Does it support specific frameworks like FastAPI?

Yes, the patterns provided are foundational for modern async frameworks including FastAPI, aiohttp, Sanic, and Tortoise ORM.

Can it help with error handling in async code?

Absolutely. It provides specific patterns for managing exceptions within gather() calls and implementing robust timeout logic to prevent hanging tasks.

What is the Async Python Patterns skill?

It is a specialized capability for Claude Code that provides expert guidance and code patterns for implementing asynchronous programming in Python using the asyncio library.

How does it handle concurrency limits?

It includes specific patterns for rate limiting and resource management using semaphores and locks to prevent overloading external services or system resources.

When should I use this skill?

Use this skill when building I/O-bound applications such as web APIs, concurrent web scrapers, real-time messaging systems, or any service requiring non-blocking operations.

Async Python Patterns

Name: Async Python Patterns
Author: HermeticOrmus

byHermeticOrmus

API Development

Implements high-performance asynchronous programming patterns in Python using asyncio and concurrent execution strategies.

About

This skill provides a comprehensive framework for building non-blocking Python applications, offering production-ready patterns for asyncio, coroutines, and concurrent task management. It guides developers through complex scenarios like rate limiting with semaphores, implementing producer-consumer queues, and managing asynchronous context managers. Whether you are building high-throughput APIs with FastAPI or scalable web scrapers with aiohttp, this skill ensures implementation of best practices for I/O-bound workloads and system optimization.

Key Features

Resource management via async context managers and iterators
Standardized producer-consumer architecture for background task processing
Advanced asyncio pattern implementation for concurrent execution
Synchronization primitives including locks and semaphores for rate limiting
0 GitHub stars
Safe error and timeout handling in asynchronous workflows

Use Cases

Scaling Python web APIs and microservices using FastAPI or aiohttp
Developing high-concurrency web scrapers and data collection systems
Optimizing database and network I/O-bound workloads for performance

About

Key Features

Resource management via async context managers and iterators
Standardized producer-consumer architecture for background task processing
Advanced asyncio pattern implementation for concurrent execution
Synchronization primitives including locks and semaphores for rate limiting
0 GitHub stars
Safe error and timeout handling in asynchronous workflows

Use Cases

Scaling Python web APIs and microservices using FastAPI or aiohttp
Developing high-concurrency web scrapers and data collection systems
Optimizing database and network I/O-bound workloads for performance