Use when querying test history, analyzing flakiness rates, tracking MTTR, or building quality trend dashboards from test execution data.
日本語の概要は準備中です。原文の説明を表示しています。
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インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Data & Analysis skill for querying test execution history, identifying trends, and surfacing actionable quality metrics.
/test-metrics-dashboard
| 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% |
# 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
# 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
# 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
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
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まだレビューはありません。使ってみた感想をお寄せください。
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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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。