Purpose
Turn a raw analytics finding into a decision-ready narrative that a legal, operations, or executive audience can act on — leading with the insight, explaining the numbers in plain language, and connecting the data directly to a business or policy decision. Solves the recurring problem of technically correct analysis that stakeholders don't understand, don't trust, or can't act on.
When to Use
Invoke this skill when the user asks to:
- Present dashboard or model results to executives, a board, or non-technical stakeholders
- Translate a statistical or analytics finding into a business narrative
- Prepare a data story for a legal, compliance, or policy audience
- Explain what a metric change or A/B test result actually means for a decision
- Build talking points or a slide narrative around a data finding
- Reframe a technical report that "isn't landing" with leadership
Inputs Required
Before starting, collect:
- The core finding: The main result, metric, or model output to communicate (numbers, direction, magnitude)
- Audience: Who is receiving this — executives, legal/compliance, operations, board, or a mixed room
- Decision at stake: What the audience needs to decide or approve based on this data
- Supporting numbers: Key figures, comparisons, confidence levels, or sample sizes available
- Methodology in brief: How the finding was derived (test, model, cohort analysis) — for the appendix and credibility
- Known caveats or limitations: Anything that qualifies the finding (data gaps, assumptions, small samples)
- Prior context: What this audience already believes or has heard about this metric
If the decision at stake is unclear, ask one clarifying question: "What action do you want this audience to take after hearing this?" Do not proceed without it — a data story without a decision is just a report.
Instructions
Step 1: Extract the One-Sentence Headline
Lead with the insight, not the analysis. Write a single sentence that states what happened and why it matters to this audience's decision.
- Use the finding + consequence structure: "[What the data shows] means [what it costs, gains, or risks for us]"
- Convert statistics into stakes: a 12% churn increase becomes "we're losing roughly 1 in 8 renewal-eligible customers, worth about $X annually"
- Match the vocabulary to the audience: legal cares about exposure and precedent; ops cares about throughput and cost; executives care about revenue, risk, and growth
- Kill the hedge: no "the data suggests it may possibly indicate" — state it, then qualify separately in the caveats
Step 2: Explain the Numbers in Plain Language
Give the audience just enough to trust the headline without teaching them statistics.
- Anchor with a comparison: raw numbers mean nothing alone — compare to last quarter, to target, to a benchmark, or to a familiar reference point
- Translate technical terms: replace "statistically significant at p<0.05" with "this is a real pattern, not random noise — we'd see this by chance less than 1 in 20 times"
- Use one visual-friendly framing per point: "for every 100 cases, X now happens instead of Y"
- Size the effect honestly: distinguish a directionally interesting signal from a decision-grade result
- Avoid decimals the audience can't use — round to the precision the decision actually requires
Step 3: Connect Data to Decision
Bridge from "here's what's true" to "here's what we should do."
- State the recommended action explicitly and in one line
- Show the counterfactual: what happens if we do nothing
- Present 1–3 options where relevant, each with its data-backed tradeoff
- Tie each option to the metric it moves — do not recommend anything the finding doesn't support
Step 4: Frame Risk and Uncertainty Responsibly
Non-technical audiences either over-trust or over-dismiss data — pre-empt both.
- State the confidence level in plain terms: "high confidence," "early signal," or "directional only"
- Name the top 1–2 caveats that would change the recommendation if wrong
- For legal/compliance audiences: flag what the data does NOT prove, to avoid overstated claims and liability
- Distinguish correlation from causation explicitly when the analysis can't establish cause
Step 5: Build the Anticipated-Questions Appendix
Prepare the presenter for the room. Anticipate 4–6 questions this specific audience will ask, each with a tight answer.
- Include the skeptic's question ("How do we know this isn't seasonal / a data error / a one-off?")
- Include the "what do we do about it" follow-up
- Include one methodology question for the technically curious stakeholder
- Answers stay in plain language, 2–3 sentences each
Output Format
- Structure: A presentation-ready narrative document with labeled sections, front-loaded for a busy reader
- Length: 700–1,000 words
- Format: Structured document — short prose blocks and tight bulleted points, not a wall of text
- Sections: Headline Insight → What the Numbers Say → The Decision & Options → Risk & Confidence → Anticipated Questions (appendix)
- Tone: Confident and plain-spoken — a trusted advisor explaining stakes to a decision-maker, never lecturing, never buried in jargon
Quality Checklist
Before delivering output, verify:
- [ ] The headline is one sentence and states a consequence, not just a metric
- [ ] Every statistic is anchored to a comparison or converted into a stake the audience cares about
- [ ] No unexplained technical term survives (p-values, confidence intervals, model names are translated)
- [ ] A specific recommended action is stated — the narrative is not left open-ended
- [ ] The appendix contains 4–6 anticipated questions, including at least one skeptic question
- [ ] Caveats explicitly name what the data does NOT prove, calibrated to the audience
Notes
- The single most common failure is leading with methodology instead of the insight — always open with the "so what"
- For mixed rooms (e.g., legal + executives together), write the headline for the most senior decision-maker and route technical detail entirely to the appendix
- Legal and compliance audiences require the most conservative claims framing — when in doubt, understate the certainty and be precise about scope
- Numbers persuade, but stakes decide — a finding phrased as dollars, cases, or exposure moves a room faster than a percentage
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