Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.

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. Limitations Use this skill only when the task clearly matches the scope described above. Do not treat the output as a substitute for environment specific validation, testing, or expert review. Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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