菜单
Use when reviewing or defining a business metric, KPI, or analytics indicator. Checks business intent, population, event definition, time window, grain, filters, edge cases, status lifecycle, consistency, and questions that must be confirmed.
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。
基于 SOC 职业分类
Use when writing a concise daily report, daily worklog, standup update, shift handoff, or end-of-day summary for data analysis, data development, BI, governance, or operations work.
Use when writing or improving data documentation for warehouse tables, metrics, dashboards, SQL jobs, data products, or handover materials so other people and agents can understand data purpose, grain, fields, logic, ownership, and usage constraints.
Use when analyzing upstream and downstream impact for table changes, metric changes, field deprecation, pipeline migration, dashboard breakage, data governance review, or data asset dependency communication.
Use when planning integration with databases, BI tools, data platforms, APIs, workflow systems, storage services, or third-party tools, including connection requirements, API availability, auth strategy, configuration questions, and validation steps.
Use when planning or writing market research for a specific industry, product category, competitor set, business opportunity, user segment, or go-to-market question with structured evidence and clear assumptions.
Use when diagnosing business metric changes or anomalies such as GMV decline, conversion drop, retention loss, churn increase, revenue change, lead decline, or active user fluctuation with a structured root cause analysis plan.
| name | metric-definition-reviewer |
| description | Use when reviewing or defining a business metric, KPI, or analytics indicator. Checks business intent, population, event definition, time window, grain, filters, edge cases, status lifecycle, consistency, and questions that must be confirmed. |
审查一个指标口径是否清晰、稳定、可计算、可解释。
这个 Skill 关注指标本身，不负责完整建模，也不负责直接写生产 SQL。它要帮助用户发现口径漏洞、边界条件、业务歧义和跨团队对齐风险。
使用这个 Skill，当用户需要：
典型输入：
帮我审查一下“7 日留存率”的口径。
GMV 现在按支付成功订单金额统计，退款是否扣减还没定。
不要用这个 Skill 直接完成：
data-requirement-clarifiertable-design-advisorsql-reviewerdata-quality-rule-generator如果用户只提供了一个指标名，必须先列出待确认问题，不能直接给出确定口径。
最少输入：
可选输入：
推荐使用 指标口径上下文模板。
最有价值的上下文是：业务含义、统计对象、时间窗口、分子分母、过滤条件、去重规则和边界处理。缺少业务规则时，只能给出推荐口径和待确认问题，不能把行业常见口径当成事实。
逐项检查：
## 指标口径审查
### 1. 审查结论
### 2. 推荐定义
| 项目 | 内容 |
| --- | --- |
| 指标名称 | |
| 业务含义 | |
| 统计对象 | |
| 统计粒度 | |
| 时间口径 | |
| 分子 | |
| 分母 | |
| 过滤条件 | |
| 去重规则 | |
| 更新频率 | |
### 3. 主要风险
| 风险等级 | 问题 | 影响 | 建议 |
| --- | --- | --- | --- |
### 4. 边界条件
### 5. 待确认问题
### 6. 可选 SQL 伪逻辑
输出必须：
请用 metric-definition-reviewer 审查这个指标：
指标：新用户首购转化率
当前口径：注册后 7 天内完成支付的新用户数 / 新注册用户数
使用场景：渠道投放效果看板
疑问：退款订单要不要算转化？