# Spreadsheet Data Analysis

Analyzes Excel and CSV files with statistics, pivots, joins and exports.

- Repository: https://github.com/bytedance/deer-flow (84840 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add bytedance/deer-flow --skill data-analysis -g -a claude-code -y`
Codex: `npx skills add bytedance/deer-flow --skill data-analysis -g -a codex -y`
Cursor: `npx skills add bytedance/deer-flow --skill data-analysis -g -a cursor -y`
Gemini CLI: `npx skills add bytedance/deer-flow --skill data-analysis -g -a gemini-cli -y`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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:

```bash
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:

```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
- Sample data (first 5 rows)

### Step 3: Perform Analysis

The rest: https://github.com/bytedance/deer-flow/blob/HEAD/skills/public/data-analysis/SKILL.md