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Agent Skill · 开发

code-cleanup(代码清理)

code-cleanup 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。

code-cleanup 是开发类 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,按安装量在开发类的 21,746 个 Skill 里排第 21,271。

总安装 192周安装 8GitHub Stars 1下载 64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-cleanup(代码清理)
来源仓库:https://github.com/hungv47/prod-skills
仓库路径:skills/code-cleanup
安装命令:
npx skills add https://github.com/hungv47/prod-skills --skill code-cleanup
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/hungv47/prod-skills --skill code-cleanup

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

用于处理 GitHub 仓库、Issue 和 Pull Request 等协作信息。适合围绕项目状态和代码变更进行信息整理。可通过指定仓库和技能名称进行安装使用。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。

使用前应确认权限及是否触发网络或文件读写操作。code-cleanup 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/code-cleanup

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/hungv47/prod-skills --skill code-cleanup;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-cleanup 怎么安装?

在终端执行 npx skills add https://github.com/hungv47/prod-skills --skill code-cleanup。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

code-cleanup 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-cleanup 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-cleanup 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-cleanup 用的人多吗?

公开数据是累计安装 192 次,近一周安装 8 次,下载 64 次,GitHub 1 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-cleanup 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。
  • 需要处理开发类任务、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。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
code-cleanup

代码清理 · 本页

1921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 Cleanup — Orchestrator

*Productivity — Multi-agent orchestration. Structural cleanup, code-level cleanup, and refactoring — without breaking functionality.*

Core Question: "Is this change purely structural with zero behavioral impact?"

Inputs Required

  • A codebase or set of files to clean up
  • User intent: structural reorganization, code-level cleanup, refactoring, or all three

Output

  • .agents/cleanup-report.md

Chain Position

Previous: none | Next: none (standalone)

Re-run triggers: After major feature additions, before release milestones, when test suite runtime grows significantly, or when onboarding new team members.


Multi-Agent Architecture

Agent Roster

AgentFileFocus
structural-scanner-agentagents/structural-scanner-agent.mdJunk files, empty dirs, naming conventions, structure anomalies
code-scanner-agentagents/code-scanner-agent.mdAI slop, code smells, dead code, safety issues
dependency-scanner-agentagents/dependency-scanner-agent.mdUnused packages, duplicates, security vulnerabilities
safe-removal-agentagents/safe-removal-agent.mdExecutes verified deletions with backup commits
refactoring-agentagents/refactoring-agent.mdApplies targeted refactoring without behavioral change
validation-agentagents/validation-agent.mdRuns tests, types, lint, build — reports pass/fail
critic-agentagents/critic-agent.mdGolden rules compliance, behavioral preservation review

Execution Layers

Layer 1 (parallel):
  structural-scanner-agent ───┐
  code-scanner-agent ─────────┤── scan simultaneously
  dependency-scanner-agent ───┘

Layer 2 (sequential):
  safe-removal-agent ──────────── removes verified targets from all 3 scans
    → refactoring-agent ───────── applies code-level fixes from code scanner
      → validation-agent ──────── runs all checks
        → critic-agent ─────────── final golden rules review

Dispatch Protocol

  1. Triage — determine scope from user intent:

- "Reorganize files" → structural-scanner only - "Remove AI slop" → code-scanner only - "Clean up the codebase" → all three scanners

  1. Layer 1 dispatch — send brief to relevant scanner agents in parallel.
  2. Safe removal — pass all scan results to safe-removal-agent. It creates a backup commit, then removes verified-safe targets.
  3. Refactoring — pass code scanner results + removal results to refactoring-agent. It fixes code-level issues.
  4. Validation — validation-agent runs all available checks (tests, types, lint, build).
  5. Critic review — critic-agent checks golden rules compliance. If FAIL, identify the specific change to revert.
  6. Assembly — compile cleanup report. Save to .agents/cleanup-report.md.

Routing Rules

ConditionRoute
User says "structural only"Only dispatch structural-scanner → safe-removal → validation → critic
User says "code-level only"Only dispatch code-scanner → refactoring → validation → critic
User says "refactor this"Only dispatch code-scanner → refactoring → validation → critic
User says "clean up everything"All scanners → safe-removal → refactoring → validation → critic
Validation failsIdentify which change broke it; revert that specific change
Critic PASSAssemble report and deliver
Critic FAILRevert specific change; re-run validation
Session >30 changesStop and reassess scope

Critical Gates (The 5 Golden Rules)

Before delivering, the critic-agent verifies ALL golden rules pass:

  1. Preserve behavior — Every change must produce the same observable behavior. If you can't verify this, don't make the change.
  2. Small incremental steps — One change at a time. Commit between steps. Never combine a refactor with a feature change.
  3. Check existing conventions first — Before changing anything, read the codebase's existing coding guidelines, linting config, naming patterns, and file structure. Match them.
  4. Test after each change — Run the test suite after every modification. If tests break, revert and try a smaller step.
  5. Rollback awareness — Commit before starting. Note the hash. If a change chain gets too complex, revert and try a different approach.

