Does this skill support existing Excel formulas?

Yes, the skill is designed to preserve existing formulas and references using openpyxl. It can also create new formulas to ensure derived values are calculated correctly within Excel.

Is this skill suitable for investment banking or financial modeling?

Yes, it includes specific guidelines for IB-style layouts, such as hiding gridlines, specific cell color conventions, and standardized unit formatting in headers.

Can I use this to generate charts and visualizations?

Absolutely. The skill can create native Excel charts using openpyxl.chart or generate external visualizations using matplotlib depending on your requirements.

How does the visual review process work?

If LibreOffice and Poppler are installed, the skill can render spreadsheet sheets into PNG images, allowing you to visually inspect the layout and formatting directly.

Spreadsheet & Data Analysis

Name: Spreadsheet & Data Analysis
Author: x-cmd

byx-cmd

•

Data Science & ML

Automates the creation, editing, and analysis of professional spreadsheets with support for complex formulas, data visualization, and industry-standard formatting.

The Spreadsheet skill equips Claude with the tools necessary to handle sophisticated data tasks within .xlsx, .csv, and .tsv files. By leveraging Python libraries like pandas and openpyxl, it allows for the precise manipulation of data while preserving critical formulas and styling. The skill is particularly robust for financial modeling, offering built-in conventions for investment banking layouts and color-coding. It also supports visual verification by rendering spreadsheets into images or PDFs, ensuring that layout and formatting requirements are met before final delivery.

Key Features

01Automated data analysis, filtering, and aggregation using pandas

02Preservation and creation of complex formulas and cell references

03Visual verification through PDF and PNG rendering

049 GitHub stars

05High-fidelity Excel formatting and professional color-coding

06Specialized layouts for financial modeling and investment banking

Use Cases

01Analyzing large CSV datasets to extract key business metrics and trends

02Generating automated monthly financial reports with consistent styling and logic

03Building dynamic valuation models including DCF and 3-statement structures

Key Features

01Automated data analysis, filtering, and aggregation using pandas

02Preservation and creation of complex formulas and cell references

03Visual verification through PDF and PNG rendering

049 GitHub stars

05High-fidelity Excel formatting and professional color-coding

06Specialized layouts for financial modeling and investment banking

Use Cases

01Analyzing large CSV datasets to extract key business metrics and trends

02Generating automated monthly financial reports with consistent styling and logic

03Building dynamic valuation models including DCF and 3-statement structures