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.
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Design and implement effective test automation with proper pyramid, patterns, and CI/CD integration. Use when building automation frameworks or improving test efficiency.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
<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.
中文简介正在准备中。当前显示原文说明。
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.
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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.
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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.
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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.
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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.
中文简介正在准备中。当前显示原文说明。