python-packaging
Create distributable Python packages with appropriate project structure and setup.py or pyproject.toml configuration. Covers publishing to PyPI and distributing libraries or command-line tools.
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Find your next superpower If you are an agent, refer to our llms.txt for full access.Create distributable Python packages with appropriate project structure and setup.py or pyproject.toml configuration. Covers publishing to PyPI and distributing libraries or command-line tools.
Explains Python development principles and decision-making. Covers framework selection, asynchronous patterns, type hints, and project structure, emphasizing reasoning over copying.
Patrones idiomáticos de Python, estándares PEP 8, type hints y buenas prácticas para construir aplicaciones Python robustas, eficientes y mantenibles.
Guides Python development decisions across framework selection, asynchronous programming, type hints, and project organization. Emphasizes reasoning about development choices.
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.
Use cProfile, memory profilers, and performance practices to investigate slow Python code. Identify bottlenecks and improve application performance.
Generates Python 3.11+ code using type hints, dataclasses, dependency injection, and async/await. Configures logging and error handling, creates pytest fixtures and mocks, and validates code with mypy, black, and ruff.
Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in the latest Python ecosystem including uv, ruff, pydantic, and FastAPI. Use PROACTIVELY for Python development, optimization, or advanced Python patterns.
Plan Python project structure and module architecture. Covers new project setup, directory layouts, public API design, and interface definitions with __all__.
Handle transient failures using automatic retries, exponential backoff, and timeouts. Covers fault-tolerant decorators and resilience patterns for Python services.
Guides resource lifecycle handling for connections and file handles. Covers context managers, cleanup logic, and streaming responses that maintain accumulated state.
Handle connections and file handles using context managers and cleanup patterns. Covers streaming responses that maintain accumulated state.
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