
python-code-style
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/full-stack-skills/python-skills --skill python-code-style- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads python-code-style automatically when relevant.
- 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.
来源声明:本技能内容源自 wshobson/agents (MIT License, Copyright (c) 2024 Seth Hobson;完整许可文本见同目录
LICENSE.txt)。 使用、修改与再分发须遵守该 MIT 许可条款并保留版权声明。
中文描述
本技能提供全栈开发相关的最佳实践和模式指导。
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
- Use ruff - Single tool for linting and formatting
- Enable strict mypy - Catch type errors before runtime
- 120 character lines - Modern standard for readability
- Descriptive names - Clarity over brevity
- Absolute imports - More maintainable than relative
- Google-style docstrings - Consistent, readable documentation
- Document public APIs - Every public function needs a docstring
- Keep docs updated - Treat documentation as code
- Automate in CI - Run linters on every commit
- Target Python 3.10+ - For new projects, Python 3.12+ is recommended for modern language features
能力边界
✅ 擅长处理
- 全栈开发相关的最佳实践
- 代码架构设计指导
- 性能优化建议
⚠️ 需要素材
- 具体的项目上下文
- 技术栈信息
❌ 超出范围
- 非技术领域的业务决策
- 具体的代码实现(仅提供指导)
工作流程
Step 1: 需求分析
理解项目需求和技术约束
Step 2: 架构设计
设计系统架构和组件划分
Step 3: 实现指导
提供具体的实现建议和代码模式
Step 4: 代码审查
审查代码质量和最佳实践遵循情况
Step 5: 优化建议
提供性能优化和改进建议
Gotchas
1. 过度设计
避免在简单场景中使用复杂的设计模式
2. 性能忽视
不要忽视性能优化,特别是在生产环境中
3. 安全考虑
始终考虑安全性和数据保护
4. 测试覆盖
确保有足够的测试覆盖,特别是边界情况
5. 文档维护
保持文档与代码同步更新
FAQ
What does the python-code-style skill do?
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.
How do I install python-code-style?
Run npx skills add https://github.com/full-stack-skills/python-skills --skill python-code-style 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-code-style/ (personal) or .claude/skills/python-code-style/ (project).
Which AI agents can use python-code-style?
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-code-style 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-code-style?
It is published by full-stack-skills in full-stack-skills/python-skills. It has 96 recorded installs.
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