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Agent Skill · 研究检索

momentic-result-classification(即时结果分类)

momentic-result-classification 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。

momentic-result-classification 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,权限为操作浏览器;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 1,307。

总安装 101,352周安装 4,145GitHub Stars 11下载 31,816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:momentic-result-classification(即时结果分类)
来源仓库:https://github.com/momentic-ai/skills
仓库路径:skills/momentic-result-classification
安装命令:
npx skills add https://github.com/momentic-ai/skills --skill momentic-result-classification
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/momentic-ai/skills --skill momentic-result-classification

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。

momentic-result-classification 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。适用于需要根据关键词或任务场景进行信息检索的场景。通过 npx skills add 命令从 GitHub 仓库安装使用。

建议确认权限范围和维护状态,避免触发联网或文件读写操作。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。

来源仓库内路径:skills/momentic-result-classification

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/momentic-ai/skills --skill momentic-result-cla…;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

momentic-result-classification 怎么安装?

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

momentic-result-classification 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。

momentic-result-classification 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。

momentic-result-classification 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。

momentic-result-classification 用的人多吗?

公开数据是累计安装 101,352 次,近一周安装 4,145 次,下载 31,816 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。

momentic-result-classification 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
momentic-result-classification

即时结果分类 · 本页

101,35211操作浏览器未明确有提醒

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

Momentic result classification (MCP)

Momentic is an end-to-end testing framework where each test is composed of browser interaction steps. Each step combines Momentic-specific behavior (AI checks, natural-language locators, ai actions, etc.) with Playwright capabilities wrapped in our YAML step schema. When these tests are run, they produce results data that can be used to analyze the outcome of the test. The results data contains metadata about the run as well as any assets generated by the run (e.g. screenshots, logs, network requests, video recordings, etc.). Your job is to use these test results to classify failures that occurred in Momentic test runs.

Instructions

  1. Given a failing test run, analyze why the test run failed. Often you'll need to look beyond the current run to understand this, looking at past runs of the same test, or other context provided by the Momentic MCP tools
  2. After analyzing why the run failed, bucket the failure into one of the below categories, explaining the reasoning for choosing the specific category.

Helpful MCP tools

momentic_get_run — Returns some metadata about the run and the path to the full run results. Use the metadata to help you parse through the run results (e.g. which attempt to look at, which step failed, etc.)

momentic_list_runs — Recent runs for a test so you can compare the result of past runs over time. Always pass gitBranchName when it exists on the run in question so that it's more likely you're looking at the same version of the test.

Background

Test run result structure

When momentic tests are run via the CLI, the results are stored in a "run group". The data for this run group is stored in a single directory within the momentic project. By default, the directory is called test-results, but can be changed in momentic project settings or on a single run of a run group. The run group results folder has the following structure:

test-results/
├── metadata.json         data about the run group, including git metadata and timing info.
└── runs/                 On zip for each test run in the run group.
    ├── <runId_1>.zip         a zipped run directory containing data about this specific test run.  Follows the structure described below.
    └── <runId_2>.zip

When unzipped, run directories have the following structure:

<runId>/
├── metadata.json           run-level metadata.
└── attempts/<n>/           one folder per attempt (1-based n).
    ├── metadata.json       attempt outcome and step results.
    ├── console.json        optional browser console output.
    └── assets/
        ├── <snapshotId>.jpeg     before/after screenshot for each step (see attempt metadata.json for snapshot ID).
        ├── <snapshotId>.html     before/after DOM snapshot for each step (see attempt metadata.json for snapshot ID).
        ├── har-pages.log         HAR pages (ndjson).
        ├── har-entries.log       HAR network entries (ndjson).
        ├── resource-usage.ndjson CPU/memory samples taken during the attempt.
        ├── <videoName>           video recording (when video recording is enabled).
        └── browser-crash.zip     browser crash dump (only present on crash).

When getting run results via the momentic MCP, tools such as momentic_get_run will return links to the MCP working directory (default .momentic-mcp). This directory will contain unzipped run result folders, following the structure above, named run-result-<runId>.

Element locators

Certain step types that interact with elements have a "target" property, or locator, that specifies which element the step should interact with.

Locator caches

Locators identify elements by sending the page state html/xml to an llm as well as a screenshot. The llm identifies which element on the page the user is referring to. Momentic will attempt to "cache" the answer from the llm so that future runs don't require AI calls. On future runs, the page state is checked against the cached element to determine whether the element is still usable, or the page has changed enough such that another AI call is required.

