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。
Design and implement effective test automation with proper pyramid, patterns, and CI/CD integration. Use when building automation frameworks or improving test efficiency.
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 designing or improving test automation:
Quick Anti-Pattern Detection:
| Problem | Symptom | Fix |
|---|---|---|
| Ice cream cone | 80% E2E, 10% unit | Invert pyramid |
| Slow suite | 30+ min CI | Parallelize, prune |
| Flaky tests | Random failures | Quarantine, fix timing |
| Coupled tests | Order-dependent | Isolate data |
| Brittle selectors | Break on CSS change | Use data-testid |
name: Test Pipeline
on: [push, pull_request]
jobs:
unit-tests:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- run: npm ci
- run: npm run test:unit -- --coverage
timeout-minutes: 5
- uses: codecov/codecov-action@v3
integration-tests:
needs: unit-tests
runs-on: ubuntu-latest
services:
postgres:
image: postgres:15
steps:
- run: npm run test:integration
timeout-minutes: 10
e2e-tests:
needs: integration-tests
runs-on: ubuntu-latest
steps:
- run: npx playwright test
timeout-minutes: 15
// Quarantine flaky tests
describe.skip('Quarantined - INC-123', () => {
test('flaky test awaiting fix', () => { /* ... */ });
});
// Agent-assisted stabilization
await Task("Fix Flaky Tests", {
tests: quarantinedTests,
analysis: ['timing-issues', 'data-isolation', 'race-conditions'],
strategies: ['add-waits', 'isolate-fixtures', 'mock-externals']
}, "qe-flaky-test-hunter");
// Generate tests following pyramid
await Task("Generate Test Suite", {
sourceCode: 'src/',
pyramid: { unit: 70, integration: 20, e2e: 10 },
patterns: ['page-object', 'builder', 'factory'],
framework: 'jest'
}, "qe-test-generator");
// Optimize test execution
await Task("Optimize Suite", {
algorithm: 'johnson-lindenstrauss',
targetReduction: 0.3,
maintainCoverage: 0.95
}, "qe-regression-risk-analyzer");
// Analyze flaky patterns
await Task("Flaky Analysis", {
testHistory: 'last-30-days',
detectPatterns: ['timing', 'data', 'environment'],
recommend: 'stabilization-strategy'
}, "qe-flaky-test-hunter");
aqe/automation/
├── test-pyramid/* - Coverage by layer
├── page-objects/* - Shared page objects
├── flaky-registry/* - Quarantined tests
└── execution-metrics/* - Suite performance data
const automationFleet = await FleetManager.coordinate({
strategy: 'test-automation',
agents: [
'qe-test-generator', // Generate pyramid-compliant tests
'qe-test-executor', // Parallel execution
'qe-coverage-analyzer', // Coverage gaps
'qe-flaky-test-hunter', // Flaky detection
'qe-regression-risk-analyzer' // Smart selection
],
topology: 'hierarchical'
});
With Agents: Agents generate pyramid-compliant tests, detect flaky patterns, optimize execution time, and maintain test infrastructure. Use agents to scale automation quality.
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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。