async-python-patterns

# Async Python Patterns Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems. ## Use this skill when - Building async web APIs (FastAPI, aiohttp, Sanic) - Implementing concurrent I/O operations (database, file, network) - Creating web scrapers with concurrent requests - Developing real-time applications (WebSocket servers, chat systems) - Processing multiple independent tasks simultaneously - Building microservices with async communication - Optimizing I/O-bound workloads - Implementing async background tasks and queues ## Do not use this skill when - The workload is CPU-bound with minimal I/O. - A simple synchronous script is sufficient. - The runtime environment cannot support asyncio/event loop usage. ## Instructions - Clarify workload characteristics (I/O vs CPU), targets, and runtime constraints. - Pick concurrency patterns (tasks, gather, queues, pools) with cancellation rules. - Add timeouts, backpressure, and structured error handling. - Include testing and debugging guidance for async code paths. - If detailed examples are required, open `resources/implementation-playbook.md`. Refer to `resources/implementation-playbook.md` for detailed patterns and examples. ## Resources - `resources/implementation-playbook.md` for detailed patterns and examples. ## When to Use Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or I/O-bound applications requiring non-blocking operations.