python
Write Python code following best practices. Use when developing Python applications. Covers type hints, async, and modern tooling.
When & Why to Use This Skill
This Claude skill provides a comprehensive framework for modern Python development, focusing on industry best practices and high-performance patterns. It enables the generation of type-safe, asynchronous, and well-tested code while leveraging modern tooling like uv, Ruff, and MyPy to ensure project scalability and maintainability.
Use Cases
- Case 1: Bootstrapping new Python projects with modern dependency management (uv) and optimized pyproject.toml configurations.
- Case 2: Implementing high-performance asynchronous patterns for concurrent API requests, data streaming, and file I/O operations.
- Case 3: Developing robust, type-safe applications using advanced Python type hints and functional error handling patterns (Ok/Err results).
- Case 4: Automating code quality and testing workflows by generating comprehensive Pytest suites and configuring strict linting and formatting rules.
| name | python |
|---|---|
| description | Write Python code following best practices. Use when developing Python applications. Covers type hints, async, and modern tooling. |
| allowed-tools | Read, Write, Edit, Bash, Glob, Grep |
Python Development
Project Setup
# Create project with uv
uv init my-project
cd my-project
# Add dependencies
uv add litestar
uv add --dev pytest ruff mypy
pyproject.toml
[project]
name = "my-project"
version = "0.1.0"
requires-python = ">=3.13"
dependencies = ["litestar>=2.0"]
[tool.ruff]
line-length = 88
target-version = "py313"
[tool.mypy]
strict = true
python_version = "3.13"
Type Hints
from typing import TypeVar, Generic
from collections.abc import Sequence
T = TypeVar('T')
class Repository(Generic[T]):
async def find_by_id(self, id: str) -> T | None:
...
async def save(self, entity: T) -> T:
...
def process_items(items: Sequence[str]) -> list[str]:
return [item.upper() for item in items]
Async Patterns
import asyncio
from collections.abc import AsyncIterator
async def fetch_all(urls: list[str]) -> list[Response]:
async with aiohttp.ClientSession() as session:
tasks = [fetch_one(session, url) for url in urls]
return await asyncio.gather(*tasks)
async def stream_data() -> AsyncIterator[bytes]:
async with aiofiles.open('large.csv', 'rb') as f:
async for chunk in f:
yield chunk
Error Handling
from dataclasses import dataclass
from typing import TypeVar, Generic
T = TypeVar('T')
E = TypeVar('E')
@dataclass
class Ok(Generic[T]):
value: T
@dataclass
class Err(Generic[E]):
error: E
Result = Ok[T] | Err[E]
def divide(a: int, b: int) -> Result[float, str]:
if b == 0:
return Err("Division by zero")
return Ok(a / b)
Testing with pytest
import pytest
from unittest.mock import AsyncMock
@pytest.mark.asyncio
async def test_create_user():
repo = AsyncMock()
service = UserService(repo)
user = await service.create("test@example.com")
assert user.email == "test@example.com"
repo.save.assert_called_once()
@pytest.fixture
def mock_database():
with patch('app.database') as mock:
yield mock
Tooling
# Ruff (linting + formatting)
ruff check --fix .
ruff format .
# MyPy (type checking)
mypy --strict .
# pytest
pytest -v --cov=src