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.

Environment setup

Prepare the Python environment before running analyses. Install the required packages explicitly using the same interpreter that will run the script:

python -m pip install duckdb openpyxl

If either package is missing, the script exits with a setup instruction; it does not invoke an installer or modify the environment. DuckDB's existing spatial-extension setup for loading files is unchanged.

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:

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
• Sample data (first 5 rows)

Step 3: Perform Analysis