What makes qa-test-data-engineering notable?

1.2K downloads. Trust evidence available

How should qa-test-data-engineering be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

qa-test-data-engineering

当需要批量构造测试数据（造 1000 条订单、准备各种状态的用户数据）、或者需要使用真实生产数据但需要脱敏时使用此技能。覆盖造数策略（API 造数/DB 直接构造/数据工厂）、脱敏方案（敏感字段识别/替换/掩码）、合规要求（GDPR/等保/个保法）和数据工厂架构设计。手工一条条造数据效率太低——测试数据工程的目标是让造数变成一键操作。触发场景：造数、批量造数、数据构造、测试数据脱敏、测试数据合规、数据工厂、造1000条、造大量数据、环境数据不足需要批量构造时。 Use when the user asks about: bulk test data generation, production data masking and anonymization, data compliance, and data factory architecture. Skill: qa-test-data-engineering Owner: kokxi Summary: 当需要批量构造测试数据（造 1000 条订单、准备各种状态的用户数据）、或者需要使用真实生产数据但需要脱敏时使用此技能。覆盖造数策略（API 造数/DB 直接构造/数据工厂）、脱敏方案（敏感字段识别/替换/掩码）、合规要求（GDPR/等保/个保法）和数据工厂架构设计。手工一条条造数据效率太低——测试数据工程的目标是让造数变成一键操作。触发场景：造数、批量造数、数据构造、测试数据脱敏、测试数据合规、数据工厂、造1000条、造大量数据、环境数据不足需要批量构造时。 Use when the user asks about: bulk test data generation, production data masking and anonymization, data compliance, and data factor

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.2k

Updated

Oct 10, 2026

Version

1.8.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.2K downloadsadoption · observed Oct 10, 2026
Latest release: 1.8.0release · observed Sep 29, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170jw3s1atcj5jwhqb4r7v7eh8912kp:qa-test-data-engineering

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-kokxi-qa-test-data-engineering/snapshot"

Documentation

CLAWHUB

75,034 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

---
name: qa-test-data-engineering
description: >-
  当需要批量构造测试数据（造 1000 条订单、准备各种状态的用户数据）、或者需要使用真实生产数据但需要脱敏时使用此技能。覆盖造数策略（API 造数/DB 直接构造/数据工厂）、脱敏方案（敏感字段识别/替换/掩码）、合规要求（GDPR/等保/个保法）和数据工厂架构设计。手工一条条造数据效率太低——测试数据工程的目标是让造数变成一键操作。 触发场景：造数、批量造数、数据构造、测试数据脱敏、测试数据合规、数据工厂、造1000条、造大量数据、环境数据不足需要批量构造时。 Use when the user asks about: bulk test data generation, production data masking and anonymization, data compliance, and data factory architecture.
license: MIT
allowed-tools: Read Grep Glob Bash
metadata:
  display-name: "Test Data Engineering"
  version: "1.8.0"
  when-to-use: "用户说\"造数\"、\"批量造数\"、\"数据构造\"、\"测试数据脱敏\"、\"测试数据合规\"、\"数据工厂\"、\"造1000条\"、\"造大量数据\"、需要管理测试数据、环境数据不足需要批量构造时"
  related-skills: "{\"upstream\":[\"qa-test-env-data\",\"qa-req-deconstruction\"],\"downstream\":[\"qa-execution-observation\",\"qa-api-testing\"]}"
  references: "[\"references/data-masking.md\"]"
  input-format: "{\"required\":[{\"name\":\"测试策略\",\"type\":\"object\",\"description\":\"来自qa-test-strategy-design的测试策略\"},{\"name\":\"数据需求\",\"type\":\"string\",\"description\":\"测试数据的类型和规模需求\"}],\"optional\":[{\"name\":\"数据源信息\",\"type\":\"string\",\"description\":\"可用数据源描述\"}]}"
  output-format: "{\"traceability\":[\"每套造数方案带唯一ID（DATA-XXXX）\"],\"structure\":[{\"data_strategy\":\"测试数据策略\"},{\"data_generation\":\"数据生成方案\"},{\"data_mask_rules\":\"数据脱敏规则\"},{\"data_management\":\"数据管理流程\"}]}"
  error-recovery-guidance: "{\"on_failure\":\"造数方案遗漏合规要求时回退到需求解构补充\",\"retry_behavior\":\"补充合规要求后重新设计造数方案\"}"
  categories: "[\"Development\",\"Testing\",\"DevOps\"]"
  depth-requirement: "{\"reference_value\":\"根据数据需求调整造数深度：简单×1/中等×2/复杂×3\",\"minimum\":\"至少覆盖数据构造、脱敏、合规3个维度\"}"
---
> ⚠️ 本技能单独使用效果有限，建议配合完整技能集（12 步工作流）使用。安装：npx skills add Kokxi/qa-test-skills

