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python-code-style

Helps your agent follow style, naming, and documentation standards in Python code. Useful for keeping code clean, readable, and easy to understand.

What it does

Helps organize and standardize code written in Python. It ensures code is easy to read, understand, and maintain by following good naming and documentation practices.

When to use it

  • ✓When starting a new Python project.
  • ✓When reviewing someone else's code.
  • ✓When setting up tools that check code quality.

Example of what to ask your AI

“How can I ensure my Python code follows best style practices?”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/wshobson/agents/blob/main/plugins/python-development/skills/python-code-style/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/wshobson/agents/blob/main/plugins/python-development/skills/python-code-style/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

It uses automatic tools to format code and check if it follows standards. It also sets clear rules for naming variables, functions, and classes, as well as how to write documentation.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add wshobson/agents --skill python-code-style

Or download just the file:

curl -L https://raw.githubusercontent.com/wshobson/agents/main/plugins/python-development/skills/python-code-style/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Python Code Style & Documentation

Consistent code style and clear documentation make codebases maintainable and collaborative. This skill covers modern Python tooling, naming conventions, and documentation standards.

When to Use This Skill

  • Setting up linting and formatting for a new project
  • Writing or reviewing docstrings
  • Establishing team coding standards
  • Configuring ruff, mypy, or pyright
  • Reviewing code for style consistency
  • Creating project documentation

Core Concepts

1. Automated Formatting

Let tools handle formatting debates. Configure once, enforce automatically.

2. Consistent Naming

Follow PEP 8 conventions with meaningful, descriptive names.

3. Documentation as Code

Docstrings should be maintained alongside the code they describe.

4. Type Annotations

Modern Python code should include type hints for all public APIs.

Quick Start

# Install modern tooling
pip install ruff mypy

# Configure in pyproject.toml
[tool.ruff]
line-length = 120
target-version = "py312"  # Adjust based on your project's minimum Python version

[tool.mypy]
strict = true

Fundamental Patterns

Pattern 1: Modern Python Tooling

Use ruff as an all-in-one linter and formatter. It replaces flake8, isort, and black with a single fast tool.

# pyproject.toml
[tool.ruff]
line-length = 120
target-version = "py312"  # Adjust based on your project's minimum Python version

[tool.ruff.lint]
select = [
    "E",    # pycodestyle errors
    "W",    # pycodestyle warnings
    "F",    # pyflakes
    "I",    # isort
    "B",    # flake8-bugbear
    "C4",   # flake8-comprehensions
    "UP",   # pyupgrade
    "SIM",  # flake8-simplify
]
ignore = ["E501"]  # Line length handled by formatter

[tool.ruff.format]
quote-style = "double"
indent-style = "space"

Run with:

ruff check --fix .  # Lint and auto-fix
ruff format .       # Format code

Pattern 2: Type Checking Configuration

Configure strict type checking for production code.

# pyproject.toml
[tool.mypy]
python_version = "3.12"
strict = true
warn_return_any = true
warn_unused_ignores = true
disallow_untyped_defs = true
disallow_incomplete_defs = true

[[tool.mypy.overrides]]
module = "tests.*"
disallow_untyped_defs = false

Alternative: Use pyright for faster checking.

[tool.pyright]
pythonVersion = "3.12"
typeCheckingMode = "strict"

Pattern 3: Naming Conventions

Follow PEP 8 with emphasis on clarity over brevity.

Files and Modules:

# Good: Descriptive snake_case
user_repository.py
order_processing.py
http_client.py

# Avoid: Abbreviations
usr_repo.py
ord_proc.py
http_cli.py

Classes and Functions:

# Classes: PascalCase
class UserRepository:
    pass

class HTTPClientFactory:  # Acronyms stay uppercase
    pass

# Functions and variables: snake_case
def get_user_by_email(email: str) -> User | None:
    retry_count = 3
    max_connections = 100

Constants:

# Module-level constants: SCREAMING_SNAKE_CASE
MAX_RETRY_ATTEMPTS = 3
DEFAULT_TIMEOUT_SECONDS = 30
API_BASE_URL = "https://api.example.com"

Pattern 4: Import Organization

Group imports in a consistent order: standard library, third-party, local.

# Standard library
import os
from collections.abc import Callable
from typing import Any

# Third-party packages
import httpx
from pydantic import BaseModel
from sqlalchemy import Column

# Local imports
from myproject.models import User
from myproject.services import UserService

Use absolute imports exclusively:

# Preferred
from myproject.utils import retry_decorator

# Avoid relative imports
from ..utils import retry_decorator

Advanced Patterns

Pattern 5: Google-Style Docstrings

Write docstrings for all public classes, methods, and functions.

Simple Function:

def get_user(user_id: str) -> User:
    """Retrieve a user by their unique identifier."""
    ...

Complex Function:

def process_batch(
    items: list[Item],
    max_workers: int = 4,
    on_progress: Callable[[int, int], None] | None = None,
) -> BatchResult:
    """Process items concurrently using a worker pool.

    Processes each item in the batch using the configured number of
    workers. Progress can be monitored via the optional callback.

    Args:
        items: The items to process. Must not be empty.
        max_workers: Maximum concurrent workers. Defaults to 4.

…
FAQ

What is 'linting'?

It's the process of analyzing code for programming errors, bugs, style issues, and suspicious constructs.

What are 'docstrings'?

These are documentation strings that explain what a function, class, or module does, helping other programmers (and yourself in the future) understand the code.

Why is naming important?

Clear and consistent names for variables, functions, and classes make code easier to read and understand, preventing confusion.

Details

Original description by the author

Python code style, linting, formatting, naming conventions, and documentation standards. Use when writing new code, reviewing style, configuring linters, writing docstrings, or establishing project standards.

repo: wshobson/agents

Popularity

GitHub stars
41k
Ranking position
#4925
License
MIT
Updated
10/15/2026

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