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.