momentic-result-classification
The momentic-result-classification skill provides tools to analyze, categorize, and explain test failures within the Momentic end-to-end testing framework. It enables users to investigate test run metadata, step-level results, and historical run data to identify root causes of failures.
Is momentic-result-classification safe to install?
Safe to install: our audit of momentic-result-classification's source files found 0 shell commands, 0 external URLs, file reads and writes (low risk). Every command and URL listed appears verbatim in the skill's source. The skill reads local test result files and metadata from the project directory. It does not execute shell commands or make external network requests.
How we audit skills: our security review methodology.
Who is this skill for?
Developers and QA engineers using the Momentic testing framework to triage and debug automated test failures.
What can you do with it?
- Categorize the root cause of a failing test run.
- Compare current test results against historical runs to identify regressions.
- Inspect specific step traces, screenshots, and DOM snapshots to diagnose state-related failures.
- Identify incorrect element locators or cache issues in test steps.
- Analyze multi-attempt runs to determine if failures are flaky or caused by application changes.
How good is this skill?
Quality score: 5/10. The skill documentation is comprehensive, providing a clear workflow, evidence standards, and specific instructions for common failure modes.
What does the skill file contain?
# 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 tes...
Frequently asked questions
How does the skill determine the root cause of a failure?
The skill follows an investigation workflow that involves identifying the earliest divergent step in a run, comparing before and after screenshots, checking DOM state, and verifying if the intended action resulted in the expected application state.
Can this skill identify flaky tests?
Yes, by analyzing historical runs and multi-attempt data. The skill requires verification of application or test changes before labeling a failure as flaky.
What information is available for each test step?
The skill can retrieve step metadata, full step traces, before and after screenshots, and DOM snapshots for specific steps.
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