/test-metrics-dashboard
Use when querying test history, analyzing flakiness rates, tracking MTTR, or building quality trend dashboards from test execution data.
$ npx -y skills add proffesor-for-testing/agentic-qe --skill test-metrics-dashboard --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/test-metrics-dashboard
Context preview
The summary Claude sees to decide when to auto-load this skill.
Use when querying test history, analyzing flakiness rates, tracking MTTR, or building quality trend dashboards from test execution data.
SKILL.md
test-metrics-dashboard.SKILL.mdname: test-metrics-dashboard
description: "Use when querying test history, analyzing flakiness rates, tracking MTTR, or building quality trend dashboards from test execution data."
user-invocable: true
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).jsonTrend 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"
doneTop 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 $5}' | sort -n | head -1Composition
Feeds into:
- **`/qe-quality-assessment`** — quality gate decisions based on metrics
- **`/test-failure-investigator`** — investigate top failing tests
- **`/coverage-drop-investigator`** — when coverage trends down
Gotchas
- Metrics without baselines are meaningless — establish baselines before tracking trends
- Flakiness rate is underreported — a test that fails 1/100 times still breaks CI weekly
- Duration trends upward over time as test count grows — set alerts on rate of increase, not absolute value
- Agent may report metrics from a single run as "trends" — need 5+ data points for meaningful trends
Read more
name: test-metrics-dashboard description: "Use when querying test history, analyzing flakiness rates, tracking MTTR, or building quality trend dashboards from test execution data." user-invocable: true
Test Metrics Dashboard
Data & Analysis skill for querying test execution history, identifying trends, and surfacing actionable quality metrics.
Activation
/test-metrics-dashboard
Key Metrics
Test Health Metrics
| Metric | Formula | Target | Alert | |--------|---------|--------|-------| | **Pass Rate** | Passed / Total | > 95% | < 90% | | **Flakiness Rate** | Flaky / Total | < 5% | > 10% | | **MTTR** | Avg time from failure to fix | < 4 hours | > 24 hours | | **Execution Time** | Total suite duration | < 10 min | > 20 min | | **Coverage Delta** | Current - Previous | >= 0% | < -2% |
Data Collection
# Export Jest results to JSON
npx jest --json --outputFile=test-results/$(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).jsonTrend 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"
doneTop 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 -1Composition
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
AI-powered quality engineering agents that generate tests, find coverage gaps, detect flaky tests, and learn your codebase patterns — across 11 coding agent platforms.
Repo: proffesor-for-testing/agentic-qe
Other skills on agentic-qe.
- /a11y-ally
Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
Open skill - /accessibility-testing
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
Open skill - /agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Open skill - /agentdb-learning
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
Open skill - /agentdb-memory-patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
Open skill - /agentdb-optimization
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
Open skill

