Does this skill support Pydantic v2 features?

Yes, it is fully optimized for Pydantic v2, including modern features like ConfigDict, model_validator, and field_serializer.

Can this skill handle complex, nested data structures?

Absolutely. It includes patterns for defining nested models and correctly using model_rebuild() for complex relationship handling.

What anti-patterns does this skill prevent?

It proactively flags and corrects common mistakes such as using mutable defaults in models, missing Optional type hints for nullable fields, and redundant manual validation logic.

How does it help with API development?

It provides standardized patterns for creating API request and response models that integrate seamlessly with frameworks like FastAPI to provide automatic validation and documentation.

Pydantic Model Architect

Name: Pydantic Model Architect
Author: ricardoroche

byricardoroche

API Development

Standardizes the creation of robust Python data models using Pydantic best practices for validation and serialization.

About

The Pydantic Model Architect skill empowers Claude to build production-grade data structures with rigorous type safety and validation. It enforces modern Pydantic v2 patterns, including advanced field definitions, custom validators, and model configurations that ensure data integrity. Ideal for developers building FastAPI backends or complex data pipelines, this skill automates the implementation of nested models, computed fields, and JSON schema generation while proactively preventing common anti-patterns like mutable defaults and manual validation logic.

Key Features

Strict model configuration and computed field implementation
Comprehensive field validation using Field constraints and type hints
Advanced custom validation with field_validator and model_validator
Nested model architecture with automatic forward reference handling
0 GitHub stars
Automatic JSON schema generation and serialization patterns

Use Cases

Creating type-safe configuration objects for complex Python applications
Validating and cleaning untrusted data from external JSON sources
Designing request and response schemas for FastAPI or RESTful services

About

Key Features

Strict model configuration and computed field implementation
Comprehensive field validation using Field constraints and type hints
Advanced custom validation with field_validator and model_validator
Nested model architecture with automatic forward reference handling
0 GitHub stars
Automatic JSON schema generation and serialization patterns

Use Cases

Creating type-safe configuration objects for complex Python applications
Validating and cleaning untrusted data from external JSON sources
Designing request and response schemas for FastAPI or RESTful services