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momentic-result-classification即时结果分类

Agent Skill

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。

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101,352

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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).

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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

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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

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