Can it help me find why my async code is running slowly?

Yes, the skill includes patterns and scripts to identify blocking calls—such as time.sleep() or synchronous requests—that inadvertently freeze the event loop.

Does it work with web frameworks like FastAPI or Sanic?

Absolutely. While it focuses on core asyncio patterns, these are the fundamental building blocks for high-performance async web frameworks and microservices.

How does this skill handle rate limiting?

It provides implementation patterns for Bounded Concurrency using Semaphores, allowing you to limit the number of concurrent tasks and avoid hitting API rate limits.

Does this skill support the latest Python 3.11+ features?

Yes, it specifically includes patterns for modern Python features like TaskGroups and the native asyncio.timeout context manager for structured concurrency.

Python Async Patterns

Name: Python Async Patterns
Author: 0xDarkMatter

by0xDarkMatter

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API Development

Implements high-performance concurrent Python programming using asyncio best practices and structured concurrency patterns.

About

This skill equips Claude with specialized knowledge to architect, implement, and debug asynchronous Python applications. It provides domain-specific guidance on core asyncio concepts, including concurrent execution with gather, bounded concurrency using semaphores, and modern structured concurrency with Python 3.11+ TaskGroups. By following these implementation patterns, developers can ensure their code remains non-blocking, handles timeouts gracefully, and avoids common pitfalls like orphaned tasks or blocking I/O calls that degrade event loop performance.

Key Features

Automated identification and correction of blocking calls within async functions
Structured concurrency implementation using Python 3.11+ TaskGroups
4 GitHub stars
Bounded concurrency management via async semaphores to prevent resource exhaustion
Best practices for async context managers and producer-consumer queues
Robust error handling and timeout patterns for concurrent task execution

Use Cases

Refactoring synchronous, blocking code into efficient asynchronous workflows
Scaling backend services to handle thousands of simultaneous network connections
Building high-throughput web crawlers or API clients using aiohttp

About

Key Features

Automated identification and correction of blocking calls within async functions
Structured concurrency implementation using Python 3.11+ TaskGroups
4 GitHub stars
Bounded concurrency management via async semaphores to prevent resource exhaustion
Best practices for async context managers and producer-consumer queues
Robust error handling and timeout patterns for concurrent task execution

Use Cases

Refactoring synchronous, blocking code into efficient asynchronous workflows
Scaling backend services to handle thousands of simultaneous network connections
Building high-throughput web crawlers or API clients using aiohttp