A locator cache can bust for a variety of reasons:

  • the element description has changed, in which case we'll always bust the cache
  • the cached element could not be located in the current page state
  • the cached element was located in the page state, but fails certain checks specified on the cache entry, such as requiring a certain position, shape, or content.

You can find the cacheBustReason on the trace property in the results for a given step. The cache property is also listed on the results, showing the full cache saved for that element.

Identifying bad caches

Sometimes the element that was cached is not the element that the user intended to target. This can cause failures or unexpected behaviors in tests. In these cases, it helps to verify exactly why the wrong cache was saved in the first place. Use the runId property of the targetUpdateLoggerTags on the incorrect cache to get the details of the original run, calling momentic_get_run with this runId. This will return the run where the cache target was updated.

Using past runs

You MUST look at past runs of the same test when understanding why a test failed. Looking at past runs helps you identify:

  • When did this test start failing?
  • What differed vs the last passing run?
  • Did the same action behave differently on an earlier run?

Use step results and screenshots on past runs to answer these questions. Do NOT rely only on summaries from momentic_get_run or momentic_list_runs to understand what happened in a test run. You MUST look at the specific run details, including step results and screenshots, to determine the behavior of past runs.

When looking at past runs, use the following workflow:

  1. Call the momentic_list_runs tool to identify the runs you want more detail on.
  2. Call momentic_get_run for that specific run to get the run details.

ALWAYS look at screenshots when determining the behavior of test runs.

Multi-attempt runs

When momentic_list_runs shows a passing run with attempts > 1, treat it as a partial failure worth investigating, not a clean passing run. Pull the first attempt's step results and failure messages to understand what was going wrong before the retry succeeded.

Flakiness and intermittent failures

  • In order to consider a test flaky or failing intermittently, it must be intermittently failing for the same app and test behavior.

- Just because a test failed once does NOT mean that it's flaky - it could have failed because of an application change. You need to determine whether or not there was an application or test change between runs by analyzing the screenshots and/or browser state in the results. - IMPORTANT: You cannot make assumptions about flakiness or intermittent failures without verifying whether there was an application or test change that caused the failure

Test temporality

  • Any past results may not necessarily match today’s test file. The test may have changed, meaning the result was on a different version of the test.
  • Looking at the simplifiedTestSteps property in the response from momentic_get_run can help you determine whether the test has changed.
  • For specific step configuration details, look at the stepsSnapshot property of the full run results.

Identifying related vs unrelated issues

  • Use test name, description, and the simplifiedTestSteps property on the response from momentic_get_run to determine what the test is intending to verify
  • Failures outside that intent are unrelated, otherwise consider them related.
  • Any failures in setup or teardown steps are pretty much always considered unrelated

Bug vs change

  • Bug: something very clearly went wrong when it shouldn't have, such as an error message appearing. It's obvious just by looking at a single step or two that this is a bug.
  • Change: any other behavior changes in the application

Formal classification output

  • Exactly one category id — no new labels, no multi-label.
  • Ground your decision in data. Be sure that you've fully investigated the run before assigning the category.
Reasoning: <a few sentences tied to summary, past runs, and intent>
Category: <one id from the list>

Category ids

Use these strings verbatim:

  • NO_FAILURE — Nothing failed; all attempts passed.
  • RELATED_APPLICATION_CHANGE — Related to intent; expectation drift / change, not a clear defect.
  • RELATED_APPLICATION_BUG — Related to intent; clearly incorrect behavior.
  • UNRELATED_APPLICATION_CHANGE — Outside intent; not a clear bug.
  • UNRELATED_APPLICATION_BUG — Outside intent but clearly broken.
  • TEST_CAN_BE_IMPROVED — Test/automation issue (race, vague locator or assertion).
  • INFRA — Rare or external (browser crash, resource pressure, rate limits, flaky environment).
  • PERFORMANCE — Load/responsiveness (stuck spinner, assertion timeouts) when not pure infra.
  • MOMENTIC_ISSUE — There was an issue with momentic itself, the platform running the test (e.g. an AI hallucination, data issues, incorrectly redirecting to the wrong element).

平台分布

Codex

36.33%
按下载量换算11,559

Claude

30.29%
按下载量换算9,637

Cursor

20.43%
按下载量换算6,500

Gemini CLI

10.91%
按下载量换算3,471

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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。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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