CSV/Dataset Explainer
Takes a CSV header + sample and tells you what the dataset actually contains, what the columns likely mean, and what questions you could answer with it.
The prompt
I'll paste a CSV header + a few sample rows. Tell me what this dataset actually is. Output: **What this dataset appears to be** — 1-2 sentences. What kind of records, what domain, what time period if visible. **Column-by-column read** — for each column: - What it probably represents - The data type - Any weirdness (mixed types, likely encoding issue, suspiciously round numbers) - Confidence: high / medium / low **Questions you could answer with this data** — 5-8 specific analytical questions. NOT "what patterns exist" — real questions like "which region has the highest average order value" or "does churn correlate with plan tier." **Questions you CAN'T answer with this alone** — things that would require joining to another dataset (which one), a time dimension you don't have, etc. **Suggested first three analyses** — the highest-value places to start. Rules: - Never claim certainty about column meanings if the header is ambiguous. Mark as low confidence. - If the sample suggests PII, flag it and remind me to handle carefully. - If the data appears to be a subset (looks like a query result), note it. ## How to use it Paste this prompt, then paste your CSV header + 5-10 sample rows. Use the output to write your first analysis plan before you touch a chart or formula. ## Why it works Analysts often waste hours re-deriving what a column means from scratch. A structured read-out gets you oriented fast, and the "can't answer" section stops you from chasing a question the data won't support.
How to use it
I’ll paste a CSV header + a few sample rows. Tell me what this dataset actually is.
Output:
What this dataset appears to be — 1-2 sentences. What kind of records, what domain, what time period if visible.
Column-by-column read — for each column:
- What it probably represents
- The data type
- Any weirdness (mixed types, likely encoding issue, suspiciously round numbers)
- Confidence: high / medium / low
Questions you could answer with this data — 5-8 specific analytical questions. NOT “what patterns exist” — real questions like “which region has the highest average order value” or “does churn correlate with plan tier.”
Questions you CAN’T answer with this alone — things that would require joining to another dataset (which one), a time dimension you don’t have, etc.
Suggested first three analyses — the highest-value places to start.
Rules:
- Never claim certainty about column meanings if the header is ambiguous. Mark as low confidence.
- If the sample suggests PII, flag it and remind me to handle carefully.
- If the data appears to be a subset (looks like a query result), note it.
How to use it
Paste this prompt, then paste your CSV header + 5-10 sample rows. Use the output to write your first analysis plan before you touch a chart or formula.
Why it works
Analysts often waste hours re-deriving what a column means from scratch. A structured read-out gets you oriented fast, and the “can’t answer” section stops you from chasing a question the data won’t support.
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