Does it support common async frameworks?

Yes, it provides patterns compatible with FastAPI, aiohttp, Sanic, and various async database drivers like asyncpg and motor.

Is testing async code covered?

Yes, the skill includes guidance on using pytest-asyncio for robust unit and integration testing of asynchronous functions.

When should I use this skill?

Use it when building I/O-bound applications, async web APIs, or concurrent systems requiring non-blocking operations in Python.

How does this help with performance?

It provides patterns for concurrent execution, connection pooling, and identifies common mistakes like blocking the event loop with synchronous calls.

Async Python Patterns

Name: Async Python Patterns
Author: NickCrew

byNickCrew

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API Development

Implements high-performance asynchronous Python patterns using asyncio for building non-blocking APIs and concurrent systems.

About

This skill provides expert guidance and implementation patterns for Python's asynchronous programming ecosystem. It helps developers build scalable, high-performance applications by mastering asyncio, await, and concurrent execution strategies. From designing async web APIs with FastAPI to managing complex I/O-bound workloads, the skill covers essential patterns like task management, rate limiting with semaphores, and memory-efficient async iterators. It also includes robust error handling, testing strategies with pytest-asyncio, and performance optimizations to prevent common event-loop-blocking pitfalls.

Key Features

Advanced resource management with async context managers and iterators
Implementation of producer-consumer pipelines with async queues
7 GitHub stars
Performance optimization patterns to prevent event loop blocking
Concurrent execution using asyncio.gather and background task management
Rate limiting and synchronization using semaphores and locks

Use Cases

Optimizing database and I/O-bound operations for maximum throughput
Building high-concurrency web APIs with FastAPI, aiohttp, or Sanic
Developing efficient web scrapers and real-time WebSocket servers

About

Key Features

Advanced resource management with async context managers and iterators
Implementation of producer-consumer pipelines with async queues
7 GitHub stars
Performance optimization patterns to prevent event loop blocking
Concurrent execution using asyncio.gather and background task management
Rate limiting and synchronization using semaphores and locks

Use Cases

Optimizing database and I/O-bound operations for maximum throughput
Building high-concurrency web APIs with FastAPI, aiohttp, or Sanic
Developing efficient web scrapers and real-time WebSocket servers