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elegant-code优雅的代码

Agent Skill

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。

总安装

312

周安装

13

GitHub Stars

16

下载量

104
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: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

简介

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。

SKILL.md

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

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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

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。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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