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SQL analysis to narrative converter

Converts raw query results, metrics, and analytical findings into a concise stakeholder-ready narrative — leading with the headline insight, framing numbers in business terms, and closing with recommended actions. Use…

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Purpose

Turn quantitative findings — query outputs, metric changes, cohort comparisons — into a tight written narrative that a busy executive can absorb in under two minutes, leading with the insight rather than the methodology. Solves the analyst's recurring problem: the numbers are correct, but the story lands flat because it reads like a data dump instead of a decision brief.

When to Use

Invoke this skill when the user asks to:

  • Turn SQL query results or a dashboard export into a written summary
  • Write up analysis findings for leadership or a stakeholder deck
  • Explain a metric change (churn spike, revenue dip, conversion lift) to a non-technical audience
  • Convert a table of numbers into a narrative or executive summary
  • Prepare talking points or a written brief from an analysis they already ran
  • Reframe a technical finding so a marketing, sales, or product lead can act on it

Inputs Required

Before starting, collect:

  • The findings: Raw numbers, query output, table, or bullet list of what the analysis showed
  • The business question: What decision or question prompted this analysis
  • Audience: Who reads this — CEO, VP, cross-functional partner, board (determines depth and jargon tolerance)
  • Comparison context: Prior period, target, benchmark, or segment being compared against
  • Known drivers or hypotheses: Any causes the analyst suspects behind the numbers
  • Desired action: What the analyst wants the reader to do or decide (if known)
  • Length constraint: Slide blurb, email, one-pager (defaults to one-pager if unspecified)

If the desired action or known drivers are missing, produce the narrative from the data alone and flag where a causal claim would need more analysis to support.

Instructions

Step 1: Extract the Headline Insight

Identify the single most decision-relevant finding and state it as one sentence a non-analyst would repeat.

  • Lead with the "so what," not the metric name — "New customers are churning twice as fast as last year" beats "Q3 90-day retention was 41%"
  • Attach the number that proves it, expressed as change or comparison, not a bare figure
  • If multiple findings compete, rank by business impact and lead with the highest — everything else becomes supporting detail

Step 2: Translate Numbers Into Business Terms

Convert every metric into language the audience already uses.

  • Relative over absolute: "up 34%" or "3x the target" lands harder than a raw count, unless the raw number itself is the shock
  • Dollarize where possible: Convert rates and counts into revenue, cost, or headcount impact when the data allows
  • Right-size precision: Round for readability (say "roughly $1.2M" not "$1,184,203") — precision signals rigor to analysts but noise to executives
  • Kill the jargon: Replace "cohort," "attribution window," "p-value," "join," "DAU/MAU" with plain equivalents or drop them

Step 3: Structure the Narrative in the Inverted Pyramid

Order information by descending importance so the reader can stop at any point and still have the essential takeaway.

  1. Headline insight (Step 1 sentence)
  2. The context — what was compared, over what period, why it matters now
  3. The supporting evidence — 2–4 numbers that substantiate the headline, each tied to a business meaning
  4. The likely driver — what appears to be causing it, with confidence qualified
  5. The recommendation — what to do, decide, or watch next

Step 4: Qualify Causation Honestly

Distinguish what the data shows from what it suggests.

  • Use hedged language for unproven causes: "This coincides with...", "A likely contributor is...", "Worth investigating whether..."
  • Reserve confident language for what the query actually measured
  • Never present a correlation as a cause — flag it as a hypothesis and note what analysis would confirm it

Step 5: Close With a Decision or Next Step

End on action, not summary.

  • State the recommended action or the decision the reader now needs to make
  • If the analysis is inconclusive, say what specifically would resolve it and how long it would take
  • Avoid ending on "let me know if you have questions" — end on the point

Output Format

  • Structure: Inverted pyramid — headline first, evidence and recommendation following
  • Length: One-pager default (250–400 words); slide blurb (60–90 words) or email (120–200 words) if specified
  • Format: Prose paragraphs with a bolded one-line headline at top; supporting numbers may use a short bullet cluster if 3+ figures need listing
  • Sections: Headline → Context → Evidence → Likely driver → Recommendation
  • Tone: Confident, plainspoken, executive-brief — no methodology walkthrough, no hedging on what's proven, honest hedging on what isn't

Quality Checklist

Before delivering output, verify:

  • [ ] The opening sentence states the insight, not the metric — a non-analyst could repeat it
  • [ ] Every number appears with a comparison or business meaning attached, never bare
  • [ ] No unexplained technical terms remain (no "cohort," "join," "p-value," raw metric names without translation)
  • [ ] Correlations are labeled as hypotheses, not stated as proven causes
  • [ ] The narrative ends on a recommendation or decision, not a summary or sign-off
  • [ ] Total length matches the specified constraint (count words)

Notes

  • The most common failure mode is burying the headline under methodology — the reader should never have to reach paragraph three to learn what happened
  • When the finding is bad news, lead with it plainly; softening the headline erodes trust faster than the number itself
  • If the analyst provides five findings, resist listing all five equally — a narrative has one protagonist, and the rest are supporting cast
  • Dollarizing impact is the single highest-leverage move for executive audiences; when the data supports it, always do it

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:

  1. Drag this file into a new chat, or copy and paste its contents
  2. 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