Writes type-safe, async-first Python 3.11+ code with pytest coverage and mypy validation.
npx skills add https://github.com/jvsandhu/agentic-skills --skill python-pro-jvsandhu
Please help me install this Agent Skill. Skill: python_pro Source: https://github.com/jvsandhu/agentic-skills/tree/main/skills/python_pro Command: npx skills add https://github.com/jvsandhu/agentic-skills --skill python-pro-jvsandhu
X | None unions, dataclasses, Protocols, and generics that passes mypy --strict. - Async & Testing Patterns: Implements async/await with asyncio or anyio for I/O-bound work, plus pytest suites with fixtures, mocking, and parametrization targeting over 90% coverage. - Modern Tooling Workflow: Standardizes on uv for package management, Ruff for linting and formatting, and pyproject.toml for unified configuration. - Use Case: When building a new data pipeline service, invoke this Skill to scaffold typed modules, async fetchers, structured logging with structlog, and a complete pytest suite that passes Ruff and mypy checks. ## Quick Start Ask the agent to write a type-safe async Python module with pytest tests that passes mypy strict mode and Ruff checks.High-intent search queries and answers about installing and using this skill.
Annotate all function signatures and class attributes, use `X | None` instead of Optional, and prefer Protocols and generics for abstractions. Run `mypy --strict` after implementation and resolve every reported error rather than ignoring them.
Use async/await for all I/O-bound operations and avoid mixing sync and async calls improperly. Task groups in Python 3.11+ or anyio provide structured concurrency for managing concurrent coroutines safely.
Ruff is a single Rust-based tool that replaces Flake8, isort, and Black, handling both linting and formatting via `ruff check` and `ruff format`. It is significantly faster and consolidates configuration into pyproject.toml.
Yes, pytest provides built-in fixtures for setup and teardown, and pytest-mock wraps unittest.mock for patching dependencies. Combined with parametrize, these tools help achieve coverage above 90 percent.
Use dataclasses whenever a class primarily stores data, since they auto-generate __init__, __repr__, and equality methods. Avoid mutable default arguments and prefer field(default_factory=...) for mutable defaults.
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