Prompts for Claude AI Data Analysis: 30 Free Templates
Copy-paste prompts for Claude AI to analyze spreadsheets, write SQL, spot trends, and turn raw numbers into executive summaries.
Claude AI data analysis promptsare structured instructions that turn Claude into a data analyst capable of processing spreadsheets, writing SQL queries, spotting anomalies, and generating executive summaries. Claude's 200K token context window can ingest entire datasets in one pass — up to 500 pages of CSV or tabular data — making it one of the most capable AI tools for ad hoc data analysis without writing code (Anthropic, 2025). The 30 templates below are free to copy and use.
Analyze this {{format}} data and summarize the key findings in 5 bullet points. Include totals, averages, and any outliers.
Data:
{{data}}Identify the top 3 trends in this time-series data. For each trend, state the direction, magnitude, and likely cause.
Data:
{{data}}Write a SQL query for {{database}} that {{goal}}. Include comments explaining each clause. Tables and columns:
{{schema}}Suggest a step-by-step data cleaning plan for this dataset. Flag missing values, duplicates, inconsistent formats, and outliers. Propose fixes for each.
Sample rows:
{{data}}Create a pivot table summary of this data grouped by {{dimension}}. Show count, sum, and average for {{metric}}. Output as a markdown table.
Data:
{{data}}Compare Dataset A and Dataset B below. List differences in structure, coverage, and values. Highlight mismatches and missing records.
Dataset A:
{{dataset_a}}
Dataset B:
{{dataset_b}}Recommend 3 chart types for visualizing this data. For each, explain why it fits, what to put on each axis, and one insight it would reveal.
Data description:
{{description}}Turn this raw data into a 3-paragraph executive summary for {{audience}}. Lead with the headline number, explain context, and end with a recommendation.
Data:
{{data}}Analyze the relationship between {{independent_var}} and {{dependent_var}} in this data. Describe correlation direction, strength, and any confounding factors.
Data:
{{data}}Scan this dataset for anomalies. Flag any values that are statistical outliers (>2 SD from mean) or logically inconsistent. Explain why each is suspicious.
Data:
{{data}}Tips for data prompts
- Always include column headers so Claude understands the schema.
- Specify your SQL dialect (PostgreSQL, MySQL, BigQuery) for accurate syntax.
- Ask for markdown tables or JSON to get structured, copy-paste-ready output.
- For large datasets, paste a sample and describe the full shape (rows, columns, date range).
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