SKILL.md

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

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/scripts/analyze.py \

  --files /mnt/user-data/uploads/sales_2024.xlsx \

  --action query \

  --sql "SELECT p.product_name, SUM(o.quantity * o.unit_price) as total_revenue, SUM(o.quantity) as total_units FROM Orders o JOIN Products p ON o.product_id = p.id GROUP BY p.product_name ORDER BY total_revenue DESC LIMIT 10"

Step 3: Monthly revenue trends

python /mnt/skills/public/data-analysis/scripts/analyze.py \

  --files /mnt/user-data/uploads/sales_2024.xlsx \

  --action query \

  --sql "SELECT DATE_TRUNC('month', order_date) as month, SUM(quantity * unit_price) as revenue FROM Orders GROUP BY month ORDER BY month" \

  --output-file /mnt/user-data/outputs/monthly-trends.csv

Step 4: Statistical summary

python /mnt/skills/public/data-analysis/scripts/analyze.py \

  --files /mnt/user-data/uploads/sales_2024.xlsx \

  --action summary \

  --table Orders

Present results to the user with clear explanations of findings, trends, and actionable insights.

Multi-file Example

User uploads orders.csv and customers.xlsx and asks: "Which region has the highest average order value?"

python /mnt/skills/public/data-analysis/scripts/analyze.py \

  --files /mnt/user-data/uploads/orders.csv /mnt/user-data/uploads/customers.xlsx \

  --action query \

  --sql "SELECT c.region, AVG(o.amount) as avg_order_value, COUNT(*) as order_count FROM orders o JOIN Customers c ON o.customer_id = c.id GROUP BY c.region ORDER BY avg_order_value DESC"

Output Handling

After analysis:

Present query results directly in conversation as formatted tables

For large results, export to file and share via present_files tool

Always explain findings in plain language with key takeaways

Suggest follow-up analyses when patterns are interesting

Offer to export results if the user wants to keep them

Caching

The script automatically caches loaded data to avoid re-parsing files on every call:

On first load, files are parsed and stored in a persistent DuckDB database under /mnt/user-data/workspace/.data-analysis-cache/

The cache key is a SHA256 hash of all input file contents — if files change, a new cache is created

Subsequent calls with the same files will use the cached database directly (near-instant startup)

Cache is transparent — no extra parameters needed

This is especially useful when running multiple queries against the same data files (inspect → query → summary).

Notes

DuckDB supports full SQL including window functions, CTEs, subqueries, and advanced aggregations

Excel date columns are automatically parsed; use DuckDB date functions (DATE_TRUNC, EXTRACT, etc.)

For very large files (100MB+), DuckDB handles them efficiently without loading everything into memory

Column names with spaces are accessible using double quotes: "Column Name"

data-analysis