.

Core Features & Use Cases

  • API Request/Response Models: Define clear, validated models for incoming requests and outgoing responses.
  • Input Validation: Ensure user input, external data, and internal data structures meet strict requirements.
  • Field & Model Validators: Implement custom logic for single-field validation and cross-field consistency checks.
  • Serialization Control: Configure how Pydantic models are converted to and from JSON.
  • Use Case: When building an API endpoint that accepts user profile data, use Pydantic models to validate the incoming JSON, ensuring fields like email format, password strength, and required fields are correct before processing.

Quick Start

Use the pydantic skill to create a BaseModel for user registration with fields for username, email, and password.

Frequently Asked Questions about pydantic

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

FAQPage Schema
How do I validate API request and response schemas using Pydantic V2?▼

You validate API request and response schemas in Pydantic V2 by defining BaseModel classes with explicit field types, ensuring incoming JSON and outgoing data strictly match your expected structures before processing.

What is the best way to implement custom cross-field validation logic in Pydantic?▼

The best way to implement custom cross-field validation in Pydantic is using model validators, which allow you to define custom logic that checks consistency and dependencies across multiple fields within the same model.

How do field validators work for checking user input like email format in Pydantic?▼

Field validators in Pydantic work by applying custom validation logic to a single specific field, allowing you to verify formats like email structure or password strength directly during model instantiation.

Can I control how Pydantic models are serialized to and from JSON?▼

Yes, you can control how Pydantic models are serialized to and from JSON by configuring serialization settings, ensuring your API models convert cleanly while maintaining strict data integrity.

When do I need a type system for validating external data structures?▼

You need a type system for validating external data structures when accepting user input or external API data, ensuring that untrusted incoming JSON conforms to strict field requirements before application processing.

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