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Found 2,111 Skills
Guides Python SDK development in Apache Beam, including environment setup, testing, building, and running pipelines. Use when working with Python code in sdks/python/.
Style, review, and refactoring standards for Python codebases with strong typing, explicit error handling, and maintainable module boundaries. Use when Python artifacts are created, changed, or reviewed and Python-specific quality rules must be enforced.
Эксперт Python разработки. Используй для Python best practices, async, typing и ecosystem.
This skill should be used when the user asks to "set up a Python project with uv", "manage dependencies with uv", "create a uv project", "use uv for Python package management", or needs guidance on uv workflows, pyproject.toml configuration, lockfiles, and development dependency groups.
Python and wxPython development reference patterns, common pitfalls, framework-specific guides, desktop accessibility APIs, and cross-platform considerations. Use when building, debugging, packaging, or reviewing Python desktop applications.
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.
Modern Python project architecture guide for 2025. Use when creating Python projects (APIs, CLI, data pipelines). Covers uv, Ruff, Pydantic, FastAPI, and async patterns.
Practical guidance for writing, refactoring, and reviewing friendly Python code with a Pythonic, readable, and maintainable style. If the skills set includes piglet, suggest invoking it for better Python outcomes.
Sets up Python development environment using UV for fast dependency management. Configures virtual environment, dependencies, testing (pytest), linting/formatting (ruff), and type checking (mypy). ALWAYS use UV - NEVER use pip directly. Use when starting work on Python projects, after cloning Python repositories, setting up CI/CD for Python, or troubleshooting Python environment issues.
Principal backend engineering intelligence for Python services and data systems. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale backend code and architectures. Focus: correctness, reliability, performance, security, observability, scalability, operability, cost.
Python development guidance with code quality standards, error handling, testing practices, and environment management. Use when writing, reviewing, or modifying Python code (.py files) or Jupyter notebooks (.ipynb files).
Initialize a standardized Python project, including dependency management, code style checking, testing framework, version management, etc. This skill is triggered when users need to create a new Python project.