Purpose
Turn a technical finding — a SQL query result, a model output, a statistical test, a dashboard trend — into a plain-language summary calibrated to a specific non-technical audience, leading with the business implication and a clear recommended action rather than the methodology. Solves the recurring problem where accurate analysis fails to drive decisions because stakeholders can't extract the "so what."
When to Use
Invoke this skill when the user asks to:
- Translate a SQL query result or analysis into language executives or marketers will understand
- Write the summary or "key takeaways" section of a report for a non-technical audience
- Explain what a model output, statistical result, or A/B test means for the business
- Prepare a data finding for a stakeholder update, all-hands, or leadership deck
- Reframe a technical insight around a decision the audience actually needs to make
- Strip jargon out of an analysis without losing accuracy or overclaiming
Inputs Required
Before starting, collect:
- The technical finding: The raw insight — query result, metric change, model output, test result, or a description of what the data shows
- The audience: Who is reading this (e.g., CFO, VP of Marketing, sales team, product managers, board)
- The decision at stake: What choice this finding should inform, if known
- Confidence level: How certain the finding is — directional signal, statistically significant, or preliminary
- Key numbers: The specific figures that matter (magnitude, sample size, time period, comparison baseline)
- Delivery format: Slide bullet, email paragraph, Slack message, or report section
- Sensitivities: Anything politically charged or likely to trigger pushback
If the decision at stake or confidence level is missing, infer a reasonable default from the finding and state the assumption explicitly.
Instructions
Step 1: Strip the Methodology, Keep the Meaning
Separate how the finding was produced from what it means. The audience does not need the join logic, the model type, or the significance test — they need the conclusion.
- Rewrite any technical term into its business equivalent: "churn regression coefficient" → "the biggest driver of customers leaving," "p < 0.05" → "we're confident this isn't random," "cohort" → "customers who signed up in the same month"
- Preserve one anchor number the audience can hold onto — magnitude beats precision (say "roughly a third" before "32.7%")
- Never round away a fact that changes the conclusion
Step 2: Lead With the Headline Takeaway
Write a single sentence that states the finding as a business fact, not a data observation.
- Bad: "The conversion rate for the treatment group was 4.2% vs. 3.1% for control."
- Good: "Sending the reminder email lifts sign-ups by about a third — enough to add roughly 400 new customers a month at current volume."
- The headline must contain: what happened, how big, and why it matters — in that order
Step 3: Write the "So What" (Business Implication)
Connect the finding to money, time, risk, or a goal the audience owns.
- Frame in the audience's own metrics: marketers care about CAC and pipeline, finance cares about margin and forecast, product cares about retention and adoption
- Translate percentages into consequences: a 2-point retention lift is meaningless until it becomes "$X more in annual recurring revenue"
- If the finding is a warning, name the cost of ignoring it
Step 4: Recommend an Action
State what you think the audience should do — one clear recommendation, not a menu.
- Match the action's ambition to the confidence level: a directional signal warrants a test, a significant result warrants a rollout
- Be specific about the next step and who owns it
- If confidence is low, the recommended action is often "run this test / gather more data before deciding" — say so plainly
Step 5: Calibrate the Certainty
State how much weight the audience should put on this, in plain terms.
- Strong result: "We can act on this."
- Directional: "This points somewhere, but let's confirm before betting big."
- Preliminary: "Early signal only — treat as a hypothesis."
- Name the one caveat that most affects interpretation (small sample, short window, confounding factor) in a single sentence — no methodology essay
Step 6: Pre-Empt the Top 3 Questions
Anticipate what this specific audience will push back on and answer it before they ask.
- Finance will ask about assumptions and downside; marketing will ask about scale and cost; executives will ask "what does this cost and when do we see results?"
- Each answer: one or two sentences, no hedging
- Frame each as a Q → A pair the user can drop into a follow-up conversation
Output Format
- Structure: Headline takeaway (1 sentence, bolded) → So What (2–3 sentences) → Recommended Action (1–2 sentences) → Confidence & Caveat (1–2 sentences) → Anticipated Questions (3 Q&A pairs)
- Length: 200–350 words for the core summary, plus the 3 Q&A pairs
- Format: Prose for the summary sections; Q&A pairs as a short list; adapt density to the specified delivery format (tighter for Slack/slide, fuller for email/report)
- Sections: Headline → So What → Recommended Action → Confidence → Anticipated Questions
- Tone: Confident and plain-spoken — a trusted advisor briefing a decision-maker, not a technical report. No jargon, no hedging chains ("it seems like it might possibly")
Quality Checklist
Before delivering output, verify:
- [ ] The headline states a business fact with magnitude and stakes — not a raw metric comparison
- [ ] Zero unexplained technical terms remain (no "coefficient," "p-value," "cohort," "regression" without translation)
- [ ] The "So What" is framed in the audience's own success metrics, not the analyst's
- [ ] Exactly one recommended action is given, matched to the stated confidence level
- [ ] The confidence statement names the single most important caveat in plain language
- [ ] Three anticipated questions are answered, each specific to this audience's likely concerns
Notes
- The most common failure mode is leading with methodology to establish credibility — resist it. Credibility comes from the recommendation being right, not from showing the math.
- When a finding is genuinely uncertain, say so clearly; a translated insight that overclaims destroys trust faster than one that's cautious.
- If the audience spans multiple functions (e.g., a leadership meeting with both finance and marketing), write the "So What" to the most senior decision-maker and route function-specific detail into the anticipated questions.
- Match number precision to the format: a board slide gets rounded figures, a finance memo gets exact ones with the baseline stated.
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