菜单
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.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/proffesor-for-testing/agentic-qe --skill test-data-management命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| 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 |
| last_optimized | "2025-12-02T00:00:00.000Z" |
| 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:
Quick Data Strategy:
Critical Success Factors:
| 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 ;
}
anonymizedUsers = prodUsers.( ({
: user.,
: ,
: faker..(),
: ,
: user.
}));
// 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.