Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/killvxk/pm-skills-zh/generate-datagit clone --depth 1 https://github.com/killvxk/pm-skills-zhWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/killvxk/pm-skills-zh/generate-data)<a href="https://agentmods.dev/commands/killvxk/pm-skills-zh/generate-data"><img src="https://agentmods.dev/badge/commands/killvxk/pm-skills-zh/generate-data.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00028 | $0.00756 |
| Opus 5 | $0.00014 | $0.00378 |
| Sonnet 5 | $0.00006 | $0.00151 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
generate-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
/generate-data -- 测试数据生成器
为开发、测试、演示或原型制作创建逼真的虚拟数据集，以可直接使用的文件形式输出。
Invocation（调用示例）
/generate-data 1000 个用户，包含姓名、邮箱、套餐等级、注册日期和活跃度评分
/generate-data 电商订单数据集：商品、客户、时间戳、金额
/generate-data 根据以下 schema 生成示例数据：[粘贴表定义]
Workflow（工作流程）
Step 1：定义数据集
明确以下信息：
- 实体是什么？（用户、订单、事件、商品等）
- 需要哪些列？（含数据类型和约束条件）
- 需要多少行？
- 表之间是否有关联关系？
- 是否有特定的分布要求？（例如："80% 应使用免费套餐"）
- 是否有真实性约束？（邮箱需唯一、日期需按时间顺序排列）
Step 2：生成数据
应用 dummy-dataset 技能：
- 编写 Python 脚本生成数据集
- 使用看起来真实的数据（而非随机字符串）：真实姓名、有效邮箱格式、合理日期
- 遵守约束条件：唯一 ID、外键关系、时间顺序
- 应用指定的分布规则
- 执行脚本并输出文件
Step 3：交付结果
以所需格式输出（或询问用户偏好）：
- CSV：最通用，到处可用
- JSON：用于 API 测试或前端开发
- SQL INSERT：用于填充测试数据库
- Python 脚本：可重复生成（用户可调整参数后重新运行）
## 生成的数据集：[描述]
**行数**: [数量]
**列名**: [列表]
**格式**: [CSV / JSON / SQL / Python]
### Schema（结构）
| 列名 | 类型 | 约束条件 | 分布规则 |
|------|------|----------|----------|
### 示例（前 5 行）
[数据预览]
### 文件
- [数据文件]
- [生成脚本（如适用）]
将数据文件和生成脚本保存到用户工作区。
Step 4：提供后续建议
- "需要我增加更多列或扩大数据集规模吗？"
- "需要我创建关联表（例如这些用户的订单数据）吗？"
- "需要我编写使用这份数据的测试场景吗？"
- "需要我编写 SQL 查询来分析这份数据集吗？"
Notes（注意事项）
- 始终提供生成脚本，方便用户用不同参数重新生成
- 对于演示数据集，让数据讲述一个故事（例如季节性趋势、留存问题、核心用户群体）
- 注意真实的基数比例：1000 个用户不会有 1000 个不同城市
- 对于财务数据，使用符合实际的价格分布，而非均匀随机分布
- 切勿包含真实个人数据——所有姓名、邮箱和标识符必须是虚构的
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 83 lines · 28 tokens per session scan A d5f2816b0a87
generate-data is a command published in the GitHub repository killvxk/pm-skills-zh (151 stars, last pushed 5mo ago), licensed MIT. It adds 28 tokens to every session and 756 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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