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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Cross-browser, cross-platform, and cross-device compatibility testing ensuring consistent experience across environments. Use when validating browser support, testing responsive design, or ensuring platform compatibility.
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。
Browser-driven checks (viewport emulation, responsive validation, cross-browser screenshots) should go through the qe-browser fleet skill (.claude/skills/qe-browser/). Vibium is installed by aqe init. Quick reference:
# Viewport emulation per breakpoint
vibium viewport 375 667 # mobile
vibium viewport 768 1024 # tablet
vibium viewport 1920 1080 # desktop
# Full device emulation (user-agent, DPR, touch)
vibium emulate-device "iPhone 15"
# Visual diff per viewport
for vp in "375 667 mobile" "768 1024 tablet" "1920 1080 desktop"; do
read w h name <<< "$vp"
vibium viewport $w $h
node .claude/skills/qe-browser/scripts/visual-diff.js --name "homepage-$name"
done
Cross-browser (Firefox/Safari) still requires Playwright or a cloud device farm today — Vibium's BiDi backend is Chrome-only at v26.3.x.
<default_to_action> When validating cross-browser/platform compatibility:
Quick Compatibility Checklist:
Critical Success Factors:
// playwright.config.ts
import { defineConfig, devices } from '@playwright/test';
export default defineConfig({
projects: [
{ name: 'chromium', use: { ...devices['Desktop Chrome'] } },
{ name: 'firefox', use: { ...devices['Desktop Firefox'] } },
{ name: 'webkit', use: { ...devices['Desktop Safari'] } },
{ name: 'mobile-chrome', use: { ...devices['Pixel 5'] } },
{ name: 'mobile-safari', use: { ...devices['iPhone 12'] } }
]
});
// Run: npx playwright test --project=chromium --project=firefox
// BrowserStack configuration
const capabilities = {
'browserName': 'Chrome',
'browser_version': '118.0',
'os': 'Windows',
'os_version': '11',
'browserstack.user': process.env.BROWSERSTACK_USER,
'browserstack.key': process.env.BROWSERSTACK_KEY
};
// Parallel execution across devices
const deviceMatrix = [
{ os: 'Windows', browser: 'Chrome' },
{ os: 'OS X', browser: 'Safari' },
{ os: 'Android', device: 'Samsung Galaxy S24' },
{ os: 'iOS', device: 'iPhone 15' }
];
// Cross-platform visual comparison
await Task("Compatibility Testing", {
url: 'https://example.com',
browsers: ['chrome', 'firefox', 'safari', 'edge'],
devices: ['desktop', 'tablet', 'mobile'],
platform: 'browserstack',
parallel: true
}, "qe-visual-tester");
// Returns:
// {
// combinations: 12, // 4 browsers × 3 devices
// passed: 11,
// differences: [{ browser: 'safari', device: 'mobile', diff: 0.02 }]
// }
aqe/compatibility-testing/
├── browser-matrix/* - Browser/version configurations
├── device-matrix/* - Device configurations
├── visual-diffs/* - Cross-browser visual differences
└── reports/* - Compatibility reports
const compatFleet = await FleetManager.coordinate({
strategy: 'compatibility-testing',
agents: [
'qe-visual-tester', // Visual comparison
'qe-test-executor', // Cross-browser execution
'qe-performance-tester' // Performance by platform
],
topology: 'parallel'
});
Cover 95%+ of your user base. Use analytics to identify actual browser/device usage. Don't waste time on browsers nobody uses.
With Agents: Agents orchestrate parallel cross-browser testing across cloud platforms. qe-visual-tester catches visual inconsistencies across platforms automatically.
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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。