Excel Analysis

Analyze Excel spreadsheets with pandas to extract structured insights and generate charts.

Updated Oct 24, 2025
One-click install
npx skills add https://github.com/georgiymarchenkov/ai_mrm --skill excel-analysis
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: Excel Analysis
Source: https://github.com/georgiymarchenkov/ai_mrm/tree/main/.claude/skills/excel-analysis
Command: npx skills add https://github.com/georgiymarchenkov/ai_mrm --skill excel-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, openpyxl, xlrd, xlsxwriter, matplotlib.

What problem does it solve?

This Skill eliminates the tedious, manual work involved in complex Excel tasks, from data cleaning and analysis to report generation. It helps users quickly process large datasets, create insightful visualizations, and automate repetitive spreadsheet operations, saving significant time and reducing errors.

Core Features & Use Cases

  • Automated Data Analysis: Perform complex calculations, group data, and generate descriptive statistics with ease.
  • Dynamic Report Generation: Create pivot tables and generate various charts (bar, pie, line) directly from your data.
  • Efficient Data Management: Read, write, and merge multiple Excel files, and clean messy data by handling duplicates, missing values, and formatting issues.
  • Use Case: Consolidate quarterly sales data from multiple Excel files, automatically clean the data, generate a pivot table summarizing sales by region and product, and create a bar chart for a presentation, all with a single command.

Quick Start

Use the Excel Analysis skill to read the 'sales_data.xlsx' file, calculate total sales by product, and save the results to 'sales_by_product.xlsx'.

Frequently Asked Questions about Excel Analysis

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

FAQPage Schema
How do I automate data cleaning and analysis in Excel spreadsheets?

Automate data cleaning and analysis by reading Excel files with pandas, handling duplicates and missing values, then computing aggregates and grouping data to extract structured insights from .xlsx/.xls files without manual processing.

Can I generate pivot tables and charts directly from Excel data?

Yes, generate pivot tables and multiple chart types—bar, pie, line—directly from Excel data using pandas and matplotlib, then export formatted results back to Excel for presentations and reports.

How do I consolidate and process data from multiple Excel files?

Read, merge, and consolidate multiple Excel files using pandas and openpyxl, clean the combined data, perform grouping and aggregations, then export the processed results to a new Excel file with formatting.

What's the best way to handle multi-sheet Excel workbooks programmatically?

Handle multi-sheet workbooks by using pandas to read specific or all sheets, perform filtering and metrics across sheets, then write results with xlsxwriter to maintain formatting and structure in the output file.

Do I need Python libraries to automate complex Excel tasks?

Yes, libraries like pandas, openpyxl, and xlrd handle reading and writing Excel files, xlsxwriter manages formatting, and matplotlib generates visualizations—together they automate complex spreadsheet workflows.

What are the limitations when working with large or complex Excel datasets?

Performance depends on dataset size and sheet count; pandas handles large tabular data efficiently, but very large files may require filtering or chunking strategies; openpyxl and xlrd support standard .xlsx/.xls formats with standard Excel feature limits.