Is this compatible with modern Python web frameworks?

Yes, the patterns are designed to work seamlessly with async-native frameworks like FastAPI, aiohttp, and Sanic.

Does this skill cover error handling in concurrent code?

Yes, it includes specific patterns for safe execution using asyncio.gather with exception handling to prevent single failures from crashing the entire system.

Can I use this for rate-limited API calls?

Absolutely. It provides patterns for using Semaphores to limit concurrency and ensure you stay within the rate limits of external services.

When should I use the Async Python Patterns skill?

Use this skill when building I/O-bound applications that require high concurrency, such as web servers, scrapers, or real-time data processing systems.

Async Python Patterns

Name: Async Python Patterns
Author: drgaciw

bydrgaciw

Desarrollo de API

Implements high-performance Python applications using asyncio, concurrent programming, and async/await patterns.

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This skill provides comprehensive guidance for mastering Python's asynchronous ecosystem, enabling developers to build non-blocking, high-concurrency systems. It covers fundamental and advanced patterns including event loops, coroutines, task management, error handling, and synchronization primitives like semaphores and locks. Ideal for optimizing I/O-bound workloads, such as web APIs, real-time scrapers, and microservices, it ensures best practices for writing clean, scalable, and efficient async code within the Claude Code environment.

Características Principales

Practical integration patterns for aiohttp and database operations
Concurrent task management using gather and background tasks
Comprehensive asyncio and async/await implementation patterns
Advanced synchronization using locks and semaphores for rate limiting
Async context managers and iterators for efficient resource handling
0 GitHub stars

Casos de Uso

Building high-throughput web APIs with frameworks like FastAPI or Sanic
Implementing producer-consumer queues for complex background processing
Developing concurrent web scrapers with built-in rate limiting

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Características Principales

Practical integration patterns for aiohttp and database operations
Concurrent task management using gather and background tasks
Comprehensive asyncio and async/await implementation patterns
Advanced synchronization using locks and semaphores for rate limiting
Async context managers and iterators for efficient resource handling
0 GitHub stars

Casos de Uso

Building high-throughput web APIs with frameworks like FastAPI or Sanic
Implementing producer-consumer queues for complex background processing
Developing concurrent web scrapers with built-in rate limiting