一键导入
dew-create-epics-and-stories
Convert approved DEW designs into data engineering epics, story map, dependencies, and implementation backlog.
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。
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Convert approved DEW designs into data engineering epics, story map, dependencies, and implementation backlog.
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-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.