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.mdfor detailed patterns and examples.
When to Use
- •Use this skill when you need for functional programming or specific domain tasks.