XLSX

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

Visual Table Design (MUST for new tables unless user/template says otherwise)

• Apply zebra striping in data regions (not headers or totals), using low-contrast alternating fills (for example #FFFFFF and #F7F9FC)
• Apply thin, light borders for structure (for example #D9DEE7), favoring outer border + row separators over heavy full-grid borders
• For subtotal/total emphasis, use hierarchy cues (bold text and/or top border emphasis) instead of thick borders around every cell

KPI Visual Hierarchy (MUST when KPI-like metrics exist)

• Highlight an appropriate number of key metrics based on content complexity, prioritizing summary rows, current-period critical values, and exceptions
• Keep highlight treatment consistent (prefer bold + light fill) and avoid overusing saturated colors
• Keep color semantics consistent across the workbook (for example green=positive, red=risk)
• Add short labels near highlighted metrics when helpful (for example "Core KPI", "Exception")

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 scripts/recalc.py script. The script automatically configures LibreOffice on first run, including in sandboxed environments where IPC restrictions may apply (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.

⚠️ REQUIRED: Pre-Build Planning

Before writing any data/formulas/code, you MUST complete these planning steps:

Step 1: Problem Review

• Read all relevant files (source Excel/CSV/TSV, template workbook, and referenced documents)
• Combine file findings with the user query and restate the task intent in concrete terms

Step 2: Output Detailed Workbook Plan

Present a structured plan covering:

1. Sheet Plan

   • Total number of sheets
   • Each sheet name
   • Purpose of each sheet

2. Schema Plan (per sheet)

   • Row fields (if row-oriented)
   • Column fields (if column-oriented)
   • Value source/calculation for each field (raw source / lookup / formula / aggregation)

3. Style Plan (per sheet)

   • Header style (fill, font color, emphasis)
   • Zebra striping design
   • KPI visual hierarchy design

Required planning output format:

## Problem Review
- Files read: ...
- Task understanding: ...

## Detailed Plan
### Sheet Plan
1. Sheet: <name> — Purpose: <purpose>

### Schema Plan
1. Sheet: <name>
- Row fields: ...
- Column fields: ...
- Value source/calculation: ...

### Style Plan
1. Sheet: <name>
- Header: ...
- Zebra striping: ...
- KPI hierarchy: ...

If inputs are incomplete, still output this plan with explicit assumptions first.

Only proceed to implementation after:

• Problem Review is complete
• Detailed Workbook Plan is output

Common Workflow

1. Output plan first (MANDATORY): Output Problem Review + Detailed Plan using the required format above
2. Choose tool: pandas for data, openpyxl for formulas/formatting
3. Create/Load: Create new workbook or load existing file
4. Modify: Add/edit data, formulas, and formatting
5. Save: Write to file
6. Recalculate formulas (MANDATORY IF USING FORMULAS): Use the scripts/recalc.py script

   python scripts/recalc.py output.xlsx
   

7. 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 Linux, macOS, and Windows

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'])

Reusable Styling Helpers (openpyxl)

Use helper functions so visual rules are applied consistently instead of ad-hoc cell formatting:

from openpyxl.styles import PatternFill, Border, Side, Font

ZEBRA_FILL_1 = PatternFill(fill_type="solid", fgColor="FFