MR
Mayur Rathi
@mayurrathi
⭐ 40.7k GitHub stars

Xlsx Official

Xlsx Official is an data AI skill with a core value of Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. It helps developers solve real-world problems in the data domain, boosting efficiency, automating repetitive tasks, and optimizing workflows.

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, ....

Last verified on: 2026-07-07

Quick Facts

Category data
Works With Claude
Source sickn33/antigravity-awesome-skills
Stars ⭐ 40.7k
Last Verified 2026-07-07
Risk Level Low
mkdir -p ./skills/xlsx-official && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/xlsx-official/SKILL.md -o ./skills/xlsx-official/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# Requirements for Outputs


All Excel files


Zero Formula Errors

- Every Excel model MUST be delivered with ZERO formula errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?)


Preserve Existing Templates (when updating templates)

- Study and EXACTLY match existing format, style, and conventions when modifying files

- Never impose standardized formatting on files with established patterns

- Existing template conventions ALWAYS override these guidelines


Financial models


Color Coding Standards

Unless otherwise stated by the user or existing template


#### Industry-Standard Color Conventions

- **Blue text (RGB: 0,0,255)**: Hardcoded inputs, and numbers users will change for scenarios

- **Black text (RGB: 0,0,0)**: ALL formulas and calculations

- **Green text (RGB: 0,128,0)**: Links pulling from other worksheets within same workbook

- **Red text (RGB: 255,0,0)**: External links to other files

- **Yellow background (RGB: 255,255,0)**: Key assumptions needing attention or cells that need to be updated


Number Formatting Standards


#### Required Format Rules

- **Years**: Format as text strings (e.g., "2024" not "2,024")

- **Currency**: Use $#,##0 format; ALWAYS specify units in headers ("Revenue ($mm)")

- **Zeros**: Use number formatting to make all zeros "-", including percentages (e.g., "$#,##0;($#,##0);-")

- **Percentages**: Default to 0.0% format (one decimal)

- **Multiples**: Format as 0.0x for valuation multiples (EV/EBITDA, P/E)

- **Negative numbers**: Use parentheses (123) not minus -123


Formula Construction Rules


#### Assumptions Placement

- Place ALL assumptions (growth rates, margins, multiples, etc.) in separate assumption cells

- Use cell references instead of hardcoded values in formulas

- Example: Use =B5*(1+$B$6) instead of =B5*1.05


#### Formula Error Prevention

- Verify all cell references are correct

- Check for off-by-one errors in ranges

- Ensure consistent formulas across all projection periods

- Test with edge cases (zero values, negative numbers)

- Verify no unintended circular references


#### Documentation Requirements for Hardcodes

- Comment or in cells beside (if end of table). Format: "Source: [System/Document], [Date], [Specific Reference], [URL if applicable]"

- Examples:

- "Source: Company 10-K, FY2024, Page 45, Revenue Note, [SEC EDGAR URL]"

- "Source: Company 10-Q, Q2 2025, Exhibit 99.1, [SEC EDGAR URL]"

- "Source: Bloomberg Terminal, 8/15/2025, AAPL US Equity"

- "Source: FactSet, 8/20/2025, Consensus Estimates Screen"


# XLSX creation, editing, and analysis


Overview


A user may ask you to create, edit, or analyze the contents of an .xlsx file. You have different tools and workflows available for different tasks.


Important Requirements


**LibreOffice Required for Formula Recalculation**: You can assume LibreOffice is installed for recalculating formula values using the `recalc.py` script. The script automatically configures LibreOffice on first run


Reading and analyzing data


Data analysis with pandas

For data analysis, visualization, and basic operations, use **pandas** which provides powerful data manipulation capabilities:


python
import pandas as pd

# Read Excel
df = pd.read_excel('file.xlsx')  # Default: first sheet
all_sheets = pd.read_excel('file.xlsx', sheet_name=None)  # All sheets as dict

# Analyze
df.head()      # Preview data
df.info()      # Column info
df.describe()  # Statistics

# Write Excel
df.to_excel('output.xlsx', index=False)

Excel File Workflows


CRITICAL: Use Formulas, Not Hardcoded Values


**Always use Excel formulas instead of calculating values in Python and hardcoding them.** This ensures the spreadsheet remains dynamic and updateable.


❌ WRONG - Hardcoding Calculated Values

python
# Bad: Calculating in Python and hardcoding result
total = df['Sales'].sum()
sheet['B10'] = total  # Hardcodes 5000

# Bad: Computing growth rate in Python
growth = (df.iloc[-1]['Revenue'] - df.iloc[0]['Revenue']) / df.iloc[0][

🎯 Best For

  • Claude users
  • Data professionals
  • Analytics teams
  • Researchers

💡 Use Cases

  • Data pipeline auditing
  • Query optimization

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Xlsx Official to Your Work

    Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.

  4. 4

    Review and Refine

    Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.

❓ Frequently Asked Questions

How do I install Xlsx Official?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/xlsx-official/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Ignoring data quality

AI analysis inherits all data quality issues — profile your data first.

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