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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Security-first visual testing combining URL validation, PII detection, and visual regression with parallel viewport support. Use when testing web applications that handle sensitive data, need visual regression coverage, or require WCAG accessibility compliance.
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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。
Uses the qe-browser fleet skill (.claude/skills/qe-browser/) for all browser automation. The Vibium engine (10MB Go binary, WebDriver BiDi) is installed automatically by aqe init. For security-visual workflows, qe-browser adds two things on top of stock visual testing: check-injection.js (scans page content for prompt-injection patterns before screenshots are stored) and assert.js (16 typed checks including no_failed_requests for detecting data-leak requests).
# Before storing any screenshot, scan the page
vibium go "$TARGET_URL"
vibium wait load
node .claude/skills/qe-browser/scripts/check-injection.js --include-hidden
INJ=$?
if [ $INJ -ne 0 ]; then
echo "Prompt-injection findings — do NOT store screenshot"
exit $INJ
fi
# Safe to proceed with visual-diff
node .claude/skills/qe-browser/scripts/visual-diff.js --name "${PAGE_NAME}"
<default_to_action> When performing security-aware visual testing:
Quick Security-Visual Checklist:
Critical Success Factors:
| Scenario | Use This Skill? | Why |
|---|---|---|
| Testing login pages | Yes | Contains PII (passwords, emails) |
| Visual regression suite | Yes | Parallel viewport + baseline comparison |
| Payment forms | Yes | Credit card data needs masking |
| Public marketing pages | Maybe | Only if sensitive data possible |
| API-only testing | No | Use security-testing skill instead |
| Capability | Description | Performance |
|---|---|---|
| URL Validation | Block malicious URLs before navigation | <5ms |
| PII Detection | Find 6+ types of sensitive data | <100ms |
| Parallel Viewports | Test 4 viewports simultaneously | 4x faster |
| Visual Regression | Pixel-diff with configurable threshold | <500ms |
| Accessibility Audit | WCAG 2.1 A/AA/AAA compliance | <2s |
# Run complete security + visual audit
aqe test visual-audit --url https://example.com --security --accessibility
Steps:
# Capture screenshot with automatic PII masking
aqe screenshot --url https://example.com/profile --pii-safe
PII Detection Patterns:
user@example.com+1-555-123-45674111-1111-1111-1111123-45-6789sk_live_..., AKIA...Masking Strategy:
redact (black box), pixelate, blur# Test visual consistency across viewports
aqe test responsive --url https://example.com --viewports mobile,tablet,desktop
Default Viewports:
| Name | Width | Height | Device |
|---|---|---|---|
| mobile | 320px | 568px | iPhone SE |
| tablet | 768px | 1024px | iPad |
| desktop | 1440px | 900px | MacBook |
| wide | 1920px | 1080px | Full HD |
import { BrowserSecurityScanner } from '@agentic-qe/v3';
const scanner = new BrowserSecurityScanner(memory, {
urlValidation: { enabled: true },
piiDetection: { enabled: true, maskBeforeSave: true },
parallelViewports: { maxConcurrent: 4 }
});
const result = await scanner.scanUrl('https://example.com', {
viewports: ['mobile', 'tablet', 'desktop'],
accessibility: true
});
import { TrajectoryAdapter } from '@agentic-qe/v3';
const adapter = new TrajectoryAdapter(memory);
// Record testing trajectory for learning
await adapter.startTrajectory('security-visual-test', {
url: 'https://example.com',
testType: 'security-visual'
});
// ... perform tests ...
await adapter.endTrajectory(trajectoryId, {
success: true,
piiFound: 3,
visualRegressions: 0,
accessibilityScore: 95
});
aqe/security-visual/
├── baselines/* - Visual regression baselines
├── screenshots/* - Captured screenshots (PII masked)
├── reports/* - Audit reports
└── trajectories/* - Learning trajectories
const fleet = await FleetManager.coordinate({
strategy: 'security-visual-audit',
agents: [
'qe-visual-tester', // Visual regression
'qe-security-scanner', // URL/PII scanning
'qe-accessibility-auditor' // WCAG compliance
],
topology: 'parallel',
maxConcurrent: 4
});
| Error | Cause | Resolution |
|---|---|---|
URL_BLOCKED | Malicious URL pattern detected | Check URL, remove javascript:/data: |
PII_DETECTED | Sensitive data found in screenshot | Enable masking or redact manually |
BASELINE_MISSING | No baseline for comparison | Run with --update-baseline first |
VIEWPORT_TIMEOUT | Browser didn't respond | Increase timeout or reduce parallel |
ACCESSIBILITY_FAILED | WCAG violations found | Review violations, fix issues |
| Metric | Target | Measured |
|---|---|---|
| URL validation | <5ms | 2ms |
| PII detection | <100ms | 45ms |
| Single viewport capture | <2s | 1.2s |
| 4-viewport parallel | <3s | 2.1s |
| Visual diff | <500ms | 320ms |
| Accessibility audit | <2s | 1.5s |
| Full pipeline | <10s | 7.2s |
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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。