json-to-pydantic

Name: json-to-pydantic
Availability: InStock
Author: phenobarbital

Convert JSON data into Python Pydantic models with nested classes.

13|Updated Feb 21, 2026

Search Tags:#pydantic #python #models #json #code-generation #type-inference #nested-objects

Ready to run

npx skills add https://github.com/phenobarbital/antigravity-config --skill json-to-pydantic

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: json-to-pydantic
Source: https://github.com/phenobarbital/antigravity-config/tree/main/backend/.agent/skills/json-to-pydantic
Command: npx skills add https://github.com/phenobarbital/antigravity-config --skill json-to-pydantic

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Converts JSON data into Python Pydantic data models to enforce type safety.

Core Features & Use Cases

Analyze JSON data to infer schemas and generate Pydantic models
Create nested classes for nested objects and arrays
Validate and serialize data with type-safe models
Use case: Given a REST API response, automatically produce Python models for client-side validation.

Quick Start

Provide a JSON sample and I will generate corresponding Pydantic models.

Frequently Asked Questions about json-to-pydantic

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

FAQPage Schema

How do I convert JSON to Pydantic models in Python?▼

You can convert JSON to Pydantic models by providing a JSON sample to infer the schema. The tool generates ready-to-use Python code with PascalCase class names, nested classes, and optional defaults for type safety.

Can I generate nested Pydantic models from JSON arrays and optional fields?▼

Yes, generating nested Pydantic models from JSON arrays and optional fields is supported. The tool analyzes the JSON data payload and automatically creates nested classes as needed to enforce type safety.

What is the best way to create type-safe Python models from an API response?▼

The best way to create type-safe Python models from a REST API response is to use a JSON sample for schema inference. This produces Pydantic models for client-side validation and serialization automatically.

Does this JSON to Pydantic conversion support type inference for complex data payloads?▼

Yes, type inference for complex data payloads is fully supported. The conversion applies type inference rules to handle nested objects, arrays, and optional fields, generating ready-to-use Python code.

How do I handle optional fields when generating Pydantic models from JSON?▼

Optional fields are handled during JSON to Pydantic conversion by applying type inference rules. The generated Python code includes optional defaults and nested classes to accurately validate the data payload.