XLSX Skill

Core Purpose

Create, edit, and analyze spreadsheets (.xlsx, .xlsm, .csv, .tsv) with comprehensive
support for formulas, formatting, data analysis, and visualization.

Requirements for All Excel Files

Zero Formula Errors

Every Excel model MUST be delivered with ZERO formula errors:

• #REF! - Invalid cell references
• #DIV/0! - Division by zero
• #VALUE! - Wrong data type in formula
• #N/A - Value not available
• #NAME? - Unrecognized formula name

Preserve Existing Templates

When modifying files with established patterns:

• Study and EXACTLY match existing format, style, and conventions
• Never impose standardized formatting on files with established patterns
• Existing template conventions ALWAYS override these guidelines

Financial Model Standards

Color Coding Conventions

• Blue text (RGB: 0,0,255): Hardcoded inputs, numbers users will change
• Black text (RGB: 0,0,0): ALL formulas and calculations
• Green text (RGB: 0,128,0): Links from other worksheets in same workbook
• Red text (RGB: 255,0,0): External links to other files
• Yellow background (RGB: 255,255,0): Key assumptions needing attention

Number Formatting Rules

• Years: Format as text strings ("2024" not "2,024")
• Currency: Use $#,##0 format; specify units in headers ("Revenue ($mm)")
• Zeros: Format to display as "-"
• Percentages: Default to 0.0% format (one decimal)
• Multiples: Format as 0.0x for valuation multiples
• Negative numbers: Use parentheses (123) not minus -123

CRITICAL: Use Formulas, Not Hardcoded Values

Always use Excel formulas instead of calculating values in Python.
This keeps spreadsheets dynamic and updateable.

Wrong - Hardcoding:

total = df['Sales'].sum()
sheet['B10'] = total  # Hardcodes value

Correct - Using Formulas:

sheet['B10'] = '=SUM(B2:B9)'  # Excel calculates

Tool Selection

• pandas: Best for data analysis, bulk operations, simple data export
• openpyxl: Best for complex formatting, formulas, Excel-specific features

Common Workflow

1. Choose tool: pandas for data, openpyxl for formulas/formatting
2. Create/Load: Create new workbook or load existing file
3. Modify: Add/edit data, formulas, and formatting
4. Save: Write to file
5. Recalculate: Run python recalc.py output.xlsx (MANDATORY for formulas)
6. Verify: Check recalc.py output for errors, fix any found

Quick Code Examples

Reading with pandas:

import pandas as pd
df = pd.read_excel('file.xlsx')
all_sheets = pd.read_excel('file.xlsx', sheet_name=None)

Creating with openpyxl:

from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment

wb = Workbook()
sheet = wb.active
sheet['A1'] = 'Header'
sheet['B2'] = '=SUM(A1:A10)'  # Use formulas!
sheet['A1'].font = Font(bold=True, color='FF0000')
wb.save('output.xlsx')

Editing existing files:

from openpyxl import load_workbook
wb = load_workbook('existing.xlsx')
sheet = wb.active
sheet['A1'] = 'New Value'
wb.save('modified.xlsx')

Formula Verification Checklist

• Test 2-3 sample references before building full model
• Verify column mapping (column 64 = BL, not BK)
• Remember Excel rows are 1-indexed
• Check for NaN values with pd.notna()
• Handle division by zero in formulas
• Test formulas on 2-3 cells before applying broadly

Best Practices

• Cell indices are 1-based in openpyxl
• Use data_only=True to read calculated values (WARNING: saves lose formulas)
• For large files: Use read_only=True or write_only=True
• Write minimal, concise Python code
• Add comments to cells with complex formulas