Analytics findings presenter · free AI skill

Converts raw stats, metrics, or bullet-point findings into a clear business-language insight summary — translating each finding into plain English, adding so-what interpretation, quantifying business impact, and…

Purpose

Turn raw metrics and technical findings into a stakeholder-ready insight summary where every data point is translated into plain business language, framed with its "so what," and paired with a concrete recommended action. Eliminates the gap where analysts present numbers and business leaders ask "okay, but what do we do about this?"

When to Use

Invoke this skill when the user asks to:

Translate analytics findings or dashboard metrics into a stakeholder summary
Write the narrative or talking points for a data readout, QBR, or leadership deck
Add "so what" framing and recommendations to a set of raw stats
Present A/B test, cohort, funnel, or campaign results to a non-technical audience
Rewrite technical findings so executives understand what to do next
Prepare an executive summary or TL;DR from a longer analysis

Inputs Required

Before starting, collect:

Raw findings: The bullet points, stats, query outputs, or metrics to translate
Audience: Who reads this — executives, marketing lead, product team, board, etc.
Business context: What decision or goal these findings relate to
Time period: The window the data covers (e.g., Q3, last 30 days, since launch)
Comparison baseline: What "good/bad" is measured against — target, prior period, benchmark
Format needed: One-page summary / slide talking points / email / TL;DR
Known actions on the table: Any decisions or options already being considered

If comparison baseline or business context is missing, flag the assumption and interpret findings against the most likely business goal.

Instructions

Step 1: Separate Signal From Noise

Review all raw findings and sort into three buckets:

Headline findings: The 2–4 findings that actually change a decision or reveal something material
Supporting findings: Context that reinforces or explains a headline
Cut: Findings that are technically true but don't move any business needle — do not include these unless the user explicitly wants a full data appendix

Lead with the headline that matters most to the audience's goal, not the most statistically interesting one.

Step 2: Translate Each Finding to Plain Business Language

For every finding kept, rewrite it using this discipline:

Remove jargon: "p-value," "statistical significance," "cohort retention curve," "attribution model" → plain equivalents ("we're confident this isn't random," "the customers who signed up in March," "how we credited the sale")
Convert relative to absolute where it clarifies stakes: "conversion up 12%" → "conversion up 12%, which is about 340 more sign-ups a month"
Anchor every number to the baseline: a number alone means nothing; state what it's compared to (target, last period, benchmark)
Use the audience's vocabulary: revenue, customers, cost, time, risk — not table names or metric labels

Step 3: Add the "So What" for Each Finding

This is the core of the skill. For each headline finding, write one to two sentences answering: why should this audience care, and what does it mean for the business?

State the implication, not just the observation ("checkout drop-off is 40%" → "we're losing roughly 4 in 10 buyers at the final step — the single largest leak in the funnel")
Connect to money, growth, risk, or effort wherever possible
If a finding is ambiguous, state the most likely interpretation and note what would confirm it

Step 4: Recommend Next Steps

Pair the findings with a short, ranked set of recommended actions:

3–5 recommendations, each tied directly to a specific finding
Each recommendation names an owner-level action, an expected outcome, and (where possible) rough effort or priority (High / Medium / Low)
Distinguish "do this now" from "investigate further" — don't present an open question as if it were a solution
Be opinionated: state which action to take first and why

Step 5: Write the Executive Summary

Open the deliverable with a 2–4 sentence TL;DR that a busy leader could read alone and still know: what happened, what it means, and what you recommend. This is written last but appears first.

Output Format

Structure: TL;DR up top → headline findings (translated + so-what) → supporting context → ranked recommendations
Length: 400–700 words (or adjusted to the requested format — email, slide notes, one-pager)
Format: Structured document with short paragraphs and bulleted findings; a compact recommendations list or table at the end
Sections: Executive Summary (TL;DR) → Key Findings → What This Means → Recommended Next Steps
Tone: Clear, confident, business-first — like a trusted analyst briefing a busy executive; no hedging, no jargon, no data-dumping

Quality Checklist

Before delivering output, verify:

[ ] Every finding kept has an explicit "so what" — no bare stats without interpretation
[ ] No unexplained technical jargon remains (p-value, cohort, attribution, etc. are either removed or translated)
[ ] Every key number is anchored to a baseline (target, prior period, or benchmark)
[ ] There are 3–5 recommendations, each tied to a specific finding
[ ] The TL;DR can stand alone — a leader who reads only it knows what happened and what to do
[ ] Findings are ordered by business relevance to the audience, not by statistical interest

Notes

The most common failure in analyst communication is presenting the observation and stopping — the "so what" is what earns the analyst a seat at the decision table
When a finding is interesting but not actionable, name that honestly ("worth watching, no action yet") rather than inventing a recommendation
If the raw findings contradict each other or a headline is based on a small sample, surface that caveat plainly — credibility comes from calling out uncertainty, not hiding it
Percentages impress; absolute numbers persuade — always give leaders the real-world magnitude behind a rate

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