Agent skill · software engineering · aiskillstore
pydantic
Data validation and settings management using Python type annotations with Pydantic v2
Why this skill is useful
Provides domain-specific knowledge on data validation patterns and type safety in Python applications using Pydantic v2.
What it needs
About 3k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Pydantic v2 Framework Skill Pydantic is a data validation library that uses Python type annotations to define data schemas, offering fast and extensible validation with automatic type coercion. Quick Start Basic Model Definition Validation from Data Sources Common Patterns Field Configuration Model Configuration Custom Validation Serialization Control Custom Serialization Nested Models and Relationships Enum Integration TypeAdapter for Standalone Validation Data Validation Patterns Requirements Python 3.8+ Pydantic v2.x: uv add pydantic Optional dependencies for enhanced types: uv add pydantic[email] for EmailStr uv add pydantic[url] for HttpUrl uv add pydantic[typing-extensions] for extended type support Best Practices 1. Use specific types: Prefer conint(gt=0) over int for positive numbers 2. Configure models: Use ConfigDict to set global model behavior 3. Handle validation errors: Always wrap model creation in try/catch blocks 4. Use field validators: Implement custom validation logic with @fieldvalidator 5. Control serialization: Use modeldump() parameters to control output format 6. Leverage type coercion: Pydantic automatically converts compatible types 7. Use nested models: Break complex data into smaller, reusable models
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/pydantic