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pydantic-patterns

Creates typed Python models with validation, serialization, and ORM mode using Pydantic v2.

46|8|Updated Mar 25, 2026
Search Tags:#api#pydantic#validation#patterns#orm#serialization
One-click install
npx skills add https://github.com/manikosto/claude-code-python-stack --skill pydantic-patterns-manikosto
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pydantic-patterns
Source: https://github.com/manikosto/claude-code-python-stack/tree/main/skills/pydantic-patterns
Command: npx skills add https://github.com/manikosto/claude-code-python-stack --skill pydantic-patterns-manikosto

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data validation and serialization patterns using Pydantic v2.

Core Features & Use Cases

  • Basic Models, Validators, Computed Fields, and Discriminated Unions
  • Serialization Control, Nested Models, and Settings Management
  • Integration with SQLAlchemy and custom types for API-first design

Quick Start

Create a BaseModel for API input and enable ORM mode to map data from your ORM models.

Frequently Asked Questions about pydantic-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate complex Python data using Pydantic v2 models?▼

Validate complex Python data using Pydantic v2 by defining BaseModel structures with field_validator and model_validator decorators to enforce types and transform inputs before serialization.

How do I map SQLAlchemy ORM data to Pydantic v2 models?▼

Map SQLAlchemy ORM data to Pydantic v2 models by enabling ORM mode in your model configuration, allowing seamless data exchange between ORM objects and API schemas.

What is the best way to serialize nested models and discriminated unions in Pydantic?▼

Serialize nested models and discriminated unions in Pydantic using built-in serialization control features to manage API output formats, ensuring complex nested data structures serialize correctly.

Does Pydantic v2 support computed fields for API data transformation pipelines?▼

Pydantic v2 supports computed fields to dynamically calculate derived values during data transformation pipelines, integrating smoothly with validators to serialize dynamic API schema properties.

Can I manage application settings and custom types in FastAPI with Pydantic v2?▼

Manage application settings and custom types in FastAPI with Pydantic v2 by utilizing settings management features and custom type integrations for robust API-first design and validation.

When should I use model_validator instead of field_validator in Pydantic v2?▼

Use model_validator in Pydantic v2 for cross-field validation across the entire model, whereas field_validator targets individual data fields for specific type checking and data transformation.

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