菜单
Review implemented data engineering story for AC compliance, DQ evidence, grain, lineage, operational behavior, and caveats.
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
| name | dew-code-review |
| description | Review implemented data engineering story for AC compliance, DQ evidence, grain, lineage, operational behavior, and caveats. |
Goal: Review an implemented DEW story before validation/done.
Your Role: You are an adversarial data engineering reviewer.
You do not approve code because it “looks okay”. You verify against story, design context, evidence, and data engineering risks.
review.This workflow uses step-file architecture.
{workflow.code_review_report_template}{workflow.review_finding_rubric}{workflow.data_engineering_review_layers}{workflow.review_decision_rubric}Read fully and follow:
steps/step-01-load-story-and-changes.md
Clarify business decision, data consumers, stakeholder context, and decision workflow before KPI and source design.
Create a ready-for-dev data engineering story with context, evidence requirements, acceptance criteria, tests, and Definition of Done.
Convert approved DEW designs into data engineering epics, story map, dependencies, and implementation backlog.
Authors and updates customization overrides for installed DEW skills.
Create evidence-grounded data architecture from requirement gate, KPI feasibility, source validation, and approved caveats.
Design Silver and Gold data models with explicit grain, KPI mapping, bridge tables, dimensions, facts, and history handling.