Structured requirements elicitation for analysis requests. Use when scoping new analysis projects, clarifying ambiguous business questions, or documenting analysis acceptance criteria with stakeholders.
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At the start of any non-trivial analysis request, especially when the ask is vague ("can you look into X?"), when multiple stakeholders have a stake in the outcome, or when the result will drive an important decision. Spending 30 minutes on requirements prevents days of rework.
assets/interview_guide.md to surface: the business decision being made, who the audience is, what "done" looks like, and what constraints exist.references/decision_maker_framework.md to classify the decision (strategic / operational / tactical) and calibrate the required rigour and format.assets/requirements_doc_template.md covering: business question, success criteria, scope inclusions/exclusions, data sources, and timeline.references/elicitation_techniques.md for any requirement still unclear after the interview (5-whys, scenario walkthrough, MoSCoW prioritisation).assets/analysis_brief_template.md, which becomes the authoritative scope document for the project.requirements_doc_template.md)analysis_brief_template.md)还没有评价。欢迎分享你的使用感受。
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Rigorous A/B test statistical analysis. Use when analyzing experiment results, calculating statistical significance, checking for sample ratio mismatch, or validating test design before launch.
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Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.
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Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.
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Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.
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Pre-delivery quality assurance for analysis work. Use when reviewing analysis before sharing with stakeholders, checking for completeness, validating assumptions, or ensuring clarity of recommendations.
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Post-analysis learning and process improvement. Use when completing major analysis projects, documenting lessons learned, or improving team analytical practices.
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