json-to-pydantic_skill

Overview

This skill converts raw JSON snippets or API responses into clean, idiomatic Pydantic models for Python. It infers field types, extracts nested objects into sub-classes, and emits ready-to-use code with proper typing and defaults. The goal is to save time and reduce errors when mapping untyped JSON to structured data classes.

How this skill works

Provide a JSON object and the skill analyzes each key to infer a Pydantic-friendly type (str, int/float, bool, List, Optional). Nested dictionaries become their own PascalCase sub-classes and arrays are typed as List[ElementType]. Fields that may be missing or null are given Optional[...] with a default of None, and the output includes necessary imports from typing and pydantic.BaseModel.

When to use it

• Converting sample API responses into Pydantic models for development or testing.
• Generating typed models from JSON payloads before integrating with endpoints.
• Rapidly creating data classes from example data during prototyping.
• Standardizing request/response shapes for validation and serialization.
• Documenting expected JSON structure with concrete, executable classes.

Best practices

• Use PascalCase for class names and snake_case for field names to match Python conventions.
• Prefer int vs float by inspecting numeric values; default to float if mixed types occur.
• Mark fields as Optional[...] with default None if they can be missing or null.
• Extract nested objects into separate classes to keep models composable and testable.
• Add example values or Field(...) annotations manually when stricter validation is required.

Example use cases

• Turn a user profile JSON into a User and nested Preferences Pydantic model for request validation.
• Convert an API list response into a List[Item] model for typed processing in business logic.
• Generate models from webhook payloads to validate incoming events before handling.
• Create DTOs from third-party API examples to simplify integration and error handling.