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Structure and clarify analysis requests before work begins. Activate when a request is vague, has multiple stakeholders with potentially different needs, or when the analysis is non-trivial and rework would be costly.
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SOC 職業分類に基づく
Create standardized metadata for data assets. Use when documenting new datasets, building data catalogs, improving data discoverability, or creating data dictionaries for teams.
Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.
Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating production pipelines, onboarding new data sources, or when stakeholders report data quality concerns.
Identify distinct customer or user segments based on behavior, attributes, or value. Activate when you need to answer "who are our best customers?" or "what distinct groups exist in our user base?" and need data-driven profiles to inform strategy.
Create effective, publication-ready data visualizations. Use when choosing chart types, designing presentation visuals, building dashboard charts, or applying visual design best practices to data output.
Create a clear, transparent explanation of analytical methodology for any audience level. Activate when you deliver findings that require the audience to trust the method — A/B tests, attribution models, forecasts, statistical analyses, or anything where "how did you get that?" is a likely question.
| name | stakeholder-requirements-gathering |
| description | Structure and clarify analysis requests before work begins. Activate when a request is vague, has multiple stakeholders with potentially different needs, or when the analysis is non-trivial and rework would be costly. |
references/intake_interview_guide.md to surface: the business question, the decision it will inform, the stakeholder who will act on it, the timeline, and what "done" looks likereferences/decision_maker_framework.md to determine whether the decision is exploratory, confirmatory, or operational; this sets the required rigor levelassets/requirements_template.md covering: scope, data sources, output format, success criteria, and known constraintsreferences/elicitation_techniques.md to address unclear points (e.g. "what would you do differently if X was 10% vs 50%?")assets/analysis_brief_template.md with the stakeholder; confirm understanding before any data is pulledassets/requirements_template.md (filled) — scope, data, output format, success criteriaassets/analysis_brief_template.md (filled) — authoritative scope document shared with stakeholder for sign-off