Additional gate: Session limits — target ~30 changes per cleanup session. After 15 changes, generate an interim summary. If each fix spawns 2+ new issues, stop and reassess.

If any golden rule fails: the critic identifies the specific change that violated it and recommends reverting.


Single-Agent Fallback

When context window is constrained or the cleanup scope is small (fewer than 5 files):

  1. Skip multi-agent dispatch
  2. Create backup commit
  3. Scan the target files for structural issues, code smells, and dead code
  4. Apply fixes one at a time, testing after each
  5. Run all available checks
  6. Verify golden rules compliance as self-review
  7. Save to .agents/cleanup-report.md

Triage

Determine scope before starting. Parts can be used independently or combined.

User intentScanners to dispatch
"Reorganize files", "remove dead code", "clean up repo structure"structural-scanner-agent
"Remove AI slop", "clean up PR", "fix code smells"code-scanner-agent
"Check dependencies", "remove unused packages"dependency-scanner-agent
"Refactor this", "extract this", "redesign this module"code-scanner-agent → refactoring-agent
"Clean up the codebase" (broad)All three scanners → safe-removal → refactoring

AI Slop Patterns

The code-scanner-agent specifically looks for these AI-generated code patterns:

Comments to remove:

  • Obvious/redundant comments explaining what code clearly does
  • Comments that don't match the commenting style elsewhere in the file
  • Section divider comments when not used elsewhere

Defensive code to remove:

  • Try/catch blocks around code that doesn't throw
  • Null/undefined checks when callers guarantee valid input
  • Type guards that duplicate earlier validation

Type issues to fix:

  • Casts to any that bypass TypeScript's type system
  • Type assertions that hide real type mismatches
  • Overly broad generic types when specific types exist

When NOT to Refactor

The refactoring-agent skips these situations:

  • No test coverage — you can't verify behavior is preserved. Write tests first.
  • Tight deadline — ship first, refactor later.
  • Code that won't change again — if nobody will read or modify it, the investment doesn't pay off.
  • During a feature change — separate commits. Always.

Anti-Patterns

Anti-PatternProblemINSTEAD
Behavioral changes disguised as cleanupObservable output changesrefactoring-agent verifies same behavior, different structure
"Tests pass so it's fine"Incomplete coverage means passing tests don't guarantee equivalencevalidation-agent flags uncovered code for manual verification
Combining cleanup with featuresOne change at a timesafe-removal and refactoring agents never add features
Removing "probably unused" codeMay be dynamically importeddependency-scanner verifies zero imports before flagging
Flagging conventions as smellsExisting patterns are intentionalcode-scanner reads surrounding code before flagging
Large batch removalsCan't identify which removal broke somethingsafe-removal-agent works in small batches, tests between each

Worked Example

User: "Clean up this Express API project, it's gotten messy after 6 months."

Triage: Broad cleanup — dispatch all three scanners.

Layer 1 (parallel):

  • structural-scanner-agent → 4 unused files in /utils, 2 duplicate helpers, naming inconsistency (userController.js vs product-controller.js)
  • code-scanner-agent → Pass 1: 0 safety issues. Pass 2: 12 TODO comments, 3 console.log, 2 commented-out blocks (>50 lines each), 5 AI slop instances
  • dependency-scanner-agent → 2 unused dependencies (lodash, moment), 1 duplicate (underscore alongside lodash)

Layer 2 (sequential):

  • safe-removal-agent → backup commit, removes 4 unused files + 2 commented blocks + lodash + underscore. Tests pass.
  • refactoring-agent → extracts shared validation into middleware/validate.js, normalizes to kebab-case, removes 12 TODOs and 3 console.logs
  • validation-agent → bun test: 47/47 pass. tsc --noEmit: clean. Lint: clean.
  • critic-agent → PASS. All 5 golden rules pass.

Artifact saved to .agents/cleanup-report.md.


Artifact Template

On re-run: rename existing artifact to cleanup-report.v[N].md and create new with incremented version.

---
skill: code-cleanup
version: 1
date: {{today}}
status: complete
---

# Cleanup Report

## Scope
[Structural / Code-Level / Refactoring / All]

## Changes Made
### Structural
### Code-Level
### Refactoring

## Validation
- Tests: [PASS/FAIL]
- Type check: [PASS/FAIL/SKIPPED]
- Lint: [PASS/FAIL/SKIPPED]
- Build: [PASS/FAIL/SKIPPED]

## Manual Verification Needed
[Features lacking test coverage]

Scripts

  • scripts/analyze_codebase.py — Static analysis tool that generates dependency reports, identifies junk files, empty directories, large directories, and potentially unused code files. Used by structural-scanner-agent and dependency-scanner-agent.

平台分布

Codex

35.49%
按下载量换算23

Claude

32.49%
按下载量换算21

Cursor

18.09%
按下载量换算12

Gemini CLI

8.79%
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

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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