Loading...
Loading...
Found 2,039 Skills
Guides the agent through running and configuring ASGI servers (Uvicorn, Granian, Hypercorn) for Python web applications. Triggered when users say "run a FastAPI app", "configure uvicorn", "set up ASGI server", "deploy with uvicorn", "configure workers", "set up SSL/TLS", "run development server", "configure hot reload", or mention ASGI server, production deployment, server configuration, uvicorn, granian, or hypercorn.
pytest testing patterns for Python. Triggers on: pytest, fixture, mark, parametrize, mock, conftest, test coverage, unit test, integration test, pytest.raises.
Modern Python project architecture guide for 2025. Use when creating Python projects (APIs, CLI, data pipelines). Covers uv, Ruff, Pydantic, FastAPI, and async patterns.
Python Coding Standards, including type hints, logging specifications, naming conventions, code structure, etc. Applicable to all Python code files.
Modern Python development with uv (10-100x faster package manager) and ruff (extremely fast linter/formatter). Use when managing Python projects, dependencies, virtual environments, installing packages, linting code, or formatting Python files. Triggers on phrases like "uv install", "ruff check", "python package manager", "format python code", or working with pyproject.toml files.
Production Python engineering patterns covering architecture, observability, testing, performance/concurrency, and core practices. Use when designing Python systems, implementing async/sync APIs, setting up monitoring, structuring tests, optimizing performance, or following Python best practices.
Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.
Validate Python code quality with formatting, type checking, linting, and security analysis. Use for Python codebases to ensure PEP 8 compliance, type safety, and code quality.
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).
Python environment management with venv, Poetry, Pipenv, pyenv, and conda. Use when user asks to "create virtual environment", "set up Poetry", "manage Python versions", "fix pip issues", "install dependencies", "create requirements.txt", or any Python environment tasks.
Identify CPU and memory bottlenecks in Python code using cProfile or memory_profiler. Use to optimize mission-critical Python services.
Python testing with pytest, coverage, fixtures, parametrization, and mocking. Covers test organization, conftest.py, markers, async testing, and TDD workflows. Use when user mentions pytest, unit tests, test coverage, fixtures, mocking, or writing Python tests.