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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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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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。
Consolidated: For comprehensive WCAG auditing with multi-tool testing (axe-core + pa11y + Lighthouse), video accessibility, and remediation, prefer
/a11y-ally. This skill provides a quick reference card for basic accessibility testing patterns.
Browser-driven a11y checks should go through the qe-browser fleet skill. vibium a11y-tree --json returns the full accessibility tree without visual rendering — feed it into axe-core via vibium eval --stdin for ruleset enforcement. See .claude/skills/qe-browser/SKILL.md.
<default_to_action> When testing accessibility or ensuring compliance:
Quick A11y Checklist:
Critical Success Factors:
| Level | Requirement | Target |
|---|---|---|
| A | Basic accessibility | Minimum legal |
| AA | Standard (most orgs) | Industry standard |
| AAA | Enhanced | Specialized sites |
| Principle | Meaning | Key Tests |
|---|---|---|
| Perceivable | Can perceive content | Alt text, contrast, captions |
| Operable | Can operate UI | Keyboard, no seizures, navigation |
| Understandable | Can understand | Clear labels, predictable, errors |
| Robust | Works with assistive tech | Valid HTML, ARIA |
| Content | AA Ratio | AAA Ratio |
|---|---|---|
| Normal text | 4.5:1 | 7:1 |
| Large text (18pt+) | 3:1 | 4.5:1 |
| UI components | 3:1 | - |
// Test all interactive elements reachable via keyboard
test('all interactive elements keyboard accessible', async ({ page }) => {
await page.goto('/');
const focusableElements = await page.$$('button, a, input, select, textarea, [tabindex]');
for (const element of focusableElements) {
await element.focus();
const isFocused = await element.evaluate(el => document.activeElement === el);
expect(isFocused).toBe(true);
}
});
// Verify visible focus indicator
test('focus indicator visible', async ({ page }) => {
await page.goto('/');
await page.keyboard.press('Tab');
const focusedElement = await page.locator(':focus');
const outline = await focusedElement.evaluate(el =>
getComputedStyle(el).outline
);
expect(outline).not.toBe('none');
});
a11y-ally AQE skill (qe-browser + Vibium)For new work, use the a11y-ally skill — it composes qe-browser (Vibium
WebDriver BiDi) with axe-core, pa11y, and Lighthouse and produces a
WCAG-tagged JSON report with remediation guidance. It avoids the 300MB
Playwright install and is already wired into the AQE fleet.
# Runs axe-core + pa11y + Lighthouse via qe-browser (Vibium) engine
aqe skill run a11y-ally -- --url https://example.com --wcag AA
Keep this path when you have an existing Playwright suite and don't want to introduce a second browser runner, or when you need Firefox/Safari coverage that Vibium's Chrome-only BiDi backend can't provide today.
import { test, expect } from '@playwright/test';
import AxeBuilder from '@axe-core/playwright';
test('page has no accessibility violations', async ({ page }) => {
await page.goto('/');
const results = await new AxeBuilder({ page })
.withTags(['wcag2a', 'wcag2aa', 'wcag22aa'])
.analyze();
expect(results.violations).toEqual([]);
});
// CI/CD integration
test('checkout flow accessible', async ({ page }) => {
await page.goto('/checkout');
const results = await new AxeBuilder({ page })
.include('#checkout-form')
.disableRules(['color-contrast']) // Fix in next sprint
.analyze();
expect(results.violations.filter(v =>
v.impact === 'critical' || v.impact === 'serious'
)).toHaveLength(0);
});
## VoiceOver (macOS) Testing
- [ ] Page title announced on load
- [ ] Headings hierarchy correct (h1 → h2 → h3)
- [ ] Landmarks present (nav, main, footer)
- [ ] Images have descriptive alt text
- [ ] Form labels read correctly
- [ ] Error messages announced
- [ ] Dynamic content updates announced (aria-live)
// Comprehensive a11y validation
await Task("Accessibility Validation", {
url: 'https://example.com/checkout',
standard: 'WCAG2.2',
level: 'AA',
checks: ['keyboard', 'screen-reader', 'color-contrast'],
includeScreenReaderSimulation: true
}, "qe-visual-tester");
// Fleet coordination for comprehensive testing
const a11yFleet = await FleetManager.coordinate({
strategy: 'comprehensive-accessibility',
agents: [
'qe-visual-tester', // Visual & keyboard checks
'qe-test-generator', // Generate a11y tests
'qe-quality-gate' // Enforce compliance
],
topology: 'parallel'
});
aqe/accessibility/
├── wcag-results/* - WCAG audit results
├── screen-reader/* - Screen reader test logs
├── remediation/* - Fix recommendations
└── compliance/* - Compliance reports
const a11yFleet = await FleetManager.coordinate({
strategy: 'accessibility-testing',
agents: [
'qe-visual-tester', // axe-core, keyboard, focus
'qe-test-generator', // Generate a11y test cases
'qe-quality-gate' // Block non-compliant builds
],
topology: 'parallel'
});
1 billion people have disabilities. Inaccessible software excludes 15% of humanity. Legal requirements: ADA, Section 508, EU Directive 2016/2102. $13T purchasing power. 250%+ increase in lawsuits.
Automated testing catches only 30-50% of issues. Combine with manual keyboard testing, screen reader testing, and real user testing with people with disabilities.
With Agents: Agents automate WCAG 2.2 compliance checking, screen reader simulation, and focus management validation. Use agents to enforce accessibility standards in CI/CD and catch violations before production.
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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.
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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.
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Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
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