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Build Python applications that keep other tasks moving while waiting for network, file, or database operations. The skill guides asyncio patterns, task cancellation, timeouts, workload limits, and testing for asynchronous APIs, scrapers, and background processing.
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Build Python applications that keep other tasks moving while waiting for network, database, or file operations. Guides the choice of asyncio patterns, with timeouts, cancellation, error handling, and testing for asynchronous code.
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Build Python applications that handle other work while waiting for network or database operations. Use asyncio and async/await to run tasks concurrently, manage errors and timeouts, and handle cancellation with appropriate cleanup.
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Answer Python questions and write code using examples fetched from pythonsheets.com. Find patterns for syntax, asynchronous tasks, networking, databases, machine learning, and distributed computing, or use topic-based questions to prepare for Python interviews.
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Use when writing asyncio Python code — async/await coroutines, concurrent I/O with asyncio.gather, task creation and cancellation, semaphore rate limiting, producer-consumer queues, async context managers, async generators, WebSocket servers, aiohttp web scraping, async database operations, run_in_executor for blocking calls, or testing async code with pytest-asyncio. Covers FastAPI and aiohttp patterns, synchronization primitives, timeout handling, and common pitfalls like event loop blocking and missing await.
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Find Python input/output calls that may stall asynchronous backend work, then fix or guard each relevant path with runtime tests. Verify that tests fail when blocking behavior returns and pass after the fix is restored.
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Build Python integrations with crypto exchanges using CCXT. Get guidance on fetching prices and order books, placing orders, checking balances, and receiving live updates, with examples for authentication, error handling, and synchronous or asynchronous code.
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Set up Python APIs with FastAPI using organized project templates. Build routes, asynchronous database operations, authentication, and error handling, with examples for sharing dependencies and testing endpoints through a separate test database.
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Python asyncio patterns for concurrent programming. Triggers on: asyncio, async, await, coroutine, gather, semaphore, TaskGroup, event loop, aiohttp, concurrent.
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Async Python patterns via asyncio and aiohttp for I/O-bound concurrency. Use when adding async APIs, handling concurrent I/O, or debugging async code.
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Use when working with Redis - in-memory database, caching, pub/sub, sessions, rate limiting, RESP3, asyncio, Redis Stack
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Raise real concurrency in asyncio LLM batch scorers built on the OpenAI SDK (AsyncOpenAI, including OpenAI-compatible providers like DeepSeek). Use when: (1) raising an asyncio.Semaphore above ~100 produces no throughput gain, (2) a batch pipeline saturates near 100 in-flight requests despite a larger semaphore, (3) planning a high-concurrency campaign against a provider with no hard rate limit (DeepSeek v4-flash tolerates 2000+ in flight). Root cause: AsyncOpenAI's default httpx pool caps max_connections at 100, silently bottlenecking any larger semaphore — you must pass a custom http_client with httpx.Limits sized to the semaphore.
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Python asyncio、並行プログラミング、高性能アプリケーション向けの非同期/awaitパターンをマスター。非同期API、並行システム、ノンブロッキング操作が必要なI/OバウンドアプリケーションをPythonで構築する際に使用。
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使用pytest设计和编写Python测试，复用准备与清理步骤，模拟外部服务，以多组输入验证行为，并测量代码覆盖率、找出尚未验证的部分。
通过Exa搜索最新网络信息、代码示例及企业和人物资料，区分一般调研与API用法查询，帮助找到符合任务目的的参考材料。
Helps write and improve Python code with type hints, tests, and organized package settings. Covers dependency management, asynchronous tasks that wait on external operations, and profiling to identify performance bottlenecks before making changes.
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Transform sequential Python code into parallel/concurrent implementations. Use when asked to parallelize Python code, improve code performance through concurrency, convert loops to parallel execution, or identify parallelization opportunities. Handles CPU-bound (multiprocessing), I/O-bound (asyncio, threading), and data-parallel (vectorization) scenarios.
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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.
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Python 开发规范。当用户操作 .py、pyproject.toml、requirements.txt、setup.py 文件， 或涉及 FastAPI、Django、Flask、pytest、asyncio 开发时触发。 包含 PEP 8 风格、类型注解、异常处理、测试规范、异步编程、性能优化等。
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Write correct async Python runtime code with explicit blocking-I/O boundaries, cancellation, and timeout handling. Use when editing `asyncio` workflows, background loops, async services, or mixed sync/async integrations.
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Decision protocol for the map-reduce / dynamic fan-out pattern in LM pipelines — "given list L, run f(item) for each item in parallel, then combine". Activates when the coder agent is about to process N items with N LM calls (per-doc summarize, per-query retrieve, per-candidate rank, parallel tool fan-out). Encodes the *when*, *how many at once*, *what to do when one fails*, and *how to reduce* — not the API of any single framework. Cross-framework: LangGraph `Send`, CrewAI parallel tasks / Flow, `asyncio.gather`, `ThreadPoolExecutor`, LlamaIndex batch retrieval.
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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.
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Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.
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Async Python Patterns workflow skill. Use this skill when the user needs Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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