test-metrics-dashboard
Use when querying test history, analyzing flakiness rates, tracking MTTR, or building quality trend dashboards from test execution data.
Best use case
test-metrics-dashboard is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use when querying test history, analyzing flakiness rates, tracking MTTR, or building quality trend dashboards from test execution data.
Teams using test-metrics-dashboard should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/test-metrics-dashboard/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How test-metrics-dashboard Compares
| Feature / Agent | test-metrics-dashboard | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use when querying test history, analyzing flakiness rates, tracking MTTR, or building quality trend dashboards from test execution data.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Test Metrics Dashboard
Data & Analysis skill for querying test execution history, identifying trends, and surfacing actionable quality metrics.
## Activation
```
/test-metrics-dashboard
```
## Key Metrics
### Test Health Metrics
| Metric | Formula | Target | Alert |
|--------|---------|--------|-------|
| **Pass Rate** | Passed / Total | > 95% | < 90% |
| **Flakiness Rate** | Flaky / Total | < 5% | > 10% |
| **MTTR** | Avg time from failure to fix | < 4 hours | > 24 hours |
| **Execution Time** | Total suite duration | < 10 min | > 20 min |
| **Coverage Delta** | Current - Previous | >= 0% | < -2% |
### Data Collection
```bash
# Export Jest results to JSON
npx jest --json --outputFile=test-results/$(date +%Y-%m-%d).json
# Parse results for dashboard
jq '{
date: .startTime,
total: .numTotalTests,
passed: .numPassedTests,
failed: .numFailedTests,
duration_ms: (.testResults | map(.endTime - .startTime) | add),
pass_rate: ((.numPassedTests / .numTotalTests) * 100),
flaky: [.testResults[] | select(.numPendingTests > 0)] | length
}' test-results/$(date +%Y-%m-%d).json
```
### Trend Analysis
```bash
# Compare last 5 runs
for f in $(ls -t test-results/*.json | head -5); do
jq --arg file "$f" '{
file: $file,
pass_rate: ((.numPassedTests / .numTotalTests) * 100 | floor),
duration_s: ((.testResults | map(.endTime - .startTime) | add) / 1000 | floor)
}' "$f"
done
```
### Top Failing Tests
```bash
# Find most frequently failing tests across runs
for f in test-results/*.json; do
jq -r '.testResults[] | select(.numFailingTests > 0) | .testFilePath' "$f"
done | sort | uniq -c | sort -rn | head -10
```
## Run History
Store dashboard data in `${CLAUDE_PLUGIN_DATA}/test-metrics.log`:
```
2026-03-18|95.2|4.1|312|82.5|3
```
Format: `date|pass_rate|flakiness_rate|duration_s|coverage_pct|failed_count`
Read history for trend detection:
```bash
# Coverage trending down?
tail -5 "${CLAUDE_PLUGIN_DATA}/test-metrics.log" | awk -F'|' '{print $5}' | sort -n | head -1
```
## Composition
Feeds into:
- **`/qe-quality-assessment`** — quality gate decisions based on metrics
- **`/test-failure-investigator`** — investigate top failing tests
- **`/coverage-drop-investigator`** — when coverage trends down
## Gotchas
- Metrics without baselines are meaningless — establish baselines before tracking trends
- Flakiness rate is underreported — a test that fails 1/100 times still breaks CI weekly
- Duration trends upward over time as test count grows — set alerts on rate of increase, not absolute value
- Agent may report metrics from a single run as "trends" — need 5+ data points for meaningful trendsRelated Skills
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