Does this skill support modern Python 3.9+ syntax?

Yes, it specifically enforces modern syntax like using built-in collection types (list, dict) for annotations instead of the older typing module imports.

Can I use this for database modeling?

Absolutely. The skill includes specific patterns for mapping Pydantic models to external systems like MongoDB, ensuring type safety during read and write operations.

How does it handle complex function return types?

It follows a 'one breath' rule: if a return type is too complex to read in one breath, it mandates the use of a named Pydantic model for clarity and reusability.

Why does this skill prefer Pydantic over dataclasses or TypedDict?

Pydantic provides robust runtime validation and better integration with external data sources, ensuring that your data models are not just typed, but also correct at runtime.

Pythonista Typing & Pydantic

Name: Pythonista Typing & Pydantic
Author: gigaverse-app

bygigaverse-app

Desarrollo de API

Enforces modern Python type annotations and Pydantic best practices for robust, type-safe data modeling.

Acerca de

The pythonista-typing skill streamlines the development of high-quality Python applications by enforcing modern 3.9+ syntax and strict Pydantic integration. It provides Claude with specific patterns to replace ambiguous structures like raw dictionaries or 'Any' types with validated Pydantic models and structural Protocols. This skill is essential for developers building scalable backend services, complex APIs, or data-driven applications where structural integrity, readability, and runtime validation are critical for long-term maintainability.

Características Principales

0 GitHub stars
Mandates Pydantic models for all structured data to ensure runtime validation
Standardizes complex return types by requiring named Pydantic models
Enforces modern Python 3.9+ type hinting syntax (e.g., list[str] over List[str])
Implements PEP 544 Protocols for structural typing and callback signatures
Provides patterns for type-safe integration with external systems like MongoDB

Casos de Uso

Building production-grade APIs with rigorous schema validation and type safety
Developing complex data processing pipelines that require clear, named data structures
Refactoring legacy Python codebases to meet modern typing and Pydantic standards

Acerca de

Características Principales

0 GitHub stars
Mandates Pydantic models for all structured data to ensure runtime validation
Standardizes complex return types by requiring named Pydantic models
Enforces modern Python 3.9+ type hinting syntax (e.g., list[str] over List[str])
Implements PEP 544 Protocols for structural typing and callback signatures
Provides patterns for type-safe integration with external systems like MongoDB

Casos de Uso

Building production-grade APIs with rigorous schema validation and type safety
Developing complex data processing pipelines that require clear, named data structures
Refactoring legacy Python codebases to meet modern typing and Pydantic standards