Can it help with API rate limiting?

Absolutely. The skill includes specific patterns for using Semaphores to manage concurrency limits, ensuring your application doesn't overwhelm external services.

When should I use this skill?

Use it when building I/O-bound applications, such as web APIs, database-intensive scripts, or web scrapers that require concurrent execution to improve performance.

Does it support specific frameworks like FastAPI?

Yes, the patterns provided are compatible with all major modern Python async frameworks including FastAPI, aiohttp, Sanic, and Tortoise-ORM.

What is the Async Python Patterns skill?

It is a specialized capability for Claude Code that provides implementation patterns, best practices, and code snippets for Python's asynchronous programming features like asyncio and async/await.

What is the benefit of using async/await over multithreading?

Asyncio is more memory-efficient for I/O-bound tasks because it uses a single-threaded event loop to manage thousands of concurrent connections without the overhead of thread context switching.

Async Python Patterns

Name: Async Python Patterns
Author: HermeticOrmus

byHermeticOrmus

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API 개발

Implements high-performance asynchronous patterns and concurrent programming techniques using Python's asyncio framework.

소개

This skill empowers developers to master Python’s asynchronous ecosystem by providing expert guidance on asyncio, concurrent task management, and non-blocking I/O operations. It bridges the gap between basic syntax and complex real-world architectures like producer-consumer models, rate-limited API clients, and thread-safe shared state. Whether you are building scalable FastAPI backends, high-throughput web scrapers, or real-time WebSocket servers, this skill ensures your implementation follows industry best practices for performance and stability.

주요 기능

Performance optimization strategies for I/O-bound workloads and database connections
Guidance on async-native structures including context managers, iterators, and generators
2 GitHub stars
Advanced asyncio task orchestration using gather, wait_for, and create_task
Standardized error handling and timeout management for resilient async operations
Implementation of complex concurrency patterns like Producer-Consumer and Semaphores

사용 사례

Developing high-concurrency web APIs with frameworks like FastAPI or Sanic
Managing real-time data streams and asynchronous background tasks in microservices
Building efficient web scrapers and data crawlers with aiohttp for parallel processing

소개

주요 기능

Performance optimization strategies for I/O-bound workloads and database connections
Guidance on async-native structures including context managers, iterators, and generators
2 GitHub stars
Advanced asyncio task orchestration using gather, wait_for, and create_task
Standardized error handling and timeout management for resilient async operations
Implementation of complex concurrency patterns like Producer-Consumer and Semaphores

사용 사례

Developing high-concurrency web APIs with frameworks like FastAPI or Sanic
Managing real-time data streams and asynchronous background tasks in microservices
Building efficient web scrapers and data crawlers with aiohttp for parallel processing