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Data insight summary generator

Converts a table, dataset, or chart description into a decision-oriented insight summary written in smart-brevity plain language — leading with the takeaway, quantifying the change, and stating the recommended action.…

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Purpose

Turn a table, query result, or chart description into a concise, decision-oriented insight summary that leads with the finding, quantifies the movement, and names the action — so stakeholders read one screen and know what happened, why it matters, and what to do. Eliminates the analyst's most common failure mode: burying the insight under process and jargon.

When to Use

Invoke this skill when the user asks to:

  • Turn a table, query output, or metrics dump into a written summary for stakeholders
  • Write the narrative or executive summary attached to a dashboard or report
  • Explain what a chart or trend means in plain language for a non-technical audience
  • Draft a decision brief or "so what" section from analysis results
  • Summarize a week/month/quarter of metrics into the key takeaways
  • Convert a dense analytics deliverable into a smart-brevity update

Inputs Required

Before starting, collect:

  • The data: The table, chart description, query result, or metric values (paste the numbers or describe the chart — dimensions, measures, time period)
  • Comparison basis: What the numbers are measured against (prior period, target, forecast, cohort, or benchmark)
  • Audience: Who reads this — executives, marketing team, product, finance, board
  • Decision at stake: What action or question this summary is meant to inform
  • Known context: Any recent event that may explain the numbers (launch, outage, seasonality, campaign)
  • Length target: One-liner / short paragraph / full brief (default: short brief)

If comparison basis is missing, ask for it — a number without a reference point produces no insight. If audience or decision is missing, default to "operating leadership deciding where to focus next" and note the assumption.

Instructions

Step 1: Extract the Signal

Read the data and isolate what actually changed or stands out. Distinguish signal from noise:

  • The headline movement: The single largest, most decision-relevant change (up/down/flat, by how much)
  • The direction vs. expectation: Is this above/below/on target, prior period, or forecast?
  • Magnitude: Always express change both ways when possible — absolute (+1,240 signups) and relative (+18%)
  • Outliers and segments: Which slice drove the movement (a channel, region, cohort, product line)
  • What's flat that should have moved (a non-event can be the insight)

Discard anything that is directionally uninteresting or too granular to change a decision.

Step 2: Find the "So What"

For the headline movement, answer: why does this matter to this audience's this decision?

  • Connect the metric to a business outcome the audience owns (revenue, cost, retention, efficiency)
  • If a cause is knowable from context, state it as likely — not as proven ("driven by the paid campaign launched March 3") — and flag when it's a hypothesis
  • If the cause is unknown, say so explicitly rather than inventing one

Step 3: Determine the Action

State what the reader should do or decide. Choose the sharpest applicable frame:

  • Continue/scale: "This is working — the case to increase budget"
  • Investigate: "This needs a look before we act — here's the specific question"
  • Correct: "This is off-target — here's the likely lever"
  • Monitor: "Not urgent yet, but watch this threshold" If no action is warranted, say "No action needed" — do not manufacture urgency.

Step 4: Write in Smart-Brevity Structure

Assemble the summary in this order:

  1. The one-line takeaway — bold, front-loaded, contains the number. The reader could stop here and be informed.
  2. Why it matters — one or two sentences tying the number to the audience's outcome.
  3. The detail — 2–4 supporting facts as tight bullets (segment breakdown, comparison, driver).
  4. What to do — the recommended action or open question, stated plainly.

Writing rules:

  • Lead every unit with the conclusion, not the setup
  • One idea per sentence; cut every clause that doesn't change meaning
  • Use plain words over analytics jargon (say "signups grew" not "acquisition volume trended positively")
  • Round numbers to decision-relevant precision (18%, not 18.34%) unless precision matters
  • Never open with "This report shows" or "The data indicates"

Step 5: Caveat Honestly

Add a one-line caveat only if the data warrants it: small sample size, incomplete period, definitional change, or correlation-not-causation. Keep it to a single sentence. Omit if the data is clean.

Output Format

  • Structure: Front-loaded — bold takeaway line, then Why it matters, then bulleted detail, then What to do
  • Length: Short brief default — 90–160 words; one-liner and full-brief variants on request (full brief up to 300 words)
  • Format: Bold lead line + short prose + tight bullets; scannable in under 20 seconds
  • Sections: Takeaway (bold) → Why it matters → The detail (bullets) → What to do → optional one-line caveat
  • Tone: Plain-spoken, confident, decision-first — an analyst who respects the reader's time and never hides behind jargon

Quality Checklist

Before delivering output, verify:

  • [ ] The takeaway line contains a specific number and states direction (up/down/flat vs. a reference)
  • [ ] Every number is expressed against a comparison basis — no orphan figures
  • [ ] The summary names a concrete action, an open question, or explicitly states "no action needed"
  • [ ] No opening filler ("This report shows," "The data indicates") and no undefined jargon
  • [ ] Any stated cause is flagged as confirmed or hypothesized — never asserted without basis
  • [ ] Total length is within the target range (default 90–160 words)

Notes

  • The most common revision request is "get to the point faster" — when in doubt, cut the setup and lead harder with the number.
  • Correlation is not causation: if the audience will act on a stated driver, the hypothesis flag protects them from acting on a false cause.
  • A flat number can be the headline when the audience expected movement — don't skip the "nothing happened" insight when it's decision-relevant.
  • For recurring reports, keep the takeaway structure identical week to week so readers learn where to look — only the numbers and action change.

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