proffesor-for-testing 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
# 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
# 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
# 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 ${CLAUDE_PLUGIN_DATA}/test-metrics.log:
2026-03-18|95.2|4.1|312|82.5|3
Format: date|pass_rate|flakiness_rate|duration_s|coverage_pct|failed_count
Read history for trend detection:
# Coverage trending down?
tail -5 "${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 trends