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data-analysis

Analyze Excel and CSV files with SQL queries and statistical summaries.

6|2|Updated Apr 10, 2026
Search Tags:#SQL#data analysis#Excel#CSV#statistical summary
One-click install
npx skills add https://github.com/hackeshackes/deerfllow-BY --skill data-analysis-hackeshackes
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/hackeshackes/deerfllow-BY/tree/main/skills/public/data-analysis
Command: npx skills add https://github.com/hackeshackes/deerfllow-BY --skill data-analysis-hackeshackes

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, duckdb, openpyxl, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a streamlined solution for analyzing Excel and CSV files, allowing users to perform SQL queries, generate statistical summaries, and export results in various formats.

Core Features & Use Cases

  • Data Analysis: Analyze user-uploaded Excel/CSV files.
  • SQL Queries: Execute SQL queries to extract data, perform aggregations, and filter records.
  • Statistical Summaries: Generate statistical summaries including mean, median, standard deviation, and percentiles.
  • Export: Export results to CSV, JSON, or Markdown formats.
  • Use Case: A user uploads a sales data file and wants to calculate monthly revenue trends and top-selling products.

Quick Start

Use the data-analysis skill to analyze the 'sales_data.xlsx' file and generate a summary of monthly revenue trends.

Frequently Asked Questions about data-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze Excel and CSV files using SQL queries?▼

To analyze Excel and CSV files using SQL queries, this solution leverages DuckDB to parse structured data, enabling you to filter records, perform aggregations, and extract insights directly from your spreadsheets.

Can I generate statistical summaries like mean and standard deviation from CSV data?▼

Yes, you can generate statistical summaries from CSV data including mean, median, standard deviation, and percentiles, providing a comprehensive overview of your dataset's distribution and key metrics.

Do I need Python and DuckDB to run SQL queries on Excel files?▼

Yes, you need Python, DuckDB, and openpyxl to run SQL queries on Excel files, as these dependencies handle the underlying file parsing and SQL execution required for the data analysis workflow.

What's the best way to export data analysis results from Excel files?▼

The best way to export data analysis results from Excel files is to output the queried data into CSV, JSON, or Markdown formats, allowing for seamless integration into structured data reporting workflows.

How does DuckDB handle administrative data exploration for reporting?▼

DuckDB handles administrative data exploration by executing in-memory SQL queries directly on Excel and CSV files, allowing users to dynamically aggregate records and compute statistical summaries for reporting.

Are there limitations when analyzing very large Excel files with DuckDB?▼

While DuckDB efficiently handles structured data exploration, limitations may arise with extremely large Excel files due to openpyxl parsing constraints, making CSV uploads better suited for high-volume administrative data analysis.

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