pydantic-models

What problem does it solve?

This Skill enforces best practices for creating data models using Pydantic, solving issues like inconsistent data validation, unclear API contracts, and manual serialization/deserialization. It ensures data integrity, improves code readability, and streamlines API development.

Core Features & Use Cases

• BaseModel & Field: Guides on defining models with type hints, default values, and rich field constraints (min/max length, regex, numeric ranges).
• Validators: Provides patterns for field_validator for single-field logic and model_validator for cross-field validation.
• Nested Models & Generics: Shows how to build complex, hierarchical data structures and use generic types for reusable response models.
• Configuration & Serialization: Covers model_config for strict validation, computed_field for derived data, and field_serializer for custom JSON output.
• Use Case: A backend developer is building a new API endpoint that accepts user data. This skill helps them define a UserCreate Pydantic model with email format validation, password strength checks, and age constraints, ensuring only valid data enters the system.

Quick Start

Create a Pydantic model for a Product with fields for name, price, and quantity, ensuring price is a positive decimal.