MCPInstall

python-best-practices

Pythonic code with modern type hints, dataclasses, async patterns, packaging, and testing

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/rohitg00/awesome-claude-code-toolkit --skill python-best-practices
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads python-best-practices automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Python Best Practices

Type Hints (3.12+ Syntax)

# Use built-in generics (3.9+), no need for typing.List, typing.Dict
def process_items(items: list[str]) -> dict[str, int]:
    return {item: len(item) for item in items}

# Union with | syntax (3.10+)
def find_user(user_id: int) -> User | None:
    ...

# Type parameter syntax (3.12+)
type Vector[T] = list[T]
type Matrix[T] = list[Vector[T]]

def first[T](items: list[T]) -> T:
    return items[0]

# TypedDict for structured dicts
from typing import TypedDict

class UserResponse(TypedDict):
    id: int
    name: str
    email: str
    active: bool

Always type function signatures. Use mypy --strict or pyright in CI. Use type: ignore comments sparingly with justification.

Dataclasses vs Pydantic

Dataclasses (internal data, no validation needed)

from dataclasses import dataclass, field

@dataclass(frozen=True, slots=True)
class Point:
    x: float
    y: float

    def distance_to(self, other: "Point") -> float:
        return ((self.x - other.x) ** 2 + (self.y - other.y) ** 2) ** 0.5

@dataclass
class Config:
    host: str = "localhost"
    port: int = 8080
    tags: list[str] = field(default_factory=list)

Use frozen=True for immutable value objects. Use slots=True for memory efficiency.

Pydantic (external input, validation required)

from pydantic import BaseModel, Field, field_validator

class CreateUserRequest(BaseModel):
    model_config = {"strict": True}

    email: str = Field(max_length=255)
    name: str = Field(min_length=1, max_length=100)
    age: int = Field(ge=13, le=150)

    @field_validator("email")
    @classmethod
    def validate_email(cls, v: str) -> str:
        if "@" not in v:
            raise ValueError("Invalid email format")
        return v.lower()

Rule: Use dataclasses for domain models and internal structs. Use Pydantic for API boundaries, config files, and external data parsing.

Async Patterns

import asyncio
import httpx

async def fetch_user(client: httpx.AsyncClient, user_id: int) -> User:
    response = await client.get(f"/users/{user_id}")
    response.raise_for_status()
    return User(**response.json())

async def fetch_all_users(user_ids: list[int]) -> list[User]:
    async with httpx.AsyncClient(base_url="https://api.example.com") as client:
        tasks = [fetch_user(client, uid) for uid in user_ids]
        return await asyncio.gather(*tasks)

async def process_with_semaphore(items: list[str], max_concurrent: int = 10):
    semaphore = asyncio.Semaphore(max_concurrent)
    async def bounded_process(item: str):
        async with semaphore:
            return await process_item(item)
    return await asyncio.gather(*[bounded_process(i) for i in items])

Rules:

  • Use httpx instead of requests for async HTTP
  • Use asyncio.gather for concurrent tasks, asyncio.Semaphore for rate limiting
  • Never call blocking I/O in async functions (use asyncio.to_thread for legacy code)
  • Use async with for resource management (connections, sessions)

Project Structure

my-project/
  src/
    my_project/
      __init__.py
      main.py
      models.py
      services/
        __init__.py
        user_service.py
      api/
        __init__.py
        routes.py
  tests/
    conftest.py
    test_models.py
    test_services/
      test_user_service.py
  pyproject.toml

Use src layout to prevent accidental imports from the project root.

pyproject.toml

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project]
name = "my-project"
version = "1.0.0"
requires-python = ">=3.12"
dependencies = [
    "httpx>=0.27",
    "pydantic>=2.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=8.0",
    "pytest-cov",
    "pytest-asyncio",
    "mypy",
    "ruff",
]

[project.scripts]
my-project = "my_project.main:cli"

[tool.ruff]
line-length = 100
target-version = "py312"

[tool.ruff.lint]
select = ["E", "F", "I", "N", "UP", "B", "SIM", "RUF"]

[tool.mypy]
strict = true

[tool.pytest.ini_options]
asyncio_mode = "auto"
testpaths = ["tests"]

Use pyproject.toml for all tool configuration. Use Ruff instead of flake8 + isort + black (single tool, 10-100x faster).

Virtual Environments

# Use uv for fast dependency management
uv venv
uv pip install -e ".[dev]"

# Or standard venv
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Always use virtual environments. Never install packages globally. Pin exact versions in a lockfile (uv.lock or requirements.txt generated from pip freeze).

Testing with pytest

import pytest
from unittest.mock import AsyncMock, patch

@pytest.fixture
def user_service(db_session):
    return UserService(session=db_session)

async def test_create_user_returns_user_with_hashed_password(user_service):
    user = await user_service.create(email="test@example.com", password="secret")
    assert user.email == "test@example.com"
    assert user.password_hash != "secret"

async def test_create_user_rejects_duplicate_email(user_service):
    await user_service.create(email="test@example.com", password="secret")
    with pytest.raises(DuplicateEmailError):
        await user_service.create(email="test@example.com", password="other")

@pytest.fixture
def mock_http_client():
    client = AsyncMock(spec=httpx.AsyncClient)
    client.get.return_value = httpx.Response(200, json={"id": 1, "name": "Alice"})
    return client

async def test_fetch_user_parses_response(mock_http_client):
    user = await fetch_user(mock_http_client, user_id=1)
    assert user.name == "Alice"
    mock_http_client.get.assert_called_once_with("/users/1")

Use conftest.py for shared fixtures. Use pytest.mark.parametrize for test variations. Use tmp_path fixture for file system tests.

Pythonic Idioms

# Unpacking
first, *rest = items
x, y = point

# Comprehensions over map/filter
squares = [x**2 for x in numbers if x > 0]
lookup = {u.id: u for u in users}

# Context managers for resource cleanup
with open(path) as f:
    data = f.read()

# Walrus operator for assign-and-test
if (match := pattern.search(text)) is not None:
    process(match.group(1))

# Structural pattern matching (3.10+)
match command:
    case {"action": "move", "direction": d}:
        move(d)
    case {"action": "quit"}:
        sys.exit(0)
    case _:
        raise ValueError(f"Unknown command: {command}")

Error Handling

class AppError(Exception):
    def __init__(self, message: str, code: str):
        super().__init__(message)
        self.code = code

class NotFoundError(AppError):
    def __init__(self, resource: str, id: str):
        super().__init__(f"{resource} {id} not found", "NOT_FOUND")

# Specific exceptions, never bare except
try:
    user = await get_user(user_id)
except NotFoundError:
    return {"error": "User not found"}, 404
except DatabaseError as e:
    logger.exception("Database error fetching user")
    return {"error": "Internal error"}, 500

Never use bare except:. Catch the most specific exception. Use logger.exception() to include tracebacks. Define custom exception hierarchies for your application.

FAQ

What does the python-best-practices skill do?

Pythonic code with modern type hints, dataclasses, async patterns, packaging, and testing

How do I install python-best-practices?

Run npx skills add https://github.com/rohitg00/awesome-claude-code-toolkit --skill python-best-practices in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/python-best-practices/ (personal) or .claude/skills/python-best-practices/ (project).

Which AI agents can use python-best-practices?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is python-best-practices safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains python-best-practices?

It is published by rohitg00 in rohitg00/awesome-claude-code-toolkit. It has 209 recorded installs.

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