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test-metrics-dashboard

Analyzes Jest test execution data to produce quality trend dashboards and top failing tests.

Updated Jun 15, 2026
Search Tags:#test metrics#test analysis#quality dashboard#test history#flakiness analysis
One-click install
npx skills add https://github.com/CENKSSS/valocase-backend --skill test-metrics-dashboard
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: test-metrics-dashboard
Source: https://github.com/CENKSSS/valocase-backend/tree/main/.claude/skills/test-metrics-dashboard
Command: npx skills add https://github.com/CENKSSS/valocase-backend --skill test-metrics-dashboard

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill helps you quickly analyze test execution history, identify flakiness rates, track Mean Time to Repair (MTTR), and build quality trend dashboards from test execution data.

Core Features & Use Cases

  • Test Health Metrics: Monitor pass rates, flakiness rates, MTTR, execution time, and coverage deltas.
  • Data Collection: Automatically export Jest results to JSON and parse them for dashboard use.
  • Trend Analysis: Compare test run metrics over time to detect changes in quality trends.
  • Top Failing Tests: Identify and investigate the most frequently failing tests.
  • Run History: Store and read dashboard data for trend detection and historical analysis.
  • Composition: Feeds into quality gate decisions, test failure investigation, and coverage drop investigation.

Quick Start

Analyze the test metrics dashboard by invoking the skill with the command: /test-metrics-dashboard

Frequently Asked Questions about test-metrics-dashboard

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I track Jest test flakiness rates over time?▼

You can track test flakiness rates by exporting Jest results to JSON and parsing the execution data to build a quality trend dashboard. This allows you to monitor pass rates and historical test health metrics.

What is Mean Time to Repair for test failures?▼

Mean Time to Repair (MTTR) is a test health metric used to track how long it takes to fix failing tests. Analyzing test execution history helps calculate MTTR and monitor quality trends over time.

How do I identify the most frequently failing tests in my test suite?▼

To identify top failing tests, you parse historical Jest results to determine which tests fail most frequently. This analysis helps pinpoint persistent flakiness issues and prioritize test failure investigations.

Can I use this test analysis approach for Jest results only?▼

Yes, the current test metrics dashboard implementation specifically requires exporting Jest results to JSON. You must use scripting for data collection to parse the results and generate the quality trend dashboard.

How do I monitor coverage deltas in my test execution data?▼

You monitor coverage deltas by storing and reading historical test run data to detect changes over time. Comparing these test run metrics allows you to observe shifts in quality trends and investigate coverage drops.

What are the limitations of using scripts for test metrics collection?▼

The primary limitation is that data collection requires custom scripting to export and parse Jest results into JSON. Additionally, historical trend detection depends on consistently storing and reading dashboard data across runs.

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