How does this skill improve code quality?

It enforces production standards by pre-configuring Ruff for linting and formatting, and setting up Mypy for strict static type checking.

Does it work with web frameworks other than FastAPI?

While it features specific FastAPI patterns, the general principles for project structure, async I/O, type hinting, and testing apply to Django, Flask, and other modern frameworks.

Does it include database integration patterns?

Yes, it provides patterns for dependency injection and database interactions, specifically tailored for asynchronous database drivers and FastAPI.

What Python versions does this skill support?

This skill is specifically optimized for modern Python development, targeting version 3.12 and newer to leverage the latest language features and performance improvements.

Modern Python Development

Name: Modern Python Development
Author: januarfonti

byjanuarfonti

API Development

Streamlines the creation of production-grade Python applications using modern standards, FastAPI, and asynchronous patterns.

About

The Python Development skill equips Claude with the expertise to architect, develop, and test modern Python 3.12+ applications. It provides standardized project structures, strict type-hinting patterns, and advanced asynchronous workflows, making it ideal for building high-performance APIs with FastAPI, robust data processing scripts, and maintainable backend services. By integrating best practices like Ruff for linting and Mypy for static analysis, it ensures code quality and production readiness from the initial setup through deployment.

Key Features

High-performance FastAPI and Pydantic implementation patterns
Strict type safety configuration using Mypy and modern TypeVars
Comprehensive testing setup with Pytest and asynchronous mocking
Standardized Python 3.12+ project scaffolding with pyproject.toml
Advanced async/await patterns for I/O-bound operations and streaming
0 GitHub stars

Use Cases

Modernizing legacy Python codebases to comply with current best practices
Developing high-concurrency data processing and automation scripts
Building scalable RESTful APIs with FastAPI and Pydantic validation

About

Key Features

High-performance FastAPI and Pydantic implementation patterns
Strict type safety configuration using Mypy and modern TypeVars
Comprehensive testing setup with Pytest and asynchronous mocking
Standardized Python 3.12+ project scaffolding with pyproject.toml
Advanced async/await patterns for I/O-bound operations and streaming
0 GitHub stars

Use Cases

Modernizing legacy Python codebases to comply with current best practices
Developing high-concurrency data processing and automation scripts
Building scalable RESTful APIs with FastAPI and Pydantic validation