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Agent Skill · 前端设计

manual-testing(手动测试)

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

manual-testing 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,权限为操作浏览器;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 6,113。

总安装 552周安装 23GitHub Stars 35下载 184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:manual-testing(手动测试)
来源仓库:https://github.com/petekp/claude-code-setup
仓库路径:skills/manual-testing
安装命令:
npx skills add https://github.com/petekp/claude-code-setup --skill manual-testing
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/petekp/claude-code-setup --skill manual-testing

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。

用于辅助测试设计、用例整理和回归验证。适合编写单元测试、端到端测试或定位失败日志。需要确认项目测试框架和运行命令。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。涉及浏览器服务时应区分本地模拟和测试环境。

manual-testing 属于前端设计类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/manual-testing

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/petekp/claude-code-setup --skill manual-testin…;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

    它需要操作浏览器,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

manual-testing 怎么安装?

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

manual-testing 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。

manual-testing 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

manual-testing 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。

manual-testing 用的人多吗?

公开数据是累计安装 552 次,近一周安装 23 次,下载 184 次,GitHub 35 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。

manual-testing 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。
  • 需要处理前端设计类任务、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。

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
manual-testing

手动测试 · 本页

55235操作浏览器未明确通过

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

Manual Testing

Finish work with a tight verification loop: prove everything possible with tools first, then ask the user to verify only what requires human eyes, hands, devices, or judgment.

Do not make the user invent the test plan. Lead them through it.

Quality Bar

Before asking the user to do anything, know:

  • What changed
  • What the expected behavior is
  • What can be verified automatically
  • What still needs a human check
  • What nearby behavior could have regressed

Never ask the user to "poke around" or "let me know if it works." Give concrete actions, a specific screen or command, the expected result, and a short set of likely outcomes to reply with.

Workflow

1. Build a Verification Matrix

Translate the change into a short verification plan before running anything.

For each changed behavior, capture:

  • Primary success path
  • Most likely failure or edge case
  • One nearby regression check
  • Verification owner: tool or user

Use a simple internal checklist like:

BehaviorHappy pathEdge/regressionVerified by
Save settingsForm savesValidation error still worksTool + user

Keep the matrix small and focused on the current change.

2. Run Tool-Verifiable Checks First

Exhaust automated verification before involving the user.

Prefer to verify these yourself:

  • Build, compile, typecheck, lint, test
  • API responses and status codes
  • File output, database state, logs, and side effects
  • CLI behavior, exit codes, and generated artifacts
  • Browser automation, screenshots, DOM text, or network behavior when tools can prove it

Only hand work to the user when the result depends on:

  • Visual correctness
  • Motion, timing, or feel
  • Real-device behavior
  • Cross-browser differences
  • Screen reader behavior
  • Third-party flows that require human interaction

If an automated check fails, stop and address it before asking for manual verification.

3. Prepare the User Path

Set the user up so they can perform the check with minimal effort.

Provide:

  • Exact route, URL, screen, or command
  • Any required setup state
  • The single action to take
  • The expected result
  • What to reply with

If the user needs an already-running app, point them to the exact place to open. If you can safely prepare state, data, or fixtures first, do that yourself.

4. Lead the User Through Atomic Steps

Run manual verification as a guided sequence, not a dump of vague instructions.

Prefer one atomic step at a time. For a tiny smoke test, bundle at most 2-3 closely related checks.

Use this structure:

Testing: [feature or fix]
Progress: Step N of M

Action: [exact thing to click, type, or inspect]
Expected: [what should happen]
Reply with one:
1. [expected outcome]
2. [common failure mode]
3. [second common failure mode]
4. Other

If structured question tools are available, convert those reply options into a structured prompt. Otherwise ask the question in plain text with the options inline.

Example:

Testing: profile photo upload
Progress: Step 2 of 3

Action: Open `/settings/profile`, upload a PNG under 2 MB, and wait for the save state to finish.
Expected: The new avatar appears in the header and no error message is shown.
Reply with one:
1. Upload worked and the new avatar is visible
2. Upload finished but the avatar did not update
3. I saw an error message or spinner got stuck
4. Other

5. Cover the Right Surface Area

Always test the changed path first, then cover the most likely place it could fail.

Use these prompts as a calibration checklist.

For UI changes:

  • Check initial render
  • Check loading, empty, error, disabled, and success states when relevant
  • Check keyboard/focus path for interactive controls
  • Check mobile or narrow-width layout if the change is layout-sensitive
  • Check copy, spacing, and obvious visual regressions

For bug fixes:

  • Reproduce the original bug path
  • Verify the bug no longer occurs
  • Verify a nearby path still behaves correctly

For API or backend changes:

  • Verify happy-path response
  • Verify invalid-input or failure-path behavior
  • Verify the persisted side effect or downstream state change
  • Verify logs or errors do not show new breakage

For CLI or local-tool changes:

  • Verify the success path
  • Verify a common failure path and exit code
  • Verify output files, stdout/stderr, and help text when relevant

6. Handle Failures Like a Debugger

When the user reports a problem:

  • Capture the exact step that failed
  • Record expected versus actual behavior
  • Note any visible error text, logs, or screenshots available
  • Decide whether to stop and investigate immediately or finish the remaining checks only if that still adds value

Before changing code, generate 2-3 plausible hypotheses for the failure so the next debugging step is deliberate instead of guess-driven.

If the failure blocks confidence in the change, stop the manual test and switch into diagnosis.

7. Summarize With Confidence and Gaps

Close with a short verification summary that separates what is proven from what is still assumed.

Include:

  • Automated checks run and their results
  • Manual steps completed and their results
  • Bugs or regressions found
  • Remaining unverified areas
  • Recommended next action

Guidelines

  • Minimize user effort; maximize agent effort
  • Keep the user in one context at a time
  • Prefer concrete reply options over open-ended questions
  • Check one happy path, one failure path, and one nearby regression when practical
  • Use tools aggressively before asking for human verification
  • Keep the running narrative clear so the user remembers what is being tested and what has already passed

平台分布

Codex

33.95%
按下载量换算62

Claude

33.4%
按下载量换算61

Cursor

18.18%
按下载量换算33

Gemini CLI

8.93%
按下载量换算16

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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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