home / skills / julianobarbosa / claude-code-skills / python-code-style-skill
This skill helps you enforce Python code style and documentation standards by guiding linting, formatting, naming, and docstrings across projects.
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---
name: python-code-style
description: 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.
---
# 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
```bash
# 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.
```toml
# 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:
```bash
ruff check --fix . # Lint and auto-fix
ruff format . # Format code
```
### Pattern 2: Type Checking Configuration
Configure strict type checking for production code.
```toml
# 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.
```toml
[tool.pyright]
pythonVersion = "3.12"
typeCheckingMode = "strict"
```
### Pattern 3: Naming Conventions
Follow PEP 8 with emphasis on clarity over brevity.
**Files and Modules:**
```python
# 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:**
```python
# 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:**
```python
# 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.
```python
# 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:
```python
# 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:**
```python
def get_user(user_id: str) -> User:
"""Retrieve a user by their unique identifier."""
...
```
**Complex Function:**
```python
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.
on_progress: Optional callback receiving (completed, total) counts.
Returns:
BatchResult containing succeeded items and any failures with
their associated exceptions.
Raises:
ValueError: If items is empty.
ProcessingError: If the batch cannot be processed.
Example:
>>> result = process_batch(items, max_workers=8)
>>> print(f"Processed {len(result.succeeded)} items")
"""
...
```
**Class Docstring:**
```python
class UserService:
"""Service for managing user operations.
Provides methods for creating, retrieving, updating, and
deleting users with proper validation and error handling.
Attributes:
repository: The data access layer for user persistence.
logger: Logger instance for operation tracking.
Example:
>>> service = UserService(repository, logger)
>>> user = service.create_user(CreateUserInput(...))
"""
def __init__(self, repository: UserRepository, logger: Logger) -> None:
"""Initialize the user service.
Args:
repository: Data access layer for users.
logger: Logger for tracking operations.
"""
self.repository = repository
self.logger = logger
```
### Pattern 6: Line Length and Formatting
Set line length to 120 characters for modern displays while maintaining readability.
```python
# Good: Readable line breaks
def create_user(
email: str,
name: str,
role: UserRole = UserRole.MEMBER,
notify: bool = True,
) -> User:
...
# Good: Chain method calls clearly
result = (
db.query(User)
.filter(User.active == True)
.order_by(User.created_at.desc())
.limit(10)
.all()
)
# Good: Format long strings
error_message = (
f"Failed to process user {user_id}: "
f"received status {response.status_code} "
f"with body {response.text[:100]}"
)
```
### Pattern 7: Project Documentation
**README Structure:**
```markdown
# Project Name
Brief description of what the project does.
## Installation
\`\`\`bash
pip install myproject
\`\`\`
## Quick Start
\`\`\`python
from myproject import Client
client = Client(api_key="...")
result = client.process(data)
\`\`\`
## Configuration
Document environment variables and configuration options.
## Development
\`\`\`bash
pip install -e ".[dev]"
pytest
\`\`\`
```
**CHANGELOG Format (Keep a Changelog):**
```markdown
# Changelog
## [Unreleased]
### Added
- New feature X
### Changed
- Modified behavior of Y
### Fixed
- Bug in Z
```
## Best Practices Summary
1. **Use ruff** - Single tool for linting and formatting
2. **Enable strict mypy** - Catch type errors before runtime
3. **120 character lines** - Modern standard for readability
4. **Descriptive names** - Clarity over brevity
5. **Absolute imports** - More maintainable than relative
6. **Google-style docstrings** - Consistent, readable documentation
7. **Document public APIs** - Every public function needs a docstring
8. **Keep docs updated** - Treat documentation as code
9. **Automate in CI** - Run linters on every commit
10. **Target Python 3.10+** - For new projects, Python 3.12+ is recommended for modern language features
This skill teaches Python code style, linting, formatting, naming conventions, and documentation standards to make codebases consistent and maintainable. It focuses on modern tooling (ruff, mypy/pyright), PEP 8-based naming, Google-style docstrings, import organization, and CI automation. Use it when creating new code, reviewing style, or defining project standards.
The skill inspects code structure, naming, imports, type annotations, and docstrings and shows concrete configuration patterns for tools like ruff, mypy, and pyright. It provides recommended pyproject.toml snippets, lint/format commands, and examples for naming, imports, line length, and documentation. The guidance is practical: configure once, enforce in CI, and let tools auto-fix where possible.
Should I always enable strict mypy for every repository?
Aim for strict checks on public modules and critical paths. Relax rules in tests or legacy modules and incrementally tighten checks as code is modernized.
Why prefer ruff over separate tools like black and isort?
Ruff is fast and consolidates rules from multiple linters and formatters, reducing toolchain complexity while still allowing specific configuration for formatting and lint selection.