Translate technical analysis into business language. Use when explaining statistical concepts to non-analysts, simplifying technical findings, or bridging communication between data teams and business stakeholders.
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When technical output (model results, statistical tests, query findings) needs to be understood by a business audience. Also use to review your own writing before sending — it is easy to slip into jargon without noticing.
scripts/jargon_detector.py on the draft text to flag technical terms that need translation.scripts/readability_scorer.py to get Flesch-Kincaid grade level and sentence complexity metrics; target ≤ grade 10 for executive audiences.references/stakeholder_personas.md to select the persona that best matches your reader; each persona has vocabulary preferences and typical questions.references/translation_pattern_library.md to swap technical language for business equivalents (e.g., "p-value < 0.05" → "we're 95% confident this isn't random chance").references/metaphor_bank.md.assets/translation_template.md to produce the parallel technical/business version; keep the original in an appendix for technical reviewers.translation_template.md)No reviews yet. Tell others what you thought.
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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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