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Agent Skill · 研究检索

code-refactoring(代码重构)

code-refactoring 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

code-refactoring 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 50,882。

总安装 605周安装 26GitHub Stars 11下载 212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:code-refactoring(代码重构)
来源仓库:https://github.com/akillness/skills-template
仓库路径:skills/code-refactoring
安装命令:
npx skills add https://github.com/akillness/skills-template --skill code-refactoring
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/akillness/skills-template --skill code-refactoring

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于改进代码结构而不改变预期行为。code-refactoring 属于研究检索类 Skill,可作为该场景下的辅助能力补充。支持函数拆分、命名优化和依赖简化。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。需明确定义重构范围并设置行为护栏。

建议分阶段提交,每步都应有验证机制。安装前请确认代码所有权,避免影响他人分支。

来源仓库内路径:skills/code-refactoring

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/akillness/skills-template --skill code-refacto…;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

    它需要需要联网,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

code-refactoring 怎么安装?

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

code-refactoring 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。

code-refactoring 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

code-refactoring 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。

code-refactoring 用的人多吗?

公开数据是累计安装 605 次,近一周安装 26 次,下载 212 次,GitHub 11 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。

code-refactoring 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

  • 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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
code-refactoring

代码重构 · 本页

60511需要联网未明确通过

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

Code Refactoring

Use this skill when the job is to improve structure without changing intended behavior.

The center of the skill should stay small and repeatable:

  1. identify the cleanup packet you actually have,
  2. choose one refactor mode,
  3. make the behavior guardrail explicit,
  4. stage the work in reviewable slices,
  5. verify and route remaining work honestly.

Read these support docs before handling unfamiliar cleanup work:

When to use this skill

  • A function, component, service, script, or module is too tangled and needs structural cleanup without a behavior change.
  • A legacy area needs a freeze-first cleanup because the current behavior is fragile or poorly understood.
  • The same API, naming, or structure change repeats across many files and needs a codemod / migration brief.
  • A diff mixes cleanup with too much semantic work and needs to be split into smaller reviewable slices.
  • The user asks to refactor, decompose, deduplicate, rename safely, stage a cleanup, or plan a behavior-preserving migration.

When not to use this skill

  • The main job is proving why behavior is wrong, reproducing a failure, or isolating a regression → debugging
  • The main job is deciding whether a concrete diff / PR is safe to merge → code-review
  • The main job is choosing org-wide validation depth, benchmark policy, or release gates → testing-strategies
  • The main job is finding the bottleneck from traces, flamegraphs, CWV reports, or profiler output → performance-optimization
  • The main job is finding symbols, call sites, or impact scope before any cleanup tactic is chosen → codebase-search

Instructions

Step 1: Start from the cleanup packet

Use references/intake-packets-and-route-outs.md.

Choose the packet the user already has:

  • one messy file / component / service
  • a fragile legacy area with weak confidence in current behavior
  • a repeated migration pattern across many files
  • a cleanup-heavy diff that needs reshaping before review
  • only a vague desire to “find all the places first”

Output the intake briefly:

## Cleanup Packet
- Current artifact:
- Why it is enough (or not enough):
- Missing evidence to collect next:

Rule: do not force a giant refactor plan when the immediate need is only search, diagnosis, review, or performance evidence.

Step 2: Choose one primary refactor mode

Pick exactly one primary mode from references/refactor-modes.md.

Primary modes:

  • local-safe-refactor
  • behavior-freeze-first
  • repetitive-migration-codemod
  • diff-shaping-cleanup

Quick selector:

SignalMode
One file or narrow module, clear intent, at least one fast guardrail existslocal-safe-refactor
Fragile legacy area, hidden invariants, weak test trustbehavior-freeze-first
Same API or structure repeats across many filesrepetitive-migration-codemod
Cleanup is mixed into a risky review diff and needs smaller slicesdiff-shaping-cleanup

Rule: one primary mode, optional secondary note. Do not mix every cleanup tactic into one answer.

Step 3: Freeze the behavior guardrail

Before broad edits, decide how you will prove intent stayed the same.

Guardrail sources can include:

  • existing unit / integration / end-to-end tests
  • typecheck and linter
  • characterization tests or captured examples
  • fixture snapshots or golden outputs
  • screenshots / preview captures for UI work
  • before/after sample input-output tables
  • manual smoke steps when automation is thin

Minimum rule:

  • Local cleanup → at least one fast verification path
  • Fragile legacy cleanup → freeze behavior first
  • Repeated migration → pilot on a small representative sample first
  • Diff reshaping → separate mechanical cleanup from semantic follow-up

If the user mainly needs help designing the entire validation program, route to testing-strategies.

