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python-development skill

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This skill helps you build modern Python projects with FastAPI, asyncio, and type hints, enforcing production-grade structure and best practices.

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---
name: python-development
description: Modern Python development with Python 3.12+, Django, FastAPI, async patterns, and production best practices. Use for Python projects, APIs, data processing, or automation scripts.
source: wshobson/agents
license: MIT
---

# Python Development

## Project Setup

### Modern Python Project Structure
```
my-project/
├── src/
│   └── my_project/
│       ├── __init__.py
│       ├── main.py
│       └── utils.py
├── tests/
│   ├── __init__.py
│   └── test_main.py
├── pyproject.toml
├── README.md
└── .gitignore
```

### pyproject.toml
```toml
[project]
name = "my-project"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = [
    "fastapi>=0.100.0",
    "pydantic>=2.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=7.0",
    "ruff>=0.1.0",
    "mypy>=1.0",
]

[tool.ruff]
line-length = 88
select = ["E", "F", "I", "N", "W"]

[tool.mypy]
strict = true
```

## Type Hints

```python
from typing import TypeVar, Generic
from collections.abc import Sequence

T = TypeVar('T')

def process_items(items: Sequence[str]) -> list[str]:
    return [item.upper() for item in items]

class Repository(Generic[T]):
    def get(self, id: int) -> T | None: ...
    def save(self, item: T) -> T: ...
```

## Async Patterns

```python
import asyncio
from collections.abc import AsyncIterator

async def fetch_all(urls: list[str]) -> list[dict]:
    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_file.txt', 'rb') as f:
        async for chunk in f:
            yield chunk
```

## FastAPI Patterns

```python
from fastapi import FastAPI, Depends, HTTPException
from pydantic import BaseModel

app = FastAPI()

class UserCreate(BaseModel):
    email: str
    name: str

class UserResponse(BaseModel):
    id: int
    email: str
    name: str

@app.post("/users", response_model=UserResponse)
async def create_user(
    user: UserCreate,
    db: Database = Depends(get_db)
) -> UserResponse:
    result = await db.users.create(user.model_dump())
    return UserResponse(**result)
```

## Testing

```python
import pytest
from unittest.mock import AsyncMock, patch

@pytest.fixture
def mock_db():
    db = AsyncMock()
    db.users.get.return_value = {"id": 1, "name": "Test"}
    return db

@pytest.mark.asyncio
async def test_get_user(mock_db):
    result = await get_user(1, db=mock_db)
    assert result["name"] == "Test"
    mock_db.users.get.assert_called_once_with(1)
```

## Best Practices

- Use `ruff` for linting and formatting
- Use `mypy` with strict mode
- Prefer `pathlib.Path` over `os.path`
- Use dataclasses or Pydantic for data structures
- Use `asyncio` for I/O-bound operations
- Use `contextlib.asynccontextmanager` for async resources

Overview

This skill teaches modern Python development for Python 3.12+ with practical patterns for Django/FastAPI services, async code, and production-ready projects. It focuses on typing, project layout, testing, and tooling that improve reliability and developer productivity. Use it to build APIs, data pipelines, or automation with clear, maintainable code.

How this skill works

It inspects and applies idiomatic project structure, pyproject.toml configuration, and recommended linters/type checkers (ruff, mypy). It demonstrates typed code patterns, async primitives, FastAPI endpoints, and testing approaches using pytest and AsyncMock. It highlights production best practices like using pathlib, Pydantic/dataclasses, and async context management for resources.

When to use it

  • Starting a new Python 3.12+ project (library, service, CLI).
  • Building HTTP APIs with FastAPI or integrating async I/O.
  • Implementing strict static typing and runtime validation with Pydantic.
  • Setting up CI with linting, formatting, and type checks.
  • Writing async tests and mocking async dependencies.

Best practices

  • Use a src/ layout and declare requires-python >=3.12 in pyproject.toml.
  • Enforce linting and formatting with ruff and set mypy strict mode for types.
  • Prefer pathlib.Path for file paths and contextlib.asynccontextmanager for async resources.
  • Model external data with Pydantic or dataclasses and keep validation close to boundaries.
  • Use asyncio.gather for concurrent I/O tasks and stream large files with async iterators.

Example use cases

  • Create a FastAPI user service with Pydantic request/response models and dependency-injected DB access.
  • Implement an async data ingestion pipeline that fetches many URLs concurrently and streams large files to storage.
  • Set up a library with strict type coverage using mypy and ruff, plus unit and async tests with pytest and AsyncMock.
  • Build automation scripts that use pathlib and type hints for predictable, testable behavior.

FAQ

What Python version should I target?

Target Python 3.12 or later to use new typing features and language improvements described here.

When should I use Pydantic vs dataclasses?

Use Pydantic for runtime validation and parsing of external input (API requests). Use dataclasses for simple internal data structures where validation is not required.

How do I test async code?

Use pytest with asyncio support and AsyncMock to mock async dependencies. Mark async tests with pytest.mark.asyncio and assert calls with awaitable mocks.