pydantic-models

Name: pydantic-models
Availability: InStock
Author: Fran

Builds validated Python data models with Pydantic v2.

Updated Aug 27, 2026

Search Tags:#pydantic #python #settings #data-validation #api-models #model-validators

Ready to run

npx skills add https://github.com/franciscosanchezn/easyfactu-es --skill pydantic-models-franciscosanchezn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pydantic-models
Source: https://github.com/franciscosanchezn/easyfactu-es/tree/main/.github/skills/pydantic-models
Command: npx skills add https://github.com/franciscosanchezn/easyfactu-es --skill pydantic-models-franciscosanchezn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Pydantic models help you build type-safe, validated data structures for configuration, API payloads, and domain models in Python, reducing runtime errors from invalid data.

Core Features & Use Cases

Define data models with strict validation rules using Pydantic v2 (BaseModel, Field, ConfigDict)
Validate and parse external data (JSON, YAML, env vars) and manage configuration with settings classes
Support advanced patterns: validators (field and model), computed fields, discriminated unions, and generic models for scalable APIs

Quick Start

Create a simple User model with fields id, username, and email, and validate input data using Pydantic v2.

Frequently Asked Questions about pydantic-models

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

FAQPage Schema

How do I validate API payloads and configuration data in Python?▼

Use Pydantic BaseModel classes with Field constraints and validators to validate API payloads and configuration data. This approach parses external JSON or environment variables, ensuring data structures are type-safe and reducing runtime errors from invalid inputs.

What is the best way to manage environment variables and settings in Python?▼

The best way to manage environment variables and settings in Python is using Pydantic Settings management classes. This allows you to load and validate environment variables directly into type-safe data models, ensuring your application configuration is strictly validated at startup.

How do I add custom validation rules to a Python data model?▼

You can add custom validation rules to a Python data model by applying Pydantic field and model validators. These validators execute during parsing to enforce specific constraints and complex business logic, ensuring invalid data is rejected before processing.

Does Pydantic v2 support discriminated unions and computed fields?▼

Yes, Pydantic v2 supports discriminated unions and computed fields. These advanced patterns allow you to build scalable APIs by dynamically calculating derived fields and routing data to the correct typed structure during validation and API serialization.

Can I use Pydantic models for parsing YAML files and external data?▼

You can use Pydantic models for parsing YAML files and external data by loading the raw data and passing it through BaseModel validation. This process automatically coerces and validates the parsed structures into your defined domain models.

How do I configure API serialization with Pydantic v2?▼

You configure API serialization with Pydantic v2 by using ConfigDict and model_config. These settings manage how your data models serialize to JSON, controlling field exclusion, alias generation, and strict data output formatting for API responses.