Step 4: Build the smallest credible cleanup plan

Keep the plan reviewable.

Preferred slices:

  1. rename / move / extract work
  2. duplicated or dead-code cleanup
  3. mechanical migration or codemod rollout
  4. semantic follow-up only if still needed
  5. verification + handoff

For each slice, capture:

  • goal
  • behavior to preserve
  • evidence / guardrail
  • risk edge
  • whether another skill owns the next step

Rules:

  • Prefer a sequence of boring diffs over one heroic rewrite.
  • Keep structural cleanup and semantic behavior changes separate when possible.
  • For migrations, define source pattern, target pattern, known exceptions, and rollback path before scaling up.

Step 5: Use the right mode packet

Use the matching packet in references/intake-packets-and-route-outs.md:

  • local cleanup packet
  • fragile legacy / freeze-first packet
  • repeated migration / codemod packet
  • diff-shaping packet

Good moves by mode:

  • local-safe-refactor → rename unclear concepts, extract pure logic, move side effects to edges, collapse close duplication
  • behavior-freeze-first → capture examples, add characterization tests, identify one seam, refactor behind that seam
  • repetitive-migration-codemod → define source/target pattern, sample first, inspect false positives, expand only after the pilot is trustworthy
  • diff-shaping-cleanup → split cleanup from semantic changes, isolate generated or mechanical edits, leave review notes about remaining hotspots

Step 6: Verify and route remaining work

Do not stop at “looks cleaner.”

Verification brief:

## Refactor Brief
- Primary mode:
- Behavior to preserve:
- Guardrail used:
- Smallest planned slices:
- Risks still open:
- Recommended next move:

Always call out:

  • what behavior was intended to stay the same
  • what evidence was used to verify that
  • what still remains risky or out of scope
  • which neighboring skill should own the next step when the job shifts

Output format

## Cleanup Packet
- Current artifact:
- Primary mode:
- Why this mode fits:

## Behavior Guardrail
- Intended behavior to preserve:
- Evidence available:
- Missing evidence:

## Planned slices
1. ...
2. ...
3. ...

## Verification
- Fast checks:
- Higher-risk checks:

## Route-outs
- Use `debugging` for:
- Use `code-review` for:
- Use `testing-strategies` for:
- Use `performance-optimization` for:
- Use `codebase-search` for:

Examples

Example 1: Oversized service handler

Input: "Refactor this 180-line checkout handler into something readable without changing behavior."

Good response shape:

  • choose local-safe-refactor
  • preserve coupon / tax / out-of-stock behavior explicitly
  • extract validation, pricing, and persistence helpers
  • keep tests / typecheck as guardrails
  • split structural cleanup from later semantic follow-up

Example 2: Fragile legacy module

Input: "This reporting service is impossible to maintain, but we barely trust the tests. Help me refactor it safely."

Good response shape:

  • choose behavior-freeze-first
  • capture characterization cases before broad cleanup
  • identify one seam at a time instead of redesigning everything
  • route deep failure investigation to debugging if expected behavior is still unclear

Example 3: Repeated API migration

Input: "We need to replace a deprecated client API across 220 TypeScript files before the framework upgrade."

Good response shape:

  • choose repetitive-migration-codemod
  • define source and target patterns
  • pilot the transform on a subset first
  • keep mechanical rewrite separate from semantic follow-up
  • verify with tests, typecheck, and repo search

Example 4: Search-first route-out

Input: "Before we refactor anything, find every call site and wrapper around this old helper so we can see the blast radius."

Good response shape:

  • route the primary task to codebase-search
  • do not present a full refactor plan as the main answer
  • keep code-refactoring positioned as the cleanup lane after the impact map exists

Best practices

  1. Start from the packet the user actually has, not an idealized cleanup workflow.
  2. Pick one primary mode before proposing actions.
  3. Make behavior preservation explicit; do not assume it.
  4. Prefer small, reviewable slices over one giant cleanup diff.
  5. Use codemods or structural rewrites only when repetition justifies the setup cost.
  6. Keep diagnosis, review judgment, test-policy design, performance tuning, and symbol inventory routed to neighboring skills instead of absorbing them.
  7. Preserve evidence of what was verified and what still remains risky.

References

平台分布

Codex

34.5%
按下载量换算73

Claude

29.92%
按下载量换算63

Cursor

21.79%
按下载量换算46

Gemini CLI

10.17%
按下载量换算22

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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