Purpose
Translate a technical result — a model's performance, an A/B test outcome, an analysis finding — into a narrative that a non-technical stakeholder understands, remembers, and can act on. Solves the recurring problem of strong analysis dying in the last mile because it was communicated as methodology instead of consequence.
When to Use
Invoke this skill when the user asks to:
- Present model results or evaluation metrics to executives or product leads
- Turn an analysis or dashboard finding into a story for a business audience
- Prepare a readout for an A/B test, experiment, or model launch
- Explain why a technical result matters to people who won't read the notebook
- Reframe a "here's what the data says" summary into a "here's what we should do" narrative
- Build talking points for a stakeholder meeting or presentation
Inputs Required
Before starting, collect:
- The finding: The core result — model metrics, test outcome, analysis conclusion (paste numbers as they are)
- Audience: Who is hearing this — executives, product managers, sales, cross-functional peers, or a mix
- Business question: The decision or question this work was meant to inform
- Stakes: What changes based on this finding — a launch, a budget, a roadmap, a pivot
- Constraints or caveats: Confidence intervals, data limitations, or risks the audience must know
- Delivery format: Slide readout / written summary / verbal walkthrough / email
- Time available: How long the audience has (2 min, 5 min, 20 min)
If audience or delivery format is missing, assume a mixed non-technical audience receiving a 5-minute verbal walkthrough with slides, and note the assumption.
Instructions
Step 1: Extract the Headline Insight
Find the single most decision-relevant statement in the finding and write it as one sentence in business terms.
- State the consequence, not the method: not "the XGBoost model achieved 0.87 AUC" but "we can now flag 8 of 10 customers likely to churn before they leave"
- Lead with what changes for the business, not what the analysis did
- The headline must be true, defensible, and understandable to someone who has never opened a model
- If there are multiple findings, pick the one that most directly answers the business question — supporting findings become secondary beats
Step 2: Build the Three-Act Story Arc
Structure the narrative in three acts:
Act 1 — The Stakes (the "why we looked")
- Open with the business problem or question, framed as something the audience already cares about
- Establish the cost of not knowing — what was uncertain, what decision was blocked
- Keep it to 2-3 sentences; this is the hook, not the background
Act 2 — The Discovery (the "what we found")
- Present the headline insight, then 1-2 supporting findings that reinforce it
- Introduce numbers only after their meaning is established — meaning first, metric second
- Include the honest caveat here, framed as confidence rather than hedging (e.g., "reliable across the last three quarters of data")
Act 3 — The Path Forward (the "so what")
- State the recommended action or decision clearly
- Name the expected impact in the audience's units (revenue, time saved, risk reduced, customers retained)
- Give the audience a single clear next step or decision to make
Step 3: Translate the Metrics
For every technical metric that must appear, produce a plain-language translation using this pattern:
- Technical statement → What it means for the business → Optional analogy if it helps
- Precision/recall → "how often we're right when we flag something" / "how many real cases we catch"
- AUC or accuracy → framed as decision reliability, not a score
- p-value or confidence interval → "how sure we are this isn't a fluke"
- Lift or effect size → the concrete outcome in dollars, users, or percentage of the relevant base Never present a raw metric without a translation beside it. Cut any metric that doesn't change the decision.
Step 4: Match Tone and Depth to Audience
- Executives: Decision and impact first; one or two numbers; no methodology
- Product managers: Impact plus enough mechanism to reason about edge cases and tradeoffs
- Cross-functional/mixed: Narrative-forward with an appendix line for anyone who wants the detail
- Remove jargon or define it inline the first time; assume no shared vocabulary with the data team
Step 5: Write the Speaker Notes
Produce a beat-by-beat walkthrough calibrated to the time available:
- One line per beat covering what to say and roughly how long
- Flag the single moment to slow down (the headline insight)
- Include one anticipated question with a one-sentence prepared answer
- End with the ask stated in a single sentence the presenter can deliver verbatim
Output Format
- Structure: Headline insight → three-act narrative → metric translation table → audience-calibration note → speaker notes
- Length: 700–1,000 words
- Format: Structured document — prose for the narrative, a table for metric translations, a numbered list for speaker notes
- Sections: Headline Insight / The Narrative (three acts, labeled) / Metric Translation Table / Audience Note / Speaker Notes & Anticipated Q
- Tone: Confident, plainspoken, decision-oriented — a strong communicator who respects both the data and the audience's time; no hype, no false certainty
Quality Checklist
Before delivering output, verify:
- [ ] The headline insight is stated in business consequence terms, not methodology
- [ ] The narrative has all three acts, each explicitly labeled
- [ ] Every technical metric in the output has a plain-language translation beside it
- [ ] At least one honest caveat is included and framed as confidence, not apology
- [ ] The recommended action is a single clear next step or decision
- [ ] Speaker notes fit the stated time budget and end with a verbatim ask
Notes
- The most common failure is leading with the model and burying the decision — always start from what the audience already cares about
- If the finding is genuinely inconclusive, say so plainly and frame the next step as "what we'd need to decide" rather than manufacturing a takeaway
- Analogies are powerful but risky; use one only when it clarifies without distorting, and drop it if it requires its own explanation
- Numbers persuade in threes at most — if a slide has more than three numbers on it, the story is competing with a spreadsheet
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