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。
Use when a test is failing and you need to determine root cause: is it flaky, an environment issue, or a real regression? Traces failure from symptom to fix.
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。
実行スコア 未検証
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。
Runbook-style skill for systematic test failure investigation. Given a failing test, determines root cause and recommends action.
/test-failure-investigator [test-name-or-file]
Run the test 3 times and classify:
| Result Pattern | Classification | Action |
|---|---|---|
| Fails consistently | Regression or Environment | Continue to Step 2 |
| Fails intermittently | Flaky | Skip to Step 4 |
| Passes now | Transient | Check CI logs, environment diff |
# Run test 3 times
for i in 1 2 3; do npx jest {{test_file}} 2>&1 | tail -5; echo "--- Run $i ---"; done
# When did it start failing?
git log --oneline -20 -- {{related_source_files}}
# What changed recently?
git diff HEAD~5 -- {{related_source_files}}
# Does it fail in isolation?
npx jest {{test_file}} --testNamePattern="{{test_name}}"
# Does it fail with other tests?
npx jest --runInBand # sequential execution
| Symptom | Likely Cause | Investigation |
|---|---|---|
| Timeout | Network/DB dependency | Check external service availability |
| Assertion mismatch | Logic change | Compare expected vs actual, check git blame |
| Import error | Dependency change | Check package.json changes, run npm ci |
| Permission denied | Environment | Check file permissions, Docker volumes |
| Out of memory | Resource leak | Profile with --detectOpenHandles |
# Run 10 times to confirm flakiness
for i in $(seq 1 10); do npx jest {{test_file}} --forceExit 2>&1 | grep -E 'PASS|FAIL'; done
# Common flaky causes:
# - Shared state between tests (missing cleanup)
# - Time-dependent assertions (use fake timers)
# - Race conditions (missing await)
# - Port conflicts (use random ports)
# - Order dependency (run with --randomize)
## Test Failure Report
- **Test**: {{test_name}}
- **File**: {{test_file}}
- **Classification**: Regression / Flaky / Environment / Transient
- **Root Cause**: {{description}}
- **First Failed**: {{commit_hash}} ({{date}})
- **Fix**: {{recommended_action}}
- **Verified**: [ ] Fix applied and test passes 3x consecutively
After investigation, compose with:
/bug-reporting-excellence — if regression found, file a bug report/regression-testing — if regression, add to regression suite/qe-test-execution — for re-running tests after fix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。
ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。
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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。