pydantic

High-performance Python data validation with type hints, Rust-powered core, and seamless FastAPI/Django integration. Python data validation using type hints and runtime type checking with Pydantic v2's Rust-powered core for high-performance validation. When using Pydantic, consider these complementary skills: [Full integration patterns available in respective skills if deployed together] - Validates data at runtime using Python type hints with automatic type coercion; strict mode available per-field or model-wide - Supports nested models, recursive types, generics, and custom validators (field-level and model-level) for complex validation logic - Includes built-in types for emails, URLs, file paths, secrets, and constrained integers/strings; extensible via custom Pydantic core schemas - Serializes to dict, JSON, or custom formats with field-level control; integrates directly with FastAPI request/response models and SQLAlchemy ORM - Settings management via BaseSettings for environment variables and config files; computed fields for derived properties - API request/response validation (FastAPI, Django) - Settings and configuration management (env variables, config files) - ORM model validation (SQLAlchemy integration) - Data parsing and serialization (JSON, dict, custom formats) - Type-safe data classes with automatic validation - CLI argument parsing with type safety -...