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python-design-modularityPython 设计 modularity

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

685

周安装

28

GitHub Stars

2

下载量

220
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:python-design-modularity(Python 设计 modularity)
来源仓库:https://github.com/ahgraber/skills
仓库路径:skills/python-design-modularity
安装命令:
npx skills add https://github.com/ahgraber/skills --skill python-design-modularity
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/ahgraber/skills --skill python-design-modularity

简介

用于提升 Python 代码的结构化与模块化设计水平。

  • 适合分析类、函数划分及模块间耦合度优化。
  • 提供重构建议和接口抽象指导,增强可维护性。
  • 基于项目实际代码风格给出适配性建议。python-design-modularity 属于前端设计类 Skill,可作为该场景下的辅助能力补充。
  • 涉及代码修改时需同步更新测试用例确保稳定性。

SKILL.md

Python Design and Modularity

Overview

Readability-first design with explicit module contracts. Keep control flow, data movement, and ownership boundaries visible so code stays maintainable and safe to change.

Treat these recommendations as preferred defaults. When a default conflicts with project constraints, suggest a better-fit alternative and call out tradeoffs and compensating controls (tests, observability, migration, rollback).

When to Use

  • Restructuring modules, packages, or ownership boundaries
  • Breaking apart god classes or deeply nested hierarchies
  • Choosing between composition and inheritance
  • Applying Functional Core / Imperative Shell separation
  • Planning a refactor that touches multiple modules
  • Reviewing code for readability or architectural clarity

When NOT to use:

  • Pure performance optimization — see python-concurrency-performance
  • Error handling and resilience patterns — see python-errors-reliability
  • Type contracts and protocol design — see python-types-contracts
  • One-off script or throwaway code with no maintenance horizon

Quick Reference

  • Keep control flow and data movement explicit.
  • Keep module ownership and invariants explicit.
  • Prefer composition by default.
  • Apply Functional Core / Imperative Shell where it improves testability and separation of concerns.
  • Separate behavior changes from structural refactors — never mix in the same commit.

Common Mistakes

  • Refactoring behavior and structure simultaneously — conflates two kinds of risk, makes rollback harder, and obscures review. Do one, then the other.
  • Reaching for inheritance first — deep hierarchies couple unrelated concerns and make reasoning non-local. Default to composition; inherit only when the "is-a" relationship is genuinely stable.
  • Hidden module coupling — importing implementation details across boundaries creates invisible contracts. Expose explicit public APIs and keep internals private.
  • Premature abstraction — extracting a shared interface before the second or third concrete use leads to wrong abstractions that are expensive to undo. Wait for duplication to reveal the real seam.
  • Ignoring the Functional Core / Imperative Shell split — mixing I/O with business logic makes unit testing painful and increases the blast radius of changes. Push side effects to the edges.

Scope Note

  • Treat these recommendations as preferred defaults for common cases, not universal rules.
  • If a default conflicts with project constraints or worsens the outcome, suggest a better-fit alternative and explain why it is better for this case.
  • When deviating, call out tradeoffs and compensating controls (tests, observability, migration, rollback).

Invocation Notice

  • Inform the user when this skill is being invoked by name: python-design-modularity.

References

  • references/design-rules.md
  • references/readability-and-complexity.md
  • references/module-boundaries.md
  • references/functional-core-shell.md
  • references/refactor-guidelines.md

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能力 2

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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

34.83%
按下载量换算77

Claude

30.49%
按下载量换算67

Cursor

17.8%
按下载量换算39

Gemini CLI

10.32%
按下载量换算23

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/ahgraber/skills --skill python-design-modularity 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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

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