a11y-ally
Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse,…
Strategic test data generation, management, and privacy compliance. Use when creating test data, handling PII, ensuring GDPR/CCPA compliance, or scaling data generation for realistic testing scenarios.
$ npx -y skills add proffesor-for-testing/agentic-qe --skill test-data-management --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/test-data-managementContext preview
The summary Claude sees to decide when to auto-load this skill.
Strategic test data generation, management, and privacy compliance. Use when creating test data, handling PII, ensuring GDPR/CCPA compliance, or scaling data generation for realistic testing scenarios.
name: test-data-management description: "Strategic test data generation, management, and privacy compliance. Use when creating test data, handling PII, ensuring GDPR/CCPA compliance, or scaling data generation for realistic testing scenarios." category: specialized-testing priority: high tokenEstimate: 1000 agents: [qe-test-data-architect, qe-test-executor, qe-security-scanner] implementation_status: optimized optimization_version: 1.0 last_optimized: 2025-12-02 dependencies: [] quick_reference_card: true tags: [test-data, faker, synthetic, gdpr, pii, anonymization, factories] trust_tier: 3 validation: schema_path: schemas/output.json validator_path: scripts/validate-config.json eval_path: evals/test-data-management.yaml
<default_to_action> When creating or managing test data: 1. NEVER use production PII directly 2. GENERATE synthetic data with faker libraries 3. ANONYMIZE production data if used (mask, hash) 4. ISOLATE test data (transactions, per-test cleanup) 5. SCALE with batch generation (10k+ records/sec)
**Quick Data Strategy:**
**Critical Success Factors:**
</default_to_action>
| Type | When | Size | |------|------|------| | **Minimal** | Unit tests | 1-10 records | | **Realistic** | Integration | 100-1000 records | | **Volume** | Performance | 10k+ records | | **Edge cases** | Boundary testing | Targeted |
---
// Masking
function maskEmail(email) {
const [user, domain] = email.split('@');
return `${user[0]}***@${domain}`;
}
// john@example.com → j***@example.com
function maskCreditCard(cc) {
return `****-****-****-${cc.slice(-4)}`;
}
// 4242424242424242 → ****-****-****-4242
// Anonymize production data
const anonymizedUsers = prodUsers.map(user => ({
id: user.id, // Keep ID for relationships
email: `user-${user.id}@example.com`, // Fake email
firstName: faker.person.firstName(), // Generated
phone: null, // Remove PII
createdAt: user.createdAt // Keep non-PII
}));---
// Best practice: use transactions for cleanup
beforeEach(async () => {
await db.beginTransaction();
});
afterEach(async () => {
await db.rollbackTransaction(); // Auto cleanup!
});
test('user registration', async () => {
const user = await userService.register({
email: 'test@example.com'
});
expect(user.id).toBeDefined();
// Automatic rollback after test - no cleanup needed
});---
// High-speed generation with constraints
await Task("Generate Test Data", {
schema: 'ecommerce',
count: { users: 10000, products: 500, orders: 5000 },
preserveReferentialIntegrity: true,
constraints: {
age: { min: 18, max: 90 },
roles: ['customer', 'admin']
}
}, "qe-test-data-architect");
// GDPR-compliant anonymization
await Task("Anonymize Production Data", {
source: 'production-snapshot',
piiFields: ['email', 'phone', 'ssn'],
method: 'pseudonymization',
retainStructure: true
}, "qe-test-data-architect");---
aqe/test-data-management/ ├── schemas/* - Data schemas ├── generators/* - Generator configs ├── anonymization/* - PII handling rules └── fixtures/* - Reusable fixtures
const dataFleet = await FleetManager.coordinate({
strategy: 'test-data-generation',
agents: [
'qe-test-data-architect', // Generate data
'qe-test-executor', // Execute with data
'qe-security-scanner' // Validate no PII exposure
],
topology: 'sequential'
});---
---
**Never use production PII directly.** Always use synthetic data or properly anonymized production snapshots.
**With Agents:** `qe-test-data-architect` generates 10k+ records/sec with realistic patterns, relationships, and constraints. Agents ensure GDPR/CCPA compliance automatically and eliminate test data bottlenecks.
AI-powered quality engineering agents that generate tests, find coverage gaps, detect flaky tests, and learn your codebase patterns — across 11 coding agent platforms.
Repo: proffesor-for-testing/agentic-qe
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