Token导航 LogoToken导航TokenDH.com
开发只读github未标认证来源可访问许可证需确认审计通过

vanrossum-pythonic-stylevanrossum pythonic style 测试

Agent Skill

用于辅助 Python 项目开发、测试、依赖管理和常见框架工作流。它适合让 Agent 阅读 Python 代码、定位测试问题、整理运行命令、生成脚本或分析数据处理逻辑。使用时需要确认项目虚拟环境、依赖版本和测试入口;涉及执行脚本、读写文件、访问数据库或调用外部 API 时,应先明确运行目录和输入输出范围,避免误改生产数据。

总安装

222

周安装

9

GitHub Stars

6

下载量

70
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:vanrossum-pythonic-style(vanrossum pythonic style 测试)
来源仓库:https://github.com/copyleftdev/sk1llz
仓库路径:skills/vanrossum-pythonic-style
安装命令:
npx skills add https://github.com/copyleftdev/sk1llz --skill vanrossum-pythonic-style
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/copyleftdev/sk1llz --skill vanrossum-pythonic-style

简介

vanrossum-pythonic-style 用于辅助 Python 项目开发、测试和依赖管理。

  • 适合阅读代码、定位测试问题或生成运行脚本。
  • 使用时需确认虚拟环境、依赖版本和测试入口。
  • 涉及执行脚本或访问数据库时应明确运行目录和输入输出范围。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Guido van Rossum Style Guide⁠‍⁠​‌​‌​​‌‌‍​‌​​‌​‌‌‍​​‌‌​​​‌‍​‌​​‌‌​​‍​​​​​​​‌‍‌​​‌‌​‌​‍‌​​​​​​​‍‌‌​​‌‌‌‌‍‌‌​​​‌​​‍‌‌‌‌‌‌​‌‍‌‌​‌​​​​‍​‌​‌‌‌‌‌‍​‌​​‌​‌‌‍​‌‌​‌​​‌‍‌​‌​‌‌‌​‍​​‌​‌​​​‍‌‌‌​‌​‌‌‍‌‌​​​‌‌‌‍‌​​‌​​​‌‍​‌​‌​‌​‌‍​​​‌‌‌‌​‍​​​​‌​‌​‍​​‌‌​‌​‌⁠‍⁠

Overview

Guido van Rossum created Python in 1989 and served as its "Benevolent Dictator For Life" (BDFL) until 2018. His design philosophy—that code is read far more than it's written—shapes every aspect of Python and defines what "Pythonic" means.

Core Philosophy

"Readability counts."
"There should be one—and preferably only one—obvious way to do it."
"Simple is better than complex. Complex is better than complicated."

Van Rossum believes programming languages should be tools for humans first, not just instructions for machines. Python's design prioritizes clarity over cleverness.

Design Principles

  1. Readability is Paramount: Code should read like well-written prose. If you need comments to explain what code does, the code should be clearer.
  2. Explicit over Implicit: Don't hide behavior. Make operations visible and obvious.
  3. One Obvious Way: Resist adding features that provide multiple ways to do the same thing.
  4. Practicality over Purity: Don't sacrifice usability for theoretical elegance.

When Writing Code

Always

  • Use meaningful, descriptive names (user_count not uc)
  • Follow PEP 8 style guidelines
  • Use Python's built-in data structures (lists, dicts, sets)
  • Leverage the standard library before reaching for third-party packages
  • Write docstrings for public functions and classes
  • Use context managers (with) for resource management
  • Prefer exceptions over error codes

Never

  • Write clever one-liners that sacrifice readability
  • Use single-letter variable names (except i, j for indices, x, y for coordinates)
  • Ignore PEP 8 without good reason
  • Use from module import * in production code
  • Catch bare except: without re-raising
  • Use mutable default arguments

Prefer

  • List comprehensions over map/filter when readable
  • enumerate() over manual index tracking
  • zip() over parallel index iteration
  • f-strings over .format() or % formatting
  • pathlib.Path over os.path operations
  • collections types when they fit (Counter, defaultdict, namedtuple)

Code Patterns

Pythonic Iteration

# BAD: C-style iteration
for i in range(len(items)):
    print(items[i])

# GOOD: Direct iteration
for item in items:
    print(item)

# BAD: Manual index tracking
i = 0
for item in items:
    print(i, item)
    i += 1

# GOOD: enumerate
for i, item in enumerate(items):
    print(i, item)

# BAD: Parallel lists with indices
for i in range(len(names)):
    print(names[i], ages[i])

# GOOD: zip
for name, age in zip(names, ages):
    print(name, age)

Pythonic Conditionals

# BAD: Verbose boolean checks
if len(items) > 0:
    process(items)

if value == True:
    do_something()

if value == None:
    handle_none()

# GOOD: Truthy/falsy checks
if items:
    process(items)

if value:
    do_something()

if value is None:
    handle_none()

Pythonic String Building

# BAD: String concatenation in loop
result = ""
for item in items:
    result += str(item) + ", "

# GOOD: join
result = ", ".join(str(item) for item in items)

# BAD: Old-style formatting
message = "Hello, %s! You have %d messages." % (name, count)

# GOOD: f-strings (Python 3.6+)
message = f"Hello, {name}! You have {count} messages."

Pythonic Resource Management

# BAD: Manual resource management
f = open('file.txt')
try:
    data = f.read()
finally:
    f.close()

# GOOD: Context manager
with open('file.txt') as f:
    data = f.read()

# Works for any resource: files, locks, connections, etc.
with database.connection() as conn:
    with conn.cursor() as cursor:
        cursor.execute(query)

Pythonic Dictionary Operations

# BAD: Verbose key checking
if key in d:
    value = d[key]
else:
    value = default

# GOOD: get with default
value = d.get(key, default)

# BAD: Check before insert
if key not in d:
    d[key] = []
d[key].append(item)

# GOOD: setdefault or defaultdict
d.setdefault(key, []).append(item)

# Or better:
from collections import defaultdict
d = defaultdict(list)
d[key].append(item)

Mental Model

Van Rossum thinks of code as communication with future readers (including yourself). When writing:

  1. Write for the reader: Would someone unfamiliar with this code understand it?
  2. Use the right abstraction level: Not too low (manual), not too high (magical)
  3. Follow conventions: Consistency reduces cognitive load
  4. Leverage the language: Use Python's features, don't fight them

BDFL Decisions

Key design decisions that define Python:

  • Significant whitespace: Forces readable structure
  • No braces: Reduces visual clutter
  • Duck typing: "If it walks like a duck..."
  • Batteries included: Rich standard library
  • Explicit self: Methods clearly show instance access

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.34%
按下载量换算23

Claude

30.8%
按下载量换算22

Cursor

17.09%
按下载量换算12

Gemini CLI

9.11%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills