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Agent Skill · 研究检索

test-with-meticulous(一丝不苟地测试)

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。

test-with-meticulous 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,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 59,696。

总安装 380周安装 16GitHub Stars 1下载 133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:test-with-meticulous(一丝不苟地测试)
来源仓库:https://github.com/alwaysmeticulous/skills
仓库路径:skills/test-with-meticulous
安装命令:
npx skills add https://github.com/alwaysmeticulous/skills --skill test-with-meticulous
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/alwaysmeticulous/skills --skill test-with-meticulous

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。

用于执行细致入微的自动化测试与回归验证。test-with-meticulous 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适合逐行检查代码逻辑、边界条件与异常处理路径。使用时应配合详细日志输出,便于追踪执行轨迹。避免仅关注表面通过率,需深入验证内部状态正确性。

安装方式:通过 GitHub 仓库安装,支持 Codex、Claude、Cursor、Gemini CLI。

来源仓库内路径:skills/test-with-meticulous

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/alwaysmeticulous/skills --skill test-with-meti…;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

test-with-meticulous 怎么安装?

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

test-with-meticulous 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。

test-with-meticulous 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。

test-with-meticulous 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。

test-with-meticulous 用的人多吗?

公开数据是累计安装 380 次,近一周安装 16 次,下载 133 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。

test-with-meticulous 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

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。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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
test-with-meticulous

一丝不苟地测试 · 本页

3801需要联网未明确有提醒

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

To test and debug frontend changes using Meticulous, follow the workflow below step by step, using the CLI commands as described.

Step 1 -- Trigger Meticulous test run

If you are already given a test run id, then skip this step.

  1. If possible, find out what build artefact Meticulous expects by checking .github/workflows/ for one of the following steps:

- uses: alwaysmeticulous/report-diffs-action/upload-assets@v1 - build assets - uses: alwaysmeticulous/report-diffs-action/upload-container@v1 - docker image

  1. Build the frontend.
  2. Upload the build artefact and trigger a Meticulous test run:
# upload-assets
npx @alwaysmeticulous/cli ci upload-assets --appDirectory <path-to-build> --repoDirectory <path-to-repo> --waitForTestRunToComplete

# upload-container
npx @alwaysmeticulous/cli ci upload-container --localImageTag <image-tag> --repoDirectory <path-to-repo> --waitForTestRunToComplete

--appDirectory must point to the build output directory (e.g., dist/ subfolder), whereas --localImageTag must point to the local Docker image tag. The --repoDirectory must point to the root of the git repository (e.g., .). The --waitForTestRunToComplete flag is required.

The command will finish with status Failure if visual differences have been detected.

Step 2 -- Assess visual frontend changes

Get an overview of all diffs, then visually inspect representative screenshots to cover all changes. The domDiffIds from Step 2a tell you which screenshots share the same structural DOM change — pick one representative per unique diff ID to efficiently cover all changes. For each representative, always look at the screenshot images first (Step 2b) — the diff image is the most informative way to understand what actually changed. Use the DOM diff (Step 2c) for additional structural detail, and the timeline (Step 2d) only when a diff is unexpected and not explained by the DOM or images. The final report should cover all significant visual changes: each visual change deserves its own explanation.

Step 2a -- Get the replay diff summary

npx @alwaysmeticulous/cli agent test-run-diffs --testRunId <testRunId>

Output format: TSV on stdout, metadata on stderr.

stdout columns:

replayDiffId	screenshotName	index	total	outcome	mismatch	domDiffIds

Example output:

CqctwLpPC7	after-event-0	1	5	diff	0.00234	1;3
RRMGQft7PD	after-event-174	3	8	diff	0.01050	1;2
CLkCJ8WLrJ	after-event-8	2	4	diff	0.00100	none
Ct8HwmJNzM	end-state	5	5	flake	0.00010	error
Ab3xKLmN9Q	after-event-12	3	6	missing-base	0.00000	n/a

Each row represents a screenshot where a visual pixel difference was detected between the base (before) and head (after) replay. Rows with outcome=diff are confirmed visual differences; other outcomes (flake, error, warning, missing-base, missing-head) are informational.

  • outcome: diff (visual pixel difference), flake, error, warning, missing-base, missing-head
  • mismatch (0-1, 5 decimal places) is the pixel mismatch fraction
  • domDiffIds is a semicolon-separated ordered list of diff IDs, one per independent DOM change in the screenshot. Each ID groups structurally identical DOM changes across screenshots (same ID = same structural change). Example: 1;3 means two independent DOM changes with IDs 1 and 3. Special values: none means no DOM changes were found (either computed successfully with no differences, or a matching screenshot where no diff is expected) -- the visual difference is purely pixel-level (e.g. anti-aliasing, rendering differences), so you must inspect the screenshot images to understand the change. n/a means the DOM diff is not applicable (e.g. error/warning outcomes where no comparison is possible). error means the DOM diff was attempted but failed (e.g. metadata unavailable or could not be retrieved).

stderr shows: total counts, unique diff counts, and timing breakdown. Proceed to Steps 2b-2c for rows with outcome=diff.

Use domDiffIds to identify which subset of diffs to inspect. Screenshots sharing the same ID contain the same structural DOM change — pick one representative per unique ID for efficient coverage.

Step 2b -- Get screenshot images

For each representative screenshot:

npx @alwaysmeticulous/cli agent image-files --replayDiffId <replayDiffId> --screenshotName <screenshotName>

This downloads the screenshot images to ~/.meticulous/agent-images/ and prints the local file paths.

Output format:

outcome: <outcome>
screenshot: <path>          # present for missing-base/missing-head
before: <path>              # present for diff/no-diff
after: <path>
diffImage: <path>

Open the before, after, and diffImage files to visually inspect the change. The diffImage is usually the most informative — it highlights exactly which pixels changed. Always inspect the images to understand the actual visual impact of a change, even when the DOM diff is clear.

Alternative: use image-urls instead of image-files to get URLs to the images rather than downloading them locally.

Step 2c -- Inspect the DOM diff (for structural detail)

npx @alwaysmeticulous/cli agent dom-diff --replayDiffId <replayDiffId> --screenshotName <screenshotName>

Optional: pass --context <N|full> to control how many context lines surround each hunk (default 3). Use --context 0 for no context, or --context full for a single unified diff with full file context.

Output format: Unified diff (+/- format) with leading indentation stripped. All diff blocks are separated by [diff 0], [diff 1], etc. headers. Example:

[diff 0]
 <span class="text-zinc-400">#7687</span>
-<span class="min-w-0 flex-1 truncate transition-colors">Use divergence-aware comparison</span>
+<span class="min-w-0 flex-1 truncate transition-colors" data-tooltip-id=":r1h:">Use divergence-aware comparison</span>
[diff 1]
 <span class="inline-flex items-center rounded-lg bg-zinc-800">Temporal Workflow</span></a>
+<a href="/projects/Foo/Bar/test-runs/abc123"><span class="inline-flex items-center rounded-lg bg-zinc-800">Original: abc123</span></a>

To view a single diff block, add --index <0-based index> (maps to the position in the domDiffIds list from Step 2a).

Step 2d -- Get the replay timeline (optional, for diagnosing unexpected diffs)

If a diff is unexpected and the images/DOM don't make it obvious why it happened:

npx @alwaysmeticulous/cli agent timeline-diff --replayDiffId <replayDiffId>

Output format: TSV on stdout, replay IDs on stderr.

stdout columns:

diff	timeMs	event	description
  • diff column: ` (identical), - (removed), + (added), !` (changed)
  • event types: user, screenshot, network, console, debug, urlChange, error, fatalError, etc.
  • description: concise one-line summary of the event

Look for anomalies such as failed network requests, unexpected redirects, or timing-related differences that could explain a visual change.

Decision guide

For each representative screenshot, classify the visual change as intended or unintended based on the diff image and DOM diff:

  • Intended: The visual change is a desired outcome of the task you're working on. Confirm and move on.
  • Unintended: The change was not a goal of the task. This includes both changes that are clearly unrelated to your code, and — often more importantly — side effects of your code changes that weren't meant to happen. A change being *explainable* by your code does not make it *intended*; if the task didn't call for that visual change, it's unintended.

For unintended changes:

  1. If the change is a side effect of your code, attempt to fix it so the code achieves the intended result without the unwanted visual change, then re-run the test.
  2. Use the timeline (Step 2d) to check for failed network requests, redirects, or other anomalies that could explain diffs unrelated to your code.
  3. If you can confidently explain the cause (e.g. a flaky timestamp, a non-deterministic element), note the explanation.
  4. If you cannot explain or fix it, flag it to the user.

Final report

After investigating all diffs and attempting fixes for any fixable issues, produce a summary that covers all significant visual changes. The number of explanation points should be at least as many as the number of unique domDiffIds you inspected — each visual change deserves its own explanation.

  1. Intended changes: For each distinct visual change that is a desired outcome of the task, describe what changed visually (based on the diff image) and why it's intended.
  2. Unintended changes (if any): For each, include:

- A representative replayDiffId / screenshotName - What the visual change looks like (e.g. "new badge element added", "layout shift in header") - Whether it's a side effect of your code or unrelated, and your best assessment of the cause

平台分布

Codex

37.75%
按下载量换算50

Claude

28.92%
按下载量换算38

Cursor

18.69%
按下载量换算25

Gemini CLI

9.91%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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