data-analysis
SQL-powered analysis of Excel and CSV files with schema inspection, aggregation, and multi-format export.
- Execute arbitrary SQL queries against uploaded data, including joins across multiple files, window functions, and pivot-style aggregations
- Inspect file structure (sheets, columns, data types, row counts) and generate statistical summaries (mean, median, stddev, percentiles, null counts) for numeric and string columns
- Export query results to CSV, JSON, or Markdown; results are cached automatically to speed up repeated queries on the same files
- Supports multi-sheet Excel workbooks and cross-file joins, with automatic table naming and special character handling
Data Analysis Skill
Overview
This skill analyzes user-uploaded Excel/CSV files using DuckDB — an in-process analytical SQL engine. It supports schema inspection, SQL-based querying, statistical summaries, and result export, all through a single Python script.
Core Capabilities
- Inspect Excel/CSV file structure (sheets, columns, types, row counts)
- Execute arbitrary SQL queries against uploaded data
- Generate statistical summaries (mean, median, stddev, percentiles, nulls)
- Support multi-sheet Excel workbooks (each sheet becomes a table)
- Export query results to CSV, JSON, or Markdown
- Handle large files efficiently with DuckDB's columnar engine
Workflow
Step 1: Understand Requirements
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