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dew-kpi-prototype
Create executable SQL, Python, or notebook prototype to compute KPI on sample data and produce validation evidence.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
メニュー
Create executable SQL, Python, or notebook prototype to compute KPI on sample data and produce validation evidence.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | dew-kpi-prototype |
| description | Create executable SQL, Python, or notebook prototype to compute KPI on sample data and produce validation evidence. |
Goal: Produce executable evidence that a KPI can be computed on sample data.
Your Role: You are a data engineer implementing a KPI feasibility prototype.
You must not create production pipeline code here. The goal is feasibility proof.
HALT-18 — No Validation Evidence.dew-kpi-feasibility.{workflow.activation_steps_prepend}.{workflow.prototype_template}{workflow.sample_output_template}Look for:
{planning_artifacts}/kpi-feasibility/**/sample-computation-plan.md{planning_artifacts}/kpi-feasibility/**/kpi-feasibility-report.mdIf missing, ask user to run dew-kpi-feasibility.
Select KPI to prototype.
If multiple primary KPIs exist, ask user which one to run first.
Look for:
{evidence_artifacts}/source-samples/**{evidence_artifacts}/schema-snapshots/**If missing, trigger HALT-18.
Create one of:
{workflow.prototype_output_path}/{kpi_id}-prototype.sql{workflow.prototype_output_path}/{kpi_id}-prototype.py{workflow.prototype_output_path}/{kpi_id}-prototype.ipynb{workflow.prototype_output_path}/{kpi_id}-manual-validation.mdUse the method specified in the computation plan.
Prototype must check:
Create:
{kpi_id}-sample-output.csv or .md{kpi_id}-prototype-notes.mdUpdate evidence index.
Do not mark KPI as verified directly.
Recommend next:
dew-kpi-validation-review
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.