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Found 2,035 Skills
Guidelines for Flask Python development with best practices for blueprints, RESTful APIs, and application factories.
Create and manipulate PowerPoint presentations programmatically. Build slide decks with layouts, shapes, charts, tables, and images. Generate data-driven presentations from templates.
Python resilience patterns including automatic retries, exponential backoff, timeouts, and fault-tolerant decorators. Use when adding retry logic, implementing timeouts, building fault-tolerant services, or handling transient failures.
Expert in Python testing with pytest and test-driven development
Modern Python development with uv, ruff, mypy, and pytest. Use when: - Writing or reviewing Python code - Setting up Python projects or pyproject.toml - Choosing dependency management (uv, poetry, pip) - Configuring linting, formatting, or type checking - Organizing Python packages Keywords: Python, pyproject.toml, uv, ruff, mypy, pytest, type hints, virtual environment, lockfile, package structure
This skill should be used when the user asks to "configure ruff", "set up ruff linting", "use ruff formatter", "replace flake8 with ruff", or needs guidance on Python code quality with Ruff linting and formatting best practices.
Python software engineering guidelines from real PR review patterns. This skill should be used when writing, reviewing, or refactoring Python code — especially dataclasses, service interfaces, error handling, and type annotations. Triggers on tasks involving Python modules, API design, data modeling, type safety, exception handling, or refactoring for maintainability.
Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.
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.
Generate a complete MCP server project in Python with tools, resources, and proper configuration
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.