القائمة
Review implemented data engineering story for AC compliance, DQ evidence, grain, lineage, operational behavior, and caveats.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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