Does it cover error handling in concurrent tasks?

Absolutely. The skill provides patterns for safely managing exceptions across multiple concurrent tasks using asyncio.gather and structured try-except blocks.

How does this skill help with Python performance?

It provides optimized patterns for asyncio, allowing your code to handle thousands of concurrent I/O operations efficiently without the overhead of traditional multi-threading.

Is this skill compatible with modern Python web frameworks?

Yes, these patterns are foundational for high-performance frameworks like FastAPI, Sanic, and aiohttp, making it ideal for modern API development.

Can I use this for web scraping projects?

Yes, it includes specific patterns for concurrent web scraping using aiohttp and provides semaphore implementations for managing rate limits.

Async Python Patterns

Name: Async Python Patterns
Author: HermeticOrmus

byHermeticOrmus

API Development

Implements high-performance asynchronous workflows using Python asyncio, concurrent programming patterns, and non-blocking I/O operations.

About

This skill provides comprehensive guidance for mastering Python's asynchronous ecosystem, enabling developers to build highly scalable and efficient applications. It covers everything from basic async/await syntax to advanced concurrency patterns like producer-consumer queues, semaphores for rate limiting, and safe error handling in concurrent environments. By implementing these battle-tested patterns, developers can optimize I/O-bound workloads, such as web scraping, API communication, and database operations, ensuring maximum throughput and minimal resource latency.

Key Features

0 GitHub stars
Robust error handling and synchronization patterns for multi-task environments
Implementation of core asyncio concepts including event loops, coroutines, and tasks
Real-world integration patterns for aiohttp scraping and async database operations
Advanced concurrency management using gather, wait_for, and semaphores
Asynchronous resource handling through async context managers and iterators

Use Cases

Building efficient web scrapers that process hundreds of requests simultaneously without blocking
Optimizing I/O-bound microservices requiring non-blocking communication and background task processing
Scaling backend APIs with FastAPI or aiohttp for high-concurrency environments

About

Key Features

0 GitHub stars
Robust error handling and synchronization patterns for multi-task environments
Implementation of core asyncio concepts including event loops, coroutines, and tasks
Real-world integration patterns for aiohttp scraping and async database operations
Advanced concurrency management using gather, wait_for, and semaphores
Asynchronous resource handling through async context managers and iterators

Use Cases

Building efficient web scrapers that process hundreds of requests simultaneously without blocking
Optimizing I/O-bound microservices requiring non-blocking communication and background task processing
Scaling backend APIs with FastAPI or aiohttp for high-concurrency environments