spreadsheet-processor

SKILL.md

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:

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 2-3 sample references: Verify they pull correct values before building full model

 Column mapping: Confirm Excel columns match (e.g., column 64 = BL, not BK)

 Row offset: Remember Excel rows are 1-indexed (DataFrame row 5 = Excel row 6)

Common Pitfalls

 NaN handling: Check for null values with pd.notna()

 Far-right columns: FY data often in columns 50+

 Multiple matches: Search all occurrences, not just first

 Division by zero: Check denominators before using / in formulas (#DIV/0!)

 Wrong references: Verify all cell references point to intended cells (#REF!)

 Cross-sheet references: Use correct format (Sheet1!A1) for linking sheets

Formula Testing Strategy

 Start small: Test formulas on 2-3 cells before applying broadly

 Verify dependencies: Check all cells referenced in formulas exist

 Test edge cases: Include zero, negative, and very large values

Interpreting recalc.py Output

The script returns JSON with error details:

{
  "status": "success",           // or "errors_found"
  "total_errors": 0,              // Total error count
  "total_formulas": 42,           // Number of formulas in file
  "error_summary": {              // Only present if errors found
    "#REF!": {
      "count": 2,
      "locations": ["Sheet1!B5", "Sheet1!C10"]
    }
  }
}

Best Practices

Library Selection

pandas: Best for data analysis, bulk operations, and simple data export

openpyxl: Best for complex formatting, formulas, and Excel-specific features

Working with openpyxl

Cell indices are 1-based (row=1, column=1 refers to cell A1)

Use data_only=True to read calculated values: load_workbook('file.xlsx', data_only=True)

Warning: If opened with data_only=True and saved, formulas are replaced with values and permanently lost

For large files: Use read_only=True for reading or write_only=True for writing

Formulas are preserved but not evaluated - use recalc.py to update values

Working with pandas

Specify data types to avoid inference issues: pd.read_excel('file.xlsx', dtype={'id': str})

For large files, read specific columns: pd.read_excel('file.xlsx', usecols=['A', 'C', 'E'])

Handle dates properly: pd.read_excel('file.xlsx', parse_dates=['date_column'])

Code Style Guidelines

IMPORTANT: When generating Python code for Excel operations:

Write minimal, concise Python code without unnecessary comments

Avoid verbose variable names and redundant operations

Avoid unnecessary print statements

For Excel files themselves:

Add comments to cells with complex formulas or important assumptions

Document data sources for hardcoded values

Include notes for key calculations and model sections

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