/data-analysis

name: data-analysis
description: Use this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot tables, SQL queries, or any form of structured data exploration. Supports multi-sheet Excel workbooks, aggregation, filtering, joins, and exporting results to CSV/JSON/Markdown.

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:

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

Based on the schema, construct SQL queries to answer the user's questions.

Run SQL Query

python /mnt/skills/public/data-analysis/scripts/analyze.py \
  --files /mnt/user-data/uploads/data.xlsx \
  --action query \
  --sql "SELECT category, COUNT(*) as count, AVG(amount) as avg_amount FROM Sheet1 GROUP BY category ORDER BY count DESC"

Generate Statistical Summary

python /mnt/skills/public/data-analysis/scripts/analyze.py \
  --files /mnt/user-data/uploads/data.xlsx \
  --action summary \
  --table Sheet1

This returns for each numeric column: count, mean, std, min, 25%, 50%, 75%, max, null_count. For string columns: count, unique, top value, frequency, null_count.

Export Results

python /mnt/skills/public/data-analysis/scripts/analyze.py \
  --files /mnt/user-data/uploads/data.xlsx \
  --action query \
  --sql "SELECT * FROM Sheet1 WHERE amount > 1000" \
  --output-file /mnt/user-data/outputs/filtered-results.csv

Supported output formats (auto-detected from extension):

• `.csv` — Comma-separated values
• `.json` — JSON array of records
• `.md` — Markdown table

Parameters

| Parameter | Required | Description | |-----------|----------|-------------| | `--files` | Yes | Space-separated paths to Excel/CSV files | | `--action` | Yes | One of: `inspect`, `query`, `summary` | | `--sql` | For `query` | SQL query to execute | | `--table` | For `summary` | Table/sheet name to summarize | | `--output-file` | No | Path to export results (CSV/JSON/MD) |

> [!NOTE] > Do NOT read the Python file, just call it with the parameters.

Table Naming Rules

• **Excel files**: Each sheet becomes a table named after the sheet (e.g., `Sheet1`, `Sales`, `Revenue`)
• **CSV files**: Table name is the filename without extension (e.g., `data.csv` → `data`)
• **Multiple files**: All tables from all files are available in the same query context, enabling cross-file joins
• **Special characters**: Sheet/file names with spaces or special characters are auto-sanitized (spaces → underscores). Use double quotes for names that start with numbers or contain special characters, e.g., `"2024_Sales"`

Analysis Patterns

Basic Exploration

-- Row count
SELECT COUNT(*) FROM Sheet1

-- Distinct values in a column
SELECT DISTINCT category FROM Sheet1

-- Value distribution
SELECT category, COUNT(*) as cnt FROM Sheet1 GROUP BY category ORDER BY cnt DESC

-- Date range
SELECT MIN(date_col), MAX(date_col) FROM Sheet1

Aggregation & Grouping

-- Revenue by category and month
SELECT category, DATE_TRUNC('month', order_date) as month,
       SUM(revenue) as total_revenue
FROM Sales
GROUP BY category, month
ORDER BY month, total_revenue DESC

-- Top 10 customers by spend
SELECT customer_name, SUM(amount) as total_spend
FROM Orders GROUP BY customer_name
ORDER BY total_spend DESC LIMIT 10

Cross-file Joins

-- Join sales with customer info from different files
SELECT s.order_id, s.amount, c.customer_name, c.region
FROM sales s
JOIN customers c ON s.customer_id = c.id
WHERE s.amount > 500

Window Functions

-- Running total and rank
SELECT order_date, amount,
       SUM(amount) OVER (ORDER BY order_date) as running_total,
       RANK() OVER (ORDER BY amount DESC) as amount_rank
FROM Sales

Pivot-style Analysis

-- Pivot: monthly revenue by category
SELECT category,
       SUM(CASE WHEN MONTH(date) = 1 THEN revenue END) as Jan,
       SUM(CASE WHEN MONTH(date) = 2 THEN revenue END) as Feb,
       SUM(CASE WHEN MONTH(date) = 3 THEN revenue END) as Mar
FROM Sales
GROUP BY category

Complete Example

User uploads `sales_2024.xlsx` (with sheets: `Orders`, `Products`, `Customers`) and asks: "Analyze my sales data — show top products by revenue and monthly trends."

Step 1: Inspect the file

python /mnt/skills/public/data-analysis/scripts/analyze.py \
  --files /mnt/user-data/uploads/sales_2024.xlsx \
  --action inspect

Step 2: Top products by revenue

python /mnt/skills/public/data-analysis/sc