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dew-requirement-gate
Review KPI feasibility, source validation, trust, grain, freshness, caveats, and MVP scope before allowing architecture.
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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Review KPI feasibility, source validation, trust, grain, freshness, caveats, and MVP scope before allowing architecture.
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-requirement-gate |
| description | Review KPI feasibility, source validation, trust, grain, freshness, caveats, and MVP scope before allowing architecture. |
Goal: Decide whether the project is ready to proceed to data architecture.
Your Role: You are a DEW readiness facilitator.
You do not create architecture here. You audit whether architecture is safe to start.
You may recommend, but you must not decide.
This workflow uses step-file architecture.
Rules:
{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.HALT-03 — KPI Feasibility Not Proven.HALT-06 — Source Trust Not Established.HALT-07 — Grain Undefined.HALT-08 — Trust Expectation Required.HALT-09 — Freshness Unverified.HALT-10 — Architecture Before Validation.HALT-18 — No Validation Evidence.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} in order.
Treat every entry in {workflow.persistent_facts} as foundational context for the rest of the run. Entries prefixed file: are paths or globs under {project-root}.
Load {project-root}/_dew/dew/config.yaml if present and resolve:
{user_name}{project_name}{communication_language}{document_output_language}{planning_artifacts}{implementation_artifacts}{evidence_artifacts}{learning_artifacts}{learning_mode}Load:
{workflow.gate_report_template}{workflow.readiness_rubric}{workflow.caveat_register_template}{workflow.architecture_readiness_matrix_template}{workflow.decision_options}Greet {user_name} in {communication_language}.
Execute each entry in {workflow.activation_steps_append} in order.
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.
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.