test-data-forge-en

简介

Test data is either too little or privacy-laden. This skill批量 makes boundary-complete, maskable data by field rules.

我能做什么

• 🎲 Batch-generate by constraints.（交付：Data rows）
• 🔳 Cover equivalence & boundaries.（交付：Edge set）
• 🕵️ Mask sensitive fields.（交付：Masked samp）
• 🧬 Support related-data consistency.（交付：Relational）
• 📋 Give coverage checklist.（交付：Cover list）
• 🧪 Provide assertion points.（交付：Assert pts）

我不能做什么

• Does not use real production data.
• No guarantee of real business distribution.
• Does not handle human-labeled data.

触发词（≥10 个）

• "test data"
• "data gen"
• "test sample"
• "boundary"
• "synthetic"
• "mock data"

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• "test set"
• "data generate"
• "equivalence"
• "case data"

输入规范

• 必填项：Fields and constraints (type/range/required).
• 可选项：Rows / mask needed.
• 输入格式示例：User: name, age(0-120), email.

输出规范（结构化交付物）

• 输出包含：Data rows、Edge set、Masked、Relational、Cover list
• 字段说明：field=f; rule=r; edge=e; mask=m; cover=c.
• 模板片段：

  <pre class='td'>{data_rows}</pre>

使用示例（≥3 个）

示例 1：User table

输入：age 0-120 输出：Includes 0/120/mid; email masked a***@x.

示例 2：Boundary

输入：cover 输出：Equivalence: valid/invalid groups.

示例 3：Relational

输入：order-user 输出：FK consistent, user_id aligned.

异常处理

• Missing constraints: returns required hint.
• Real data input: reminds to use synthetic.
• Out of scope: states boundary.

限制与免责声明

• No local filesystem access; requests only least-privilege permissions needed for the task.
• Does not collect sensitive user data (secrets, passwords, personal privacy).
• Output is a structured reference artifact, not professional legal/financial/medical advice.
• All examples are generated in a sandbox semantic; no real external write operations.
• Usable in domestic network environments; no reliance on blocked overseas services.

升级版规划（Pay Skill 预留）

Upgrade to paid once the creator billing channel opens. Free (published, fully usable): 生成合规测试数据集。 Pro (paid upgrade): 提供边界值、等价类与脱敏合成数据。 Upgrade path: Once the creator billing channel opens, this skill flips to Pro billing with no SKILL.md edit.

这个工具质量中等偏下。优点是功能说明清晰、示例充分、异常处理完善，能满足基础的测试数据生成需求。但存在语法问题导致理解困难，缺少详细的使用指南，且内容较为简略。对于需要复杂测试数据的用户来说，文档支持可能不够充分。