菜单
Use when reviewing SQL for data development. Checks logic correctness, metric consistency, joins, aggregations, deduplication, null handling, date boundaries, partition filters, performance risks, engine-specific issues, and safer rewrite suggestions.
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 | sql-reviewer |
| description | Use when reviewing SQL for data development. Checks logic correctness, metric consistency, joins, aggregations, deduplication, null handling, date boundaries, partition filters, performance risks, engine-specific issues, and safer rewrite suggestions. |
审查数据开发 SQL 的逻辑正确性、口径一致性和性能风险。
这个 Skill 不只是格式化 SQL，而是站在数据开发代码审查的角度，判断这段 SQL 会不会算错、跑慢、影响下游或难以维护。
使用这个 Skill，当用户需要审查：
重点适合检查：
不要用这个 Skill 直接完成：
data-requirement-clarifiermetric-definition-reviewertable-design-advisordata-quality-rule-generator如果缺少表结构或业务口径，可以继续审查 SQL 结构，但必须标记结论的不确定性。
最少输入：
可选输入：
推荐使用 SQL 审查上下文模板。
最有价值的上下文是：执行引擎、表结构、分区字段、指标口径、期望结果粒度和数据量级。缺少表结构时仍可审查 SQL 结构，但字段唯一性、Join 放大和性能判断必须降级为风险提示。
## SQL 审查结果
### 1. 总体结论
### 2. SQL 目标与结果粒度
### 3. 高风险问题
| 风险等级 | 位置 | 问题 | 影响 | 建议 |
| --- | --- | --- | --- | --- |
### 4. 逻辑正确性检查
### 5. 性能检查
### 6. 可维护性检查
### 7. 待确认问题
### 8. 建议改写
```sql
-- 如有必要，给出更安全的 SQL 版本
```
如果用户指定执行引擎，要补充对应检查：
输出必须：
请用 sql-reviewer 审查下面这段 Spark SQL。
背景：计算每天每个渠道的新用户首购转化率。
结果粒度：dt + channel_id。
分区字段：dt。
[粘贴 SQL]