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Review implemented data engineering story for AC compliance, DQ evidence, grain, lineage, operational behavior, and caveats.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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.
| 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