Does this skill support Pydantic v1 or v2?

This skill is specifically optimized for Pydantic v2, utilizing modern features such as the 'populate_by_name' configuration and updated Field syntax.

Can I use these models for database interactions?

Yes, it includes an 'InDB' variant specifically designed for database document structures, including metadata fields like 'doc_type' for systems like Cosmos DB.

What is the multi-model pattern in this skill?

It is a design pattern that separates data schemas into distinct variants (Base, Create, Update, Response, and InDB) to handle different API lifecycle stages and database requirements cleanly.

Is it compatible with FastAPI?

Absolutely; the generated models follow the industry best practices recommended for FastAPI and other modern Python web frameworks for automatic documentation and validation.

How does it handle camelCase for frontend integration?

It implements Pydantic aliases to allow your API to accept camelCase from frontend applications while maintaining standard snake_case in your Python backend logic.

Pydantic Model Generator

Name: Pydantic Model Generator
Author: JantonioFC

byJantonioFC

•

Desarrollo de API

Generates robust Pydantic v2 models using a multi-model pattern to ensure clean API contracts and consistent data validation.

This skill provides a standardized framework for creating Pydantic models in Python applications, specifically following the multi-model pattern. It facilitates the creation of distinct schemas for creation, updates, and responses, while supporting advanced features like camelCase aliasing and database-specific document types. By automating the boilerplate of data validation and API schema definition, it ensures developers maintain consistent, high-quality codebases that integrate seamlessly with both backend databases and frontend TypeScript types.

Características Principales

01Automatic camelCase to snake_case aliasing for frontend compatibility

02Multi-model pattern implementation (Base, Create, Update, Response, InDB)

032 GitHub stars

04Built-in Pydantic v2 validation and configuration patterns

05Database-ready document structures with doc_type support

06Smart optional field handling for PATCH request schemas

Casos de Uso

01Enforcing strict data validation and type safety across Python microservices

02Defining data models for NoSQL databases like Cosmos DB or MongoDB

03Building RESTful API request and response schemas in FastAPI or Flask

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jantoniofc/skillsbank pydantic-models-py

For use in Claude.ai and ChatGPT

Características Principales

01Automatic camelCase to snake_case aliasing for frontend compatibility

02Multi-model pattern implementation (Base, Create, Update, Response, InDB)

032 GitHub stars

04Built-in Pydantic v2 validation and configuration patterns

05Database-ready document structures with doc_type support

06Smart optional field handling for PATCH request schemas

Casos de Uso

01Enforcing strict data validation and type safety across Python microservices

02Defining data models for NoSQL databases like Cosmos DB or MongoDB

03Building RESTful API request and response schemas in FastAPI or Flask

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jantoniofc/skillsbank pydantic-models-py

For use in Claude.ai and ChatGPT