Momentic Result Classification
0Momentic Result Classification is a skill for classifying failures in Momentic test runs by analyzing results data. It's designed for testers and developers using the Momentic end-to-end testing framework.
1200Updated 2mo ago
Testing Skills
Quick InstallView Source
$ npx skills add https://github.com/momentic-ai/skills --skill momentic-result-classificationOverview
About This Skill
Momentic Result Classification helps developers and testers understand why their end-to-end tests fail by analyzing test results data and identifying likely failure causes. The skill automatically separates genuine bugs from test noise, improving the signal-to-noise ratio of your Momentic test suite. It works by processing Momentic test run results and categorizing failures to help teams focus on real issues. This is particularly useful for teams running large test suites across browser, Android, and iOS platforms who need to quickly triage failures. By integrating with Claude Code, it enables natural-language analysis of test results.
How to Use
Install using the provided npm command and follow the instructions to classify test failures.
Use Cases
Analyze test run failures
Classify failure categories
Review past test runs for context
Utilize Momentic tools for metadata
Tags
Setup & Installation
Quick Install
Terminal
$ npx skills add https://github.com/momentic-ai/skills --skill momentic-result-classificationAlternative Install (Git Clone)
git clone https://github.com/momentic-ai/skillsRequirements
- Claude Code or compatible AI agent
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Analyze the following Momentic test run results for [COMPANY]'s [PRODUCT_NAME] in the [INDUSTRY] industry. Classify each failure into one of these categories: flaky, infrastructure, logic, environment, or data. Provide a summary of root causes with confidence levels. Here are the results: [DATA].
Example Output
### Momentic Result Classification Report **Test Run ID:** TR-2024-05-17-001 **Product:** [COMPANY] Analytics Dashboard **Industry:** FinTech #### Failure Classification Summary | Failure ID | Test Case | Category | Confidence | Root Cause | |------------|-----------|----------|------------|------------| | FLR-001 | TC-452 | Logic | 95% | Incorrect rounding logic in revenue calculation | | FLR-002 | TC-478 | Infrastructure | 90% | API timeout due to server load | | FLR-003 | TC-491 | Flaky | 85% | Intermittent network latency | | FLR-004 | TC-503 | Environment | 98% | Browser version incompatibility | | FLR-005 | TC-512 | Data | 92% | Missing required test data | #### Root Cause Analysis - **Logic Failures (20%):** Primarily due to incorrect business logic implementation. - **Infrastructure Issues (30%):** Server-related problems causing timeouts. - **Flaky Tests (25%):** Network or timing issues leading to inconsistent results. - **Environment Dependencies (15%):** Browser/OS compatibility issues. - **Data Issues (10%):** Missing or corrupted test data. #### Recommendations 1. **Immediate:** Fix rounding logic in TC-452 and add retry logic for TC-478. 2. **Short-term:** Investigate flaky tests (TC-491) with network team. 3. **Long-term:** Update browser compatibility matrix and implement data validation checks.
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