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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Captures and compares screenshots across viewports, runs axe-core accessibility scans, and detects visual regressions with pixel-diff analysis. Use when detecting UI regressions, validating responsive layouts, testing WCAG compliance, or ensuring visual consistency after CSS or component changes.
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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。
Guide the use of v3's visual and accessibility testing capabilities including screenshot comparison, responsive design validation, and WCAG 2.2 compliance verification.
# Visual regression test
aqe visual test --baseline production --current staging
# Responsive design test
aqe visual responsive --url https://example.com --viewports all
# Accessibility audit
aqe a11y audit --url https://example.com --standard wcag22-aa
# Cross-browser test
aqe visual cross-browser --url https://example.com --browsers chrome,firefox,safari
// Visual regression testing
Task("Run visual regression", `
Compare staging against production:
- Capture screenshots of key pages
- Detect pixel differences
- Flag significant visual changes
- Generate visual diff report
`, "qe-visual-tester")
// Accessibility audit
Task("Audit accessibility", `
Run WCAG 2.2 AA compliance audit:
- Check color contrast ratios
- Verify keyboard navigation
- Test screen reader compatibility
- Validate ARIA labels
Generate compliance report with fix suggestions.
`, "qe-accessibility-agent")
All browser automation in this skill uses the qe-browser fleet skill (Vibium engine). See .claude/skills/qe-browser/SKILL.md. The vibium binary is installed by aqe init.
# Establish baselines for the pages we care about
for path in / /login /dashboard /settings; do
slug=$(echo "$path" | tr '/' '_' | sed 's/^_//' || echo root)
vibium go "https://production.example.com$path" && vibium wait load
node .claude/skills/qe-browser/scripts/visual-diff.js --name "baseline_${slug:-root}"
done
# Compare staging against those baselines
for path in / /login /dashboard /settings; do
slug=$(echo "$path" | tr '/' '_' | sed 's/^_//' || echo root)
vibium go "https://staging.example.com$path" && vibium wait load
node .claude/skills/qe-browser/scripts/visual-diff.js \
--name "baseline_${slug:-root}" --threshold 0.001 # 0.1% pixel diff
done
Ignore dynamic regions (timestamps, live counts) by scoping the diff to a selector that excludes them:
node .claude/skills/qe-browser/scripts/visual-diff.js \
--name hero --selector "main > .content"
Legacy programmatic TypeScript API (still available for tests that prefer it over shelling out):
await visualTester.compareScreenshots({
baseline: {
source: 'production',
pages: ['/', '/login', '/dashboard', '/settings']
},
current: {
source: 'staging',
pages: ['/', '/login', '/dashboard', '/settings']
},
comparison: {
threshold: 0.1, // 0.1% pixel difference
antialiasing: true,
ignoreRegions: ['#dynamic-content', '.timestamp']
}
});
await responsiveTester.test({
url: 'https://example.com',
viewports: [
{ name: 'mobile', width: 375, height: 667 },
{ name: 'tablet', width: 768, height: 1024 },
{ name: 'desktop', width: 1920, height: 1080 }
],
checks: {
layoutShift: true,
contentOverflow: true,
touchTargets: true,
fontScaling: true
}
});
await accessibilityAgent.audit({
url: 'https://example.com',
standard: 'WCAG22-AA',
checks: {
perceivable: {
colorContrast: true,
textAlternatives: true,
captions: true
},
operable: {
keyboardAccessible: true,
noTimingIssues: true,
navigable: true
},
understandable: {
readable: true,
predictable: true,
inputAssistance: true
},
robust: {
compatible: true,
parseErrors: true
}
}
});
await visualTester.crossBrowser({
url: 'https://example.com',
browsers: ['chrome', 'firefox', 'safari', 'edge'],
versions: 'latest-2',
comparisons: {
betweenBrowsers: true,
betweenVersions: true,
againstBaseline: true
}
});
| Level | Criteria | Auto-Testable |
|---|---|---|
| A | Non-text Content | ✅ |
| A | Info and Relationships | Partial |
| A | Color Contrast (4.5:1) | ✅ |
| A | Keyboard Accessible | ✅ |
| A | Focus Visible | ✅ |
| AA | Reflow | ✅ |
| AA | Text Spacing | ✅ |
| AAA | Enhanced Contrast (7:1) | ✅ |
interface VisualReport {
summary: {
pagesCompared: number;
differencesFound: number;
passRate: number;
};
comparisons: {
page: string;
viewport: string;
baseline: string;
current: string;
diff: string;
diffPercentage: number;
status: 'pass' | 'fail' | 'review';
}[];
accessibility: {
violations: A11yViolation[];
passes: number;
incomplete: number;
score: number;
};
responsive: {
viewport: string;
issues: ResponsiveIssue[];
}[];
}
interface AccessibilityReport {
summary: {
score: number;
violations: number;
warnings: number;
passes: number;
};
violations: {
id: string;
impact: 'critical' | 'serious' | 'moderate' | 'minor';
description: string;
wcag: string[];
elements: {
selector: string;
html: string;
issue: string;
fix: string;
}[];
}[];
compliance: {
wcagLevel: 'A' | 'AA' | 'AAA';
criteriasMet: number;
criteriasTotal: number;
};
}
visual_testing:
on_pr:
- capture_screenshots
- compare_to_baseline
- run_a11y_audit
thresholds:
visual_diff: 0.1
a11y_violations: 0
artifacts:
- screenshots/
- diffs/
- a11y-report.html
Primary Agents: qe-visual-tester, qe-accessibility-agent, qe-responsive-tester Coordinator: qe-visual-coordinator Related Skills: qe-test-execution, qe-quality-assessment
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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。