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。
Strategic regression testing with test selection, impact analysis, and continuous regression management. Use when verifying fixes don't break existing functionality, planning regression suites, or optimizing test execution for faster feedback.
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。
<default_to_action> When verifying changes don't break existing functionality:
Quick Regression Strategy:
Critical Success Factors:
| Strategy | How | Reduction |
|---|---|---|
| Change-based | Git diff analysis | 70-90% |
| Risk-based | Priority by impact | 50-70% |
| Historical | Frequently failing | 40-60% |
| Time-budget | Fixed time window | Variable |
// Analyze changed files and select impacted tests
function selectTests(changedFiles: string[]): string[] {
const testsToRun = new Set<string>();
for (const file of changedFiles) {
// Direct tests
testsToRun.add(`${file.replace('.ts', '.test.ts')}`);
// Dependent tests (via coverage mapping)
const dependentTests = testCoverage[file] || [];
dependentTests.forEach(t => testsToRun.add(t));
}
return Array.from(testsToRun);
}
// Example: payment.ts changed
// Runs: payment.test.ts, checkout.integration.test.ts, e2e/purchase.test.ts
# .github/workflows/regression.yml
jobs:
quick-regression:
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- name: Analyze changes
id: changes
uses: dorny/paths-filter@v2
with:
filters: |
payment:
- 'src/payment/**'
auth:
- 'src/auth/**'
- name: Run affected tests
run: npm run test:affected
- name: Smoke tests (always)
run: npm run test:smoke
nightly-regression:
if: github.event_name == 'schedule'
timeout-minutes: 120
steps:
- run: npm test -- --coverage
// Smart test selection
await Task("Regression Analysis", {
pr: 1234,
strategy: 'change-based-with-risk',
timeBudget: '15min'
}, "qe-regression-risk-analyzer");
// Returns:
// {
// mustRun: ['payment.test.ts', 'checkout.integration.test.ts'],
// shouldRun: ['order.test.ts'],
// canSkip: ['profile.test.ts', 'search.test.ts'],
// estimatedTime: '12 min',
// riskCoverage: 0.94
// }
// Generate regression test from production bug
await Task("Bug Regression Test", {
bug: { id: 'BUG-567', description: 'Checkout fails > 100 items' },
preventRecurrence: true
}, "qe-test-generator");
aqe/regression-testing/
├── test-selection/* - Impact analysis results
├── suite-health/* - Flakiness, timing trends
├── coverage-maps/* - Test-to-code mapping
└── bug-regressions/* - Tests from production bugs
const regressionFleet = await FleetManager.coordinate({
strategy: 'comprehensive-regression',
agents: [
'qe-regression-risk-analyzer', // Analyze changes, select tests
'qe-test-executor', // Execute selected tests
'qe-coverage-analyzer', // Analyze coverage gaps
'qe-quality-gate' // Go/no-go decision
],
topology: 'sequential'
});
With Agents: qe-regression-risk-analyzer provides intelligent test selection achieving 90% defect detection in 10% of execution time. Agents generate regression tests from production bugs automatically.
/test-failure-investigator to diagnose root cause/bug-reporting-excellence for proper bug reporting/risk-based-testing for risk-based prioritization--affected or file list--workers=2 and --shard for CI environments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。