Does this skill help with data validation?

Yes, it covers Pydantic validators, attrs validators, and dataclass post-init checks to ensure your data structures always contain valid information.

When should I use Pydantic over standard dataclasses?

Use Pydantic when you need automatic data validation, coercion, and easy JSON serialization, especially for web APIs. Standard dataclasses are ideal for simple internal data containers where you want to avoid external dependencies.

Does it support JSON serialization?

Yes, it includes methods for converting models to and from dictionaries and JSON strings, specifically using Pydantic's model_dump and model_validate_json features.

How do I handle default values for mutable types like lists?

The skill demonstrates the use of field(default_factory=list) for dataclasses and similar patterns in Pydantic and attrs to safely handle mutable defaults without sharing state between instances.

Can I create immutable objects with these tools?

Absolutely. The skill provides patterns for using 'frozen=True' in both dataclasses and Pydantic to create read-only data structures that prevent accidental modification.

Python Data Modeling & Classes

Name: Python Data Modeling & Classes
Author: TheBushidoCollective

byTheBushidoCollective

•

Database Management

Master Python data structures using dataclasses, attrs, and Pydantic for clean, type-safe modeling and automated validation.

About

This skill equips Claude with specialized knowledge to implement robust Python data models using the standard dataclasses module, the flexible attrs library, and the high-performance Pydantic framework. It provides patterns for creating immutable structures, enforcing data validation at runtime, handling complex serialization, and choosing the right modeling tool based on project requirements like performance, dependency constraints, or API compatibility. Whether you are building simple data containers or complex API schemas, this skill ensures your Python models are professional, efficient, and maintainable.

Key Features

Comprehensive Pydantic model implementation with runtime validation, field aliasing, and JSON serialization.
Advanced dataclass configurations including frozen types, order comparison, and memory-efficient slots.
Custom field validators, converters, and post-initialization logic for enforcing complex business rules.
72 GitHub stars
Comparison-based selection logic to choose between dataclasses, attrs, and Pydantic for specific use cases.
Inheritance and generic type support for building scalable and reusable data structures.

Use Cases

Implementing Domain-Driven Design (DDD) entities with strict validation and custom serialization logic.
Designing robust API request and response models with Pydantic for FastAPI or Flask services.
Creating immutable, memory-efficient data containers for high-performance data processing pipelines.

About

Key Features

Comprehensive Pydantic model implementation with runtime validation, field aliasing, and JSON serialization.
Advanced dataclass configurations including frozen types, order comparison, and memory-efficient slots.
Custom field validators, converters, and post-initialization logic for enforcing complex business rules.
72 GitHub stars
Comparison-based selection logic to choose between dataclasses, attrs, and Pydantic for specific use cases.
Inheritance and generic type support for building scalable and reusable data structures.

Use Cases

Implementing Domain-Driven Design (DDD) entities with strict validation and custom serialization logic.
Designing robust API request and response models with Pydantic for FastAPI or Flask services.
Creating immutable, memory-efficient data containers for high-performance data processing pipelines.