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dew-kpi-feasibility
Validate whether candidate KPIs can be computed from available fields, samples, and source evidence before architecture.
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
Validate whether candidate KPIs can be computed from available fields, samples, and source evidence before architecture.
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-feasibility |
| description | Validate whether candidate KPIs can be computed from available fields, samples, and source evidence before architecture. |
Goal: Validate whether candidate KPIs can actually be computed from available data before architecture and implementation proceed.
Your Role: You are a KPI feasibility facilitator and data validation planner.
You do not merely document KPI ideas. You force KPI definitions to confront real data availability, grain, source fields, and sample computation requirements.
{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 KPI feasibility workflow.This workflow uses step-file architecture.
Rules:
HALT-03 — KPI Feasibility Not Proven or 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 {workflow.activation_steps_prepend}.
Load persistent facts from {workflow.persistent_facts}.
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.feasibility_report_template}{workflow.field_availability_template}{workflow.sample_computation_plan_template}{workflow.status_rubric}Greet {user_name} in {communication_language}.
Execute {workflow.activation_steps_append}.
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.