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Translating technical findings for non-technical audiences. Narrative frameworks (Pyramid Principle, SCQA), plain-language translation, executive summaries, policy briefs, causal language.
Passed 19 of 22 test cases with the skill, and 14 of 22 without it.
and 2 more in the full report
5 fixed14 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am drafting an executive memo for senior leaders on our automated fraud detection system. My current draft starts with our data cleaning steps, feature engineering pipeline, and cross-validation setup before revealing our recommended threshold setting on page 4. How should I restructure this document for maximum executive impact?
| Checked | Without skill | With skill |
|---|---|---|
| advises structuring the communication using the Pyramid Principle framework | Missed | Passed |
The instructions your model reads when the skill loads.
Translating technical data science findings for non-technical audiences. Covers audience analysis, narrative frameworks (Pyramid Principle, SCQA, AIDA), plain-language translation, executive summaries, policy briefs, causal language guidance, hedging and uncertainty communication, and accessibility standards. Complements data-scientist visualization references — handles what story to tell and to whom, not how to build charts. Use when presenting findings to stakeholders, writing executive summaries or policy briefs, communicating statistical results to non-statisticians, or reviewing a draft deliverable for clarity and audience fit.
Guidance for translating rigorous data science work into clear, compelling communication for non-technical audiences. This skill is additive to data-scientist's visualization references — it covers what story the chart tells and to whom, not how to build the chart.
Boundary with data-scientist: The data-scientist skill handles chart construction, encoding, color palettes, and export standards. This skill handles audience adaptation, narrative structure, plain-language translation, deliverable formatting, and communication quality review.
| File | Purpose | When to Read |
|---|---|---|
audience-analysis.md | Five audience types with strategy tables | Identifying who you're writing for |
narrative-frameworks.md | Six narrative structures with selection guide | Choosing how to structure your story |
plain-language.md | Jargon translation, hedging, uncertainty, causal language | Writing findings in accessible language |
deliverable-templates.md | Executive summary, policy brief, presentation, talking points | Formatting a specific deliverable type |
communication-review.md | 10-point checklist, common pitfalls, seven deadly sins | Reviewing a deliverable before finalization |
accessibility-equity.md | WCAG standards, people-first language, equity-aware framing | Ensuring inclusive, accessible communication |
audience-analysis.md, then narrative-frameworks.md, then deliverable-templates.mdplain-language.md firstcommunication-review.mdaccessibility-equity.mdIdentifying your audience?
├─ Academic researchers or peer reviewers
│ └─ ./references/audience-analysis.md (Academic section)
├─ Policymakers or legislative staff
│ └─ ./references/audience-analysis.md (Policy section)
├─ Executives or board members
│ └─ ./references/audience-analysis.md (Executive section)
test cases passed, vs 14 / 22 without
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