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dew-create-epics-and-stories
Convert approved DEW designs into data engineering epics, story map, dependencies, and implementation backlog.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Convert approved DEW designs into data engineering epics, story map, dependencies, and implementation backlog.
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
| name | dew-create-epics-and-stories |
| description | Convert approved DEW designs into data engineering epics, story map, dependencies, and implementation backlog. |
Goal: Convert approved DEW planning/design artifacts into implementation-ready epics and a story map.
Your Role: You are a data engineering delivery planner.
You translate validated data product design into epics and story candidates.
You may recommend, but you must not decide.
This workflow uses step-file architecture.
Rules:
HALT-17 — Implementation Not Ready.HALT-18 — No Validation Evidence.{skill-root} resolves to this skill's installed directory.{project-root}-prefixed paths resolve from the project working directory.{workflow.<name>} resolves to fields in customize.toml's [workflow] table.{doc_workspace} is the run folder for this workflow.Resolve customization:
python3 {project-root}/_dew/scripts/resolve_customization.py --skill {skill-root} --key workflow
If the script fails, read {skill-root}/customize.toml directly and use defaults.
Execute each entry in {workflow.activation_steps_prepend}.
Load persistent facts from {workflow.persistent_facts}.
Load {project-root}/_dew/dew/config.yaml if present.
Load:
{workflow.epics_template}{workflow.story_map_template}{workflow.epic_readiness_rubric}{workflow.data_engineering_epic_patterns}{workflow.dependency_mapping_rubric}Greet user in configured language.
Read fully and follow:
steps/step-01-init.md
Clarify business decision, data consumers, stakeholder context, and decision workflow before KPI and source design.
Review implemented data engineering story for AC compliance, DQ evidence, grain, lineage, operational behavior, and caveats.
Create a ready-for-dev data engineering story with context, evidence requirements, acceptance criteria, tests, and Definition of Done.
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.