python-pro

Develop Python 3.12+ applications with FastAPI, async patterns, and Pydantic validation.

Updated Feb 26, 2026
One-click install
npx skills add https://github.com/AndyAnh174/wellness --skill python-pro-andyanh174
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-pro
Source: https://github.com/AndyAnh174/wellness/tree/main/.agent/skills/python-pro
Command: npx skills add https://github.com/AndyAnh174/wellness --skill python-pro-andyanh174

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Master Python 3.12+ development with modern tooling and production-ready practices.

Core Features & Use Cases

  • Modern Python features: async/await, pattern matching, type hints, Pydantic models, dataclasses
  • Tooling and environment: uv, ruff, mypy/pyright, pyproject.toml, virtual environments, pre-commit, packaging
  • Testing & QA: pytest, Hypothesis, coverage, pytest-benchmark, CI
  • Web APIs & frameworks: FastAPI, Django, SQLAlchemy 2.0+ with async support
  • Deployment & performance: Docker, Kubernetes, caching, performance profiling

Quick Start

Set up a modern Python 3.12+ project with FastAPI, uv, and Pydantic to build a production-ready API.

Frequently Asked Questions about python-pro

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

FAQPage Schema
How do I set up a modern Python 3.12 project with FastAPI and uv?▼

To set up a modern Python project, configure a virtual environment using uv and define settings in pyproject.toml. This foundation enables building production-ready FastAPI applications with Pydantic data validation and async patterns.

What is the best way to test async FastAPI endpoints?▼

The best way to test async endpoints is using pytest with Hypothesis for property-based testing. You can measure performance with pytest-benchmark and track code coverage to ensure robust API quality assurance.

Does SQLAlchemy 2.0 work with FastAPI async patterns?▼

Yes, SQLAlchemy 2.0 works with FastAPI async patterns to handle database operations. Combining them allows you to build high-performance APIs with non-blocking database queries and robust data validation.

How do I configure ruff and mypy in pyproject.toml for type hints?▼

You configure ruff and mypy in pyproject.toml by specifying strict type-checking rules and linting configurations. This enforces modern type hint usage and maintains code quality across your Python project.

How do I deploy Python APIs using Docker and Kubernetes?▼

To deploy Python APIs, containerize your FastAPI application using Docker and orchestrate the deployment with Kubernetes. This approach supports performance profiling and caching for scalable production environments.