Xlsx

What This Skill Does

• The XLSX skill provides guidelines and requirements for creating and modifying Excel files, particularly financial models.
• It emphasizes error-free formulas, preservation of existing templates, and adherence to color-coding and number formatting standards.
• This skill is useful for ensuring consistency and accuracy in Excel-based financial analysis.

When to Use

• Create a financial model with zero formula errors.
• Update an existing Excel template while matching style.
• Apply industry-standard color conventions.
• Format numbers correctly for financial reporting.
• Construct formulas with clear assumptions.
• Prevent formula errors through careful verification.

Key Features

• Requires zero formula errors in Excel models.
• Preserves existing template formatting.
• Uses blue text for hardcoded inputs.
• Uses black text for formulas and calculations.
• Formats years as text strings.
• Formats currency with specified units.

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 =B51.05

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:

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

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]['Revenue'] sheet['C5'] = growth # Hardcodes 0.15

Bad: Python calculation for average avg = sum(values) / len(values) sheet['D20'] = avg # Hardcodes 42.5

✅ CORRECT - Using Excel Formulas

Good: Let Excel calculate the sum

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

Good: Growth rate as Excel formula sheet['C5'] = '=(C4-C2)/C2'

Good: Average using Excel function sheet['D20'] = '=AVERAGE(D2:D19)'

This applies to ALL calculations - totals, percentages, ratios, differences, etc. The spreadsheet should be able to recalculate when source data changes.

Common Workflow

Choose tool: pandas for data, openpyxl for formulas/formatting Create/Load: Create new workbook or load existing file Modify: Add/edit data, formulas, and formatting Save: Write to file Recalculate formulas (MANDATORY IF USING FORMULAS): Use the recalc.py script python recalc.py output.xlsx Verify and fix any errors:

• The script returns JSON with error details
• If status is errors_found, check error_summary for specific error types and locations
• Fix the identified errors and recalculate again
• Common errors to fix:
  • #REF!: Invalid cell references
  • #DIV/0!: Division by zero
  • #VALUE!: Wrong data type in formula
  • #NAME?: Unrecognized formula name

Creating new Excel files

Using openpyxl for formulas and formatting

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

wb = Workbook() sheet = wb.active

Add data sheet['A1'] = 'Hello' sheet['B1'] = 'World' sheet.append(['Row', 'of', 'data'])

Add formula sheet['B2'] = '=SUM(A1:A10)'

Formatting sheet['A1'].font = Font(bold=True, color='FF0000') sheet['A1'].fill = PatternFill('solid', start_color='FFFF00') sheet['A1'].alignment = Alignment(horizontal='center')

Column width sheet.column_dimensions['A'].width = 20

wb.save('output.xlsx')

Editing existing Excel files

Using openpyxl to preserve formulas and formatting

from openpyxl import load_workbook

Load existing file wb = load_workbook('existing.xlsx') sheet = wb.active # or wb['SheetName'] for specific sheet

Working with multiple sheets for sheet_name in wb.sheetnames: sheet = wb[sheet_name] print(f"Sheet: {sheet_name}")

Modify cells sheet['A1'] = 'New Value' sheet.insert_rows(2) # Insert row at position 2 sheet.delete_cols(3) # Delete column 3

Add new sheet new_sheet = wb.create_sheet('NewSheet') new_sheet['A1'] = 'Data'

wb.save('modified.xlsx')

Recalculating formulas

Excel files created or modified by openpyxl contain formulas as strings but not calculated values. Use the provided recalc.py script to recalculate formulas:

python recalc.py <excel_file> [timeout_seconds]

Example: python recalc.py output.xlsx 30

The script: Automatically sets up LibreOffice macro on first run Recalculates all formulas in all sheets Scans ALL cells for Excel errors (#REF!, #DIV/0!, etc.) Returns JSON with detailed error locations and counts Works on both Linux and macOS

Formula Verification Checklist

Quick checks to ensure formulas work correctly:

Essential Verification

[ ] Test 2-