Requirements for Outputs

All Excel files

Professional Font

Use a consistent, professional font (e.g., Arial, Times New Roman) for all deliverables unless otherwise instructed by the user

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 scripts/recalc.py script. The script automatically configures LibreOffice on first run, including in sandboxed environments where Unix sockets are restricted (handled by scripts/office/soffice.py ) 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 scripts/recalc.py script python scripts/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

scripts/recalc.py

script to recalculate formulas:

python scripts/recalc.py

<

excel_file

>

[

timeout_seconds

]

Example:

python scripts/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 scripts/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 scripts/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