data-analysislisted

# 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 When a user uploads data files and requests analysis, identify: - **File location**: Path(s) to uploaded Excel/CSV files under `/mnt/user-data/uploads/` - **Analysis goal**: What insights the user wants (summary, filtering, aggregation, comparison, etc.) - **Output format**: How results should be presented (table, CSV export, JSON, etc.) - You don't need to check the folder under `/mnt/user-data` ### Step 2: Inspect File Structure First, inspect the uploaded file to understand its schema: ```bash python /mnt/skills/public/data-analysis/scripts/analyze.py \ --files /mnt/user-data/uploads/data.xlsx \ --action inspect ``` This returns: - Sheet names (for Excel) or filename (for CSV) - Column names, data types, and non-null counts - Row count per sheet/file - Sam