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。
Comprehensive mobile testing for iOS and Android platforms including gestures, sensors, permissions, device fragmentation, and performance. Use when testing native apps, hybrid apps, or mobile web, ensuring quality across 1000+ device variants.
中文简介正在准备中。当前显示原文说明。
实测评分 未实测
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 testing mobile applications:
Quick Mobile Checklist:
Critical Success Factors:
| Aspect | iOS | Android |
|---|---|---|
| OS Versions | 2-3 supported | 10+ in use |
| Devices | ~40 models | 1000+ variants |
| Back Button | Gesture/nav | Hardware/software |
| Permissions | Single prompt | Runtime granular |
| App Store | Strict review | Google Play + sideload |
| Tier | Coverage | Devices |
|---|---|---|
| Tier 1 | 60% users | iPhone 15, Galaxy S24, iPad |
| Tier 2 | 30% users | iPhone 14/13, Pixel 8 |
| Tier 3 | 10% users | Older devices, other manufacturers |
// Offline mode testing
test('app works offline', async () => {
await driver.toggleAirplaneMode();
await driver.findElement('view-saved-items').click();
const items = await driver.findElements('saved-item');
expect(items.length).toBeGreaterThan(0);
const banner = await driver.findElement('offline-banner');
expect(banner.getText()).toContain('No internet');
await driver.toggleAirplaneMode(); // Restore
});
// Location testing
test('location-based features', async () => {
await driver.setGeoLocation({
latitude: 37.7749,
longitude: -122.4194,
altitude: 0
});
const stores = await driver.findElement('stores-list');
expect(stores.getText()).toContain('San Francisco');
});
// Permission testing (Android)
test('camera permission flow', async () => {
await driver.findElement('take-photo').click();
// Handle permission dialog
await driver.findElement(
'com.android.packageinstaller:id/permission_allow_button'
).click();
expect(await driver.findElement('camera-view')).toBeDefined();
});
// Cross-platform mobile testing
await Task("Mobile Test Suite", {
platforms: ['iOS', 'Android'],
deviceTiers: [1, 2],
tests: 'regression-suite',
parallelDevices: 5,
deviceFarm: 'browserstack'
}, "qe-test-executor");
// Device farm integration
await Task("Device Farm Execution", {
service: 'browserstack',
devices: [
'iPhone 15 - iOS 17',
'Samsung Galaxy S24 - Android 14'
],
recordVideo: true,
captureNetworkLogs: true
}, "qe-test-executor");
aqe/mobile-testing/
├── device-matrix/* - Device coverage strategy
├── platform-tests/* - iOS/Android specific tests
├── gesture-library/* - Reusable gesture patterns
└── performance/* - Mobile performance metrics
const mobileFleet = await FleetManager.coordinate({
strategy: 'mobile-testing',
agents: [
'qe-test-executor', // Cross-platform execution
'qe-performance-tester', // Mobile performance
'qe-visual-tester' // Screen size validation
],
topology: 'parallel'
});
Test on real devices for critical flows. Emulators catch 80% of bugs but real devices are needed for actual performance, sensor behavior, and platform quirks.
With Agents: qe-test-executor orchestrates testing across device farms, manages platform differences, and tests 10+ devices in parallel.
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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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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。