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Agent Skill · 前端设计

elegant-code(优雅的代码)

elegant-code 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

elegant-code 是前端设计类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描已通过,按安装量在前端设计类的 10,216 个 Skill 里排第 8,138。

总安装 312周安装 13GitHub Stars 16下载 104核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:elegant-code(优雅的代码)
来源仓库:https://github.com/oleksandrkucherenko/e-bash
仓库路径:skills/elegant-code
安装命令:
npx skills add https://github.com/oleksandrkucherenko/e-bash --skill elegant-code
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/oleksandrkucherenko/e-bash --skill elegant-code

这个 Skill 是做什么的

前端设计类 Skill 关注页面结构、视觉规范和组件写法,输出通常是可运行的界面代码。

elegant-code 提供代码优雅性评估和改进指导,注重可读性和可维护性。优先保证正确性和意图清晰,避免过度优化和复杂技巧。强调变更安全性,使修改局部化和低风险。适用于重构和代码评审场景,提升团队编码质量。

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。

来源仓库内路径:skills/elegant-code

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/oleksandrkucherenko/e-bash --skill elegant-cod…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

elegant-code 怎么安装?

在终端执行 npx skills add https://github.com/oleksandrkucherenko/e-bash --skill elegant-code。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

elegant-code 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

elegant-code 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/oleksandrkucherenko/e-bash --skill elegant-code 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。

elegant-code 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

elegant-code 用的人多吗?

公开数据是累计安装 312 次,近一周安装 13 次,下载 104 次,GitHub 16 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

elegant-code 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理前端设计类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

前端设计类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
frontend-design

前端设计 · Codex、Claude

8,823,360126,072只读MIT通过
vercel-composition-patterns

Vercel 组合模式 · Codex、Claude

3,858,81625,893需要联网MIT通过
vercel-react-native-skills

Vercel React native skills 前端 · Codex、Claude

2,468,66425,878只读MIT通过
adapt

adapt 搜索 · Codex、Claude

2,027,04023,125操作浏览器未明确通过
bolder

bolder 分析 · Codex、Claude

2,004,28823,130只读未明确通过
elegant-code

优雅的代码 · 本页

31216执行命令未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Elegant Code

Elegant code = Correctness + Clarity + Minimal complexity + Natural efficiency + Easy change.

Not "shortest" or "cleverest." The solution that makes readers think: *"Of course. That's simple, right, and hard to mess up."*

Optimization Priority (in order)

  1. Correctness & safety — does what it claims, handles edge cases
  2. Clarity of intent — reader answers "what/why/how" from code itself
  3. Simplicity — minimal concepts that still solve the problem
  4. Changeability — modifications are localized and low-risk
  5. Efficiency — good algorithms; performance from good design, not micro-optimizations

Elegant vs "Smart" Code

ElegantSmart/Clever
Clarity & solutionCleverness & brevity
Humans firstMachine first (humans decode)
Low complexityHigh complexity
Easy to debug/changeFragile and opaque
"Of course!""Wait… how?"

The Workflow

Use this sequence when writing or refactoring:

1. Clarify the problem

  • Define inputs, outputs, invariants, constraints, failure modes
  • Identify postconditions (what must be true after)
  • List edge cases and "gotchas"

2. Choose simplest correct approach

  • Pick simplest algorithm/data structure meeting constraints
  • Prefer fewer concepts over more layers
  • If adding abstraction, state what complexity it removes

3. Design shape before details

  • Outline modules/functions and responsibilities
  • Decide boundaries: pure logic vs side effects (I/O, database, network)

4. Write readable-by-default code

  • Clear names, small units, straightforward control flow
  • Make "happy path" obvious; handle errors intentionally

5. Add guardrails

  • Validation, assertions (where appropriate), tests
  • Define invalid input handling

6. Refine (remove, simplify, clarify)

  • Remove duplication, tighten interfaces, reduce nesting
  • Each unit should read like a single thought

7. Verify against reality

  • Run tests; benchmark if performance matters
  • Confirm behavior matches original problem statement

Core Rules

Rule 1 — Preserve intent above everything

Reader must answer: What does this do? Why? What constraints? What can go wrong?

  • Comments explain why, tradeoffs, constraints—not what code does
  • If you need comments to explain *what*, the code is unclear

Rule 2 — Minimize concepts, not lines

Keep distinct "ideas" (types, abstractions, layers, config knobs) minimal.

  • Prefer one good function over a mini-framework
  • No "future-proofing" without concrete known need
  • If abstraction adds indirection without removing complexity, remove it

Rule 3 — Common case simple; edge cases explicit

  • Happy path easy to follow
  • Edge cases via early returns, guard clauses, explicit validation
  • Avoid deep nesting hiding the main story

Rule 4 — Small, cohesive units

  • One primary responsibility per function/module/class
  • Group related logic; separate unrelated concerns
  • If name needs "and" (parseAndSave), it's doing too much

Rule 5 — Explicit data flow over hidden state

  • Data through parameters and return values, not globals/singletons/mutable shared state
  • If you can't test without elaborate setup, hidden context exists
  • If call order matters, make it explicit

Rule 6 — DRY knowledge, not syntax

  • Unify if two places must change together
  • Allow small obvious repetition if abstraction is harder to read
  • Duplicate code sometimes cheaper than leaky abstraction

Rule 7 — Right algorithmic shape

  • Choose algorithms/data structures appropriate to constraints
  • Prefer clarity unless profiling demands complexity
  • Prefer asymptotic wins over micro-optimizations

Rule 8 — Make invalid states unrepresentable

  • Represent domain constraints in types/structures so illegal combinations are impossible
  • Validate at boundaries, convert to trusted internal representations

Rule 9 — Local reasoning

  • Understand a unit without chasing definitions across codebase
  • Small interfaces, directness over indirection
  • Configuration close to usage or centralized with clear naming

Rule 10 — Idiomatic but readable

  • Follow language/project conventions
  • Don't use obscure tricks only experts recognize
  • If idiom is compact but unclear, choose clearer form

Micro-Rules

Naming

  • DO: Names encode intent and domain meaning, not mechanics
  • DO: Concrete nouns/verbs: calculate_total, is_valid, parse_header
  • AVOID: Vague names: data, process, handle, doThing, tmp, manager
  • DO: One concept → one term (consistent vocabulary)

Functions

  • Short enough for working memory
  • Inputs/outputs obvious and stable
  • Side effects clear from name or context
  • Favor pure functions for core logic
  • I/O at edges ("functional core, imperative shell")

Control Flow

  • Avoid deep nesting; use guard clauses
  • Early exits for invalid conditions
  • Straightforward over clever

Error Handling

  • Decide: recover, retry, fallback, or fail fast—then implement consistently
  • Errors carry enough context to debug
  • Validate at boundaries
  • Never swallow errors silently

Dependencies

  • Depend on stable interfaces, not unstable internals
  • Minimal and purposeful dependencies
  • Inject dependencies where it improves testability

Self-Review Checklist

Before finalizing code, verify:

  • Correctness: Meets requirements; edge cases handled; failures intentional
  • Clarity: Names meaningful; reads top-to-bottom; minimal mental jumps
  • Simplicity: No unnecessary abstractions; minimal moving parts
  • Cohesion: Each unit has one job; responsibilities not mixed
  • Coupling: Dependencies minimal; interfaces small and stable
  • Data flow: Inputs/outputs explicit; minimal hidden state
  • Error handling: Consistent strategy; errors include context
  • Efficiency: Algorithm/data structures fit constraints; no obvious waste
  • Consistency: Patterns match codebase norms
  • Testability: Core logic testable easily; tests exist for tricky parts

Refactor Decision Rules

Refactor if:

  • Function/module cannot be summarized in one sentence
  • Must read twice to trust it
  • One change requires edits in many unrelated places
  • Bugs cluster in same area repeatedly
  • Keep adding special cases ("just one more flag")

Avoid refactoring if:

  • No tests and behavior unclear (add tests first)
  • Code stable and rarely changed, improvements purely aesthetic
  • Near deadline and risk is high (smallest safe improvements only)

Detailed References

平台分布

Codex

32.31%
按下载量换算34

Claude

31.79%
按下载量换算33

Cursor

16.73%
按下载量换算17

Gemini CLI

9.79%
按下载量换算10

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/oleksandrkucherenko/e-bash --skill elegant-code 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。目前只有一个公开来源,建议同时核对源仓库。

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