Purpose
Transform technical analysis into a decision-ready narrative that leads with the insight, explains what it means in business terms, and tells stakeholders exactly what to do next — eliminating the gap between what the data shows and what a non-technical audience actually understands and acts on.
When to Use
Invoke this skill when the user asks to:
- Turn analysis results, a model output, or query findings into a presentation or exec summary
- Explain a statistical or ML result to a non-technical audience without the math
- Write the narrative for a slide deck, readout, or stakeholder email about data findings
- Reframe a dashboard or metrics report into a "so what / now what" story
- Prep talking points for presenting analysis to leadership or a cross-functional team
- Translate a confusing or nuanced result into a clear, defensible recommendation
Inputs Required
Before starting, collect:
- The findings: Raw results — key numbers, model metrics, trends, comparisons, or query outputs
- Business question: The decision or question the analysis was meant to inform
- Audience: Who receives this (executives, marketing, product, finance, mixed cross-functional)
- Decision at stake: What action or investment hinges on this
- Format needed: Slide narrative / exec email / verbal talking points / one-pager
- Confidence level: How solid the findings are — strong signal, directional, or preliminary
- Known sensitivities: Any results that are politically charged, counterintuitive, or bad news
If audience or decision is unclear, default to a mixed executive audience oriented around a resource-allocation decision, and note the assumption.
Instructions
Step 1: Extract the Single Headline Insight
Distill everything into one sentence a busy executive would repeat to a colleague.
- Lead with the business outcome, not the metric: "Churn is concentrated in the first 30 days" — not "The Kaplan-Meier curve shows steep early decay"
- State the magnitude in terms the audience feels ("we're losing ~$400K/quarter"), not raw units
- If findings are directional, say so in the headline — don't overclaim precision you don't have
Step 2: Build the 3-Act Story Arc
Structure the narrative as a story, not a report dump.
- Act 1 — The situation: The business context and the question that prompted the analysis. One or two sentences that ground the audience in why this matters now.
- Act 2 — The complication/finding: What the data revealed, especially the surprising or decision-relevant part. This is the tension — the thing that changes the current plan.
- Act 3 — The resolution: What this means and the path forward. Point directly at the decision.
- Each act gets a plain-language framing; no method names, no p-values in the arc itself.
Step 3: Translate Every Metric into Plain Language
For each technical term or metric in the findings, provide a stakeholder translation:
- Percentages and ratios → concrete counts or dollars ("a 3-point lift" → "roughly 1,200 more sign-ups a month")
- Statistical terms → confidence in words ("statistically significant" → "we're confident this isn't a fluke"; "correlation" → "these move together, but we haven't proven one causes the other")
- Model metrics → business meaning ("0.82 AUC" → "the model correctly flags ~4 of 5 at-risk accounts")
- Never leave a raw metric unexplained. If it appears, it gets a translation.
Step 4: Add the "So What / Now What" Recommendation
Explicitly state the recommended action, ranked if there are several.
- Frame each recommendation as: what to do, why the data supports it, and the expected impact
- Distinguish "the data says" (fact) from "I recommend" (judgment) — never blur the two
- Include the cost of inaction where relevant — it sharpens urgency honestly
Step 5: Handle Honesty and Caveats Without Undermining the Story
- State the top 1–2 limitations in plain terms, framed as "what we'd need to be more certain" rather than a wall of hedges
- For counterintuitive or bad-news findings, name the discomfort directly and pivot to the constructive angle
- Do not bury the headline under caveats — caveats live in a dedicated section, not the lead
Step 6: Prep Anticipated Questions
Draft the 4–5 hardest questions this audience will ask, each with a tight, honest answer.
- Include at least one methodology-skeptic question ("How do you know it's not just seasonality?")
- Include at least one "what should we do differently" question
- Keep answers to 2–3 sentences — presentation-ready, not essays
Output Format
- Structure: Headline insight up top, then the 3-act narrative, then supporting sections
- Length: 700–1,000 words
- Format: Structured document — prose narrative for the story arc; bulleted lists for translations, recommendations, and Q&A
- Sections: Headline Insight → The Story (3 acts) → Metrics Translated → Recommendation (So What / Now What) → Caveats & Confidence → Anticipated Questions
- Tone: Confident and plain-spoken — a trusted advisor briefing a decision-maker, never a lecture or a lab report
Quality Checklist
Before delivering output, verify:
- [ ] The headline insight is a single sentence and leads with business outcome, not method
- [ ] The narrative follows all three acts (situation, finding, resolution) in order
- [ ] Every technical term or metric in the findings has a plain-language translation — none left raw
- [ ] The recommendation section clearly separates "the data says" from "I recommend"
- [ ] Caveats are confined to their own section and framed constructively, not scattered through the lead
- [ ] Anticipated Questions includes 4–5 questions with 2–3 sentence answers, including at least one skeptic question
Notes
- The single biggest failure mode in data storytelling is leading with methodology; if the audience has to wait for the "so what," you've lost them — the headline always comes first.
- When the finding is bad news, resist softening it into meaninglessness. Executives trust analysts who deliver uncomfortable truths clearly, then point to a path forward.
- Match jargon tolerance to the audience: a product team can hear "conversion rate"; a board cannot hear "regularization." When in doubt, translate down.
- If the confidence level is preliminary, the entire story should be framed as "here's what we're seeing so far and what we'd test next" — never dress up a hunch as a conclusion.
How to use this skill
This file is a Claude Code skill in Anthropic's standard format — markdown with YAML frontmatter at the top.
Installing in Claude Code:
~/.claude/skills/<folder-name>/SKILL.md— auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)<your-project>/.claude/skills/<folder-name>/SKILL.md— auto-loads only when working in that project (recommended for project-specific skills)
Restart Claude Code or open a new session to register.
Using in Claude.ai, ChatGPT, Gemini, or other AI tools:
- Drag this file into a new chat, or copy and paste its contents
- Send your task or request as your next message — the AI will apply the skill where it fits
For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills