python-code-style

Source Attribution: The content of this skill is derived from wshobson/agents (MIT License, Copyright (c) 2024 Seth Hobson; the full license text is available in the same directory at LICENSE.txt). Use, modification, and redistribution must comply with the terms of this MIT License and retain the copyright notice.

Chinese Description

This skill provides guidance on best practices and patterns related to full-stack development.

---

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

# 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.
        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:

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.

# 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:

# 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):

# 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

Capability Boundaries

✅ Good at handling

• Best practices related to full-stack development
• Code architecture design guidance
• Performance optimization suggestions

⚠️ Requires materials

• Specific project context
• Tech stack information

❌ Out of scope

• Business decisions in non-technical domains
• Specific code implementation (guidance only)

Workflow

Step 1: Requirements Analysis

Understand project requirements and technical constraints

Step 2: Architecture Design

Design system architecture and component division

Step 3: Implementation Guidance

Provide specific implementation suggestions and code patterns

Step 4: Code Review

Review code quality and adherence to best practices

Step 5: Optimization Suggestions

Provide performance optimization and improvement suggestions

Gotchas

1. Over-engineering

Avoid using complex design patterns in simple scenarios

2. Ignoring Performance

Do not neglect performance optimization, especially in production environments

3. Security Considerations

Always consider security and data protection

4. Test Coverage

Ensure sufficient test coverage, especially for edge cases

5. Documentation Maintenance

Keep documentation updated in sync with the code