Verified against Gemini in Sheets · 2026-05-27
Turn a raw Google Sheet into a plain-English insight brief
A prompt that uses Gemini's @Sheets mention to analyze real spreadsheet data in place and return a structured insight brief with numbers attached, instead of a vague 'looks fine' summary.
The prompt
Ready to copy — highlighted parts are example details you can swap.
@Sheets the "Q3 Support Tickets" sheet in the Customer Ops folder Analyze the data in this sheet, specifically the "Raw Tickets" tab, columns A through H. QUESTIONS TO ANSWER Which ticket category is growing fastest month over month, and is response time correlated with ticket category? INSTRUCTIONS 1. State how many rows/columns you actually read, so I know if you saw the whole sheet or just a preview. 2. Identify the 3 most significant trends or outliers, referencing exact column names and row ranges — not just "some values look high." 3. Flag any data quality issues you notice (blank cells, inconsistent formats, likely typos) before drawing conclusions from that data. 4. For each finding, state the actual numbers behind it, not just a qualitative claim. 5. End with 3 concrete next actions someone could take from this data, each tied to a specific finding above. Don't invent a trend from too few data points — if a pattern is based on fewer than 20 rows, label it as low-confidence.
Customize the highlighted detailsoptional — the prompt above already works
Why this works
The @Sheets mention lets Gemini read live cell values rather than working from a description or a partial screenshot, so requiring it to state how many rows it actually read is a direct check against silent truncation on large sheets, which is the most common way a 'complete' analysis is actually based on the first visible page. Requiring exact numbers instead of qualitative claims ('some values look high') forces every finding to be independently checkable against the sheet rather than taken on faith. The minimum_sample_size rule specifically counters a well-documented LLM tendency to describe a pattern found in a handful of rows with the same confidence as one backed by hundreds — labeling thin evidence as low-confidence keeps the brief honest about what the data actually supports.
What you get back
Read 342 rows across 8 columns (full sheet, no truncation). Trend 1: "Billing" tickets grew from 34/month (July) to 61/month (Sept) — a clear upward trend, high confidence (n=95 rows). Trend 2: Average response time for "Bug Report" tickets (14.2 hrs) is roughly double every other category (avg 7.1 hrs) — worth investigating triage routing. Data quality: 11 rows have blank "Category" values — excluded from category-level trends above. Recommendation 1: Investigate why Billing ticket volume nearly doubled — check for a pricing or invoicing change in August.
Verified against
Gemini in Sheets Gemini 3 Pro · 2026-05-27
Changelog
- 2026-05-27 — Initial publish, verified against Gemini in Sheets on Gemini 3 Pro.
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