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Found 25 Skills
Python asyncio - Modern concurrent programming with async/await, event loops, tasks, coroutines, primitives, aiohttp, and FastAPI async patterns
Complete guide for asyncio concurrency patterns including event loops, coroutines, tasks, futures, async context managers, and performance optimization
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.
Concurrent operations with asyncio and Tokio, focusing on race condition prevention, resource safety, and performance
Python 3.11+ performance optimization guidelines (formerly python-311). This skill should be used when writing, reviewing, or refactoring Python code to ensure optimal performance patterns. Triggers on tasks involving asyncio, data structures, memory management, concurrency, loops, strings, or Python idioms.
Python backend patterns for asyncio, FastAPI, SQLAlchemy 2.0 async, and connection pooling. Use when building async Python services, FastAPI endpoints, database sessions, or connection pool tuning.
Sets up async tests with proper fixtures and mocks using pytest-asyncio patterns. Use when testing async functions, creating async fixtures, mocking async services, or handling async context managers. Covers @pytest_asyncio.fixture, AsyncMock with side_effect, async generator fixtures (yield), and testing async context managers. Works with Python async/await patterns, pytest-asyncio, and unittest.mock.AsyncMock.
Python asyncio patterns for concurrent programming. Triggers on: asyncio, async, await, coroutine, gather, semaphore, TaskGroup, event loop, aiohttp, concurrent.
Use when writing or reviewing asyncio code in Jupyter notebooks or '#%%' cell workflows — structuring event-loop ownership, orchestrating async tasks, or choosing compatibility strategies. Also use when hitting RuntimeError: This event loop is already running, asyncio.run() failures in cells, or tasks silently never completing.
Use when designing or reviewing concurrent Python code — selecting between asyncio, threads, or multiprocessing; structuring cancellation and deadline propagation; bounding fan-out and backpressure. Also use when diagnosing race conditions, deadlocks, slow throughput, or thread/task leaks under load.
Provides Python async/await patterns and asyncio best practices. Activated when the user asks about async/await patterns, asyncio best practices, concurrent tasks, async generators, task groups, async context managers, event loops, running blocking code in async, or async testing. Covers asyncio, concurrency, async iterators, semaphores, and asynchronous programming patterns in Python.
Python error handling patterns for FastAPI, Pydantic, and asyncio. Follows "Let it crash" philosophy - raise exceptions, catch at boundaries. Covers HTTPException, global exception handlers, validation errors, background task failures. Use when: (1) Designing API error responses, (2) Handling RequestValidationError, (3) Managing async exceptions, (4) Preventing stack trace leakage, (5) Designing custom exception hierarchies.