# 测试数据工程

## 核心原则

测试数据是测试的基础，好的数据管理让测试可重复、可追溯。

## 数据构造方法

### 手动构造

```text
适用场景：
├─ 少量数据
├─ 复杂业务数据
├─ 一次性数据
└─ 调试用途

方法：
├─ 数据库直接插入
├─ 管理后台创建
├─ 接口调用创建
└─ 脚本批量创建
```

### 数据工厂

```text
适用场景：
├─ 批量数据
├─ 标准化数据
├─ 重复性数据
└─ 自动化测试

工具：
├─ Faker（Python/JS）
├─ Mockaroo（在线）
├─ Factory Bot（Ruby）
└─ 自建工厂类
```

### 数据库脚本

```sql
-- 示例：用户数据构造
INSERT INTO users (username, email, phone, status, created_at)
VALUES 
    ('testuser001', '[email protected]', '13800000001', 'active', NOW()),
    ('testuser002', '[email protected]', '13800000002', 'active', NOW()),
    ('testuser003', '[email protected]', '13800000003', 'inactive', NOW());
```

### API构造

```python
# 示例：通过API构造订单数据
def create_test_order(user_id, product_id, quantity=1):
    response = requests.post(
        f"{BASE_URL}/orders",
        json={
            "user_id": user_id,
            "product_id": product_id,
            "quantity": quantity
        },
        headers={"Authorization": f"Bearer {token}"}
    )
    return response.json()["order_id"]
```

## 加载时机

| 什么时候读 | 读哪个 |
|-----------|--------|
| 做脱敏规则与实现时 | [`references/data-masking.md`](references/data-masking.md) |

> `数据脱敏`的完整内容已下沉至 `references/data-masking.md`，避免每次触发都占用上下文。

## 数据清理

> 📌 本节与 qa-test-env-data「数据清理」内容同步，修改时请同步更新两处。

### 清理策

_meta.json

{
  "ownerId": "kn71y9b23csfx0ykgm55d5m9x5891zt8",
  "slug": "qa-test-data-engineering",
  "version": "1.8.0",
  "publishedAt": 1790656157577
}

references/data-masking.md

# 测试数据脱敏详解

> 本文是 `qa-test-data-engineering` 的**测试数据脱敏详解**。做脱敏规则与实现时读本文；
其余部分留在 SKILL.md，不必读本文。

---


> 📌 本节与 qa-test-env-data「数据脱敏」内容同步，修改时请同步更新两处。qa-test-env-data 为简化版，完整版见此处。

### 脱敏规则

```text
个人信息：
├─ 手机号：138****1234
├─ 身份证：110***********1234
├─ 邮箱：test****@example.com
├─ 姓名：*三
├─ 地址：北京市***
└─ 银行卡：6222****1234

业务数据：
├─ 金额：保留整数位，小数随机
├─ 订单号：保留格式，数字随机
├─ 时间：保留格式，时间随机
└─ 关联ID：保持关联关系
```

### 脱敏方法

```text
├─ 替换法：用*替换部分字符
│   └─ 示例：138****1234
│
├─ 加密法：用加密算法处理
│   └─ 示例：AES加密后存储
│
├─ 截断法：只保留部分字符
│   └─ 示例：北京市***
│
├─ 随机法：用随机值替换
│   └─ 示例：姓名随机生成
│
└─ 哈希法：用哈希值替换
    └─ 示例：SHA256哈希
```

### 脱敏实现

```python
# 示例：Python脱敏函数
import hashlib
import random

def mask_phone(phone):
    """手机号脱敏：138****1234"""
    return phone[:3] + "****" + phone[-4:]

def mask_id_card(id_card):
    """身份证脱敏：110***********1234"""
    return id_card[:3] + "*" * 10 + id_card[-4:]

def mask_name(name):
    """姓名脱敏：*三"""
    return "*" + name[-1]

def mask_email(email):
    """邮箱脱敏：test****@example.com"""
    local, domain = email.split("@")
    return local[:4] + "****@" + domain
```

skill-card.md

## Description:

Guides bulk test-data creation, data masking, and test-data lifecycle management for software testing.

This skill is ready for commercial/non-commercial use.

## Publisher:

[kokxi](https://clawhub.ai/user/kokxi)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and QA engineers use this skill to plan and generate repeatable test datasets, mask sensitive data, and manage test-data cleanup.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The optional command to install the broader skill set could fetch unreviewed code.

Mitigation: Verify the publisher and repository, and prefer a pinned or reviewed version before running it.

Risk: Running example SQL or API operations against live systems could change production data.

Mitigation: Use isolated test environments and review the target and affected records before execution.

## Reference(s):

- [Test-data masking reference](artifact/references/data-masking.md)
- [ClawHub skill release](https://clawhub.ai/kokxi/skills/qa-test-data-engineering)

## Skill Output:

**Output Type(s):** [Guidance, Code, Configuration, SQL queries]

**Output Format:** [Markdown with example SQL and Python code]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Test-data plans cover generation, masking, and lifecycle management, with a DATA-XXXX identifier for each plan.]

## Skill Version(s):

1.8.0 (source: skill metadata and ClawHub release)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

Github ReposUpdated 1d agoRank 70

AionUi

Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

MCPOPENCLAW

Github ReposUpdated 6mo agoRank 70

activepieces

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents

OPENCLAW

Github ReposUpdated 6mo agoRank 70

cherry-studio

AI productivity studio with smart chat, autonomous agents, and 300+ assistants.