Does it support common async libraries like aiohttp or asyncpg?

Yes, the skill is designed to work seamlessly with the broader async ecosystem, including aiohttp for HTTP, httpx for mixed environments, and asyncpg for database performance.

How does it handle performance-critical applications?

The skill includes specialized configurations for uvloop to increase event loop speed and provides guidance on free-threaded asyncio for Python 3.14+ builds.

Does this skill support the latest Python 3.11+ and 3.12+ features?

Yes, it provides comprehensive implementation patterns for modern features like TaskGroup, the timeout context manager, and the eager task factory.

Can this skill help optimize my existing synchronous Python code?

Absolutely. It assists in identifying I/O-bound bottlenecks and provides structured paths for migrating synchronous code to efficient async patterns.

Python Asyncio Master

Name: Python Asyncio Master
Author: JosiahSiegel

byJosiahSiegel

•

API Development

Masters Python's asynchronous programming system to build high-performance, non-blocking concurrent applications.

About

This skill empowers Claude to architect and implement complex Python asynchronous workflows using the asyncio framework. It provides expert guidance on modern concurrent patterns, structured concurrency with TaskGroups, and rate limiting via semaphores. By leveraging this skill, developers can efficiently manage network requests, database queries, and file I/O operations while avoiding common pitfalls like event loop blocking. It includes specialized support for high-performance libraries such as aiohttp and uvloop, ensuring that your applications are scalable and production-ready.

Key Features

Identifies and resolves event loop blocking issues by offloading CPU-bound tasks to executors.
Architects robust producer-consumer patterns using asynchronous Queues and generators.
7 GitHub stars
Implements structured concurrency using Python 3.11+ TaskGroups and modern timeout syntax.
Optimizes concurrent execution with asyncio.gather and Semaphore-based rate limiting.
Enables high-performance event loop configurations using uvloop for 2-4x performance gains.

Use Cases

Scaling backend services to handle thousands of concurrent API requests and database connections.
Developing real-time data processing pipelines using async generators and context managers.
Building high-speed web scrapers and network crawlers that perform multiple requests in parallel.

About

Key Features

Identifies and resolves event loop blocking issues by offloading CPU-bound tasks to executors.
Architects robust producer-consumer patterns using asynchronous Queues and generators.
7 GitHub stars
Implements structured concurrency using Python 3.11+ TaskGroups and modern timeout syntax.
Optimizes concurrent execution with asyncio.gather and Semaphore-based rate limiting.
Enables high-performance event loop configurations using uvloop for 2-4x performance gains.

Use Cases

Scaling backend services to handle thousands of concurrent API requests and database connections.
Developing real-time data processing pipelines using async generators and context managers.
Building high-speed web scrapers and network crawlers that perform multiple requests in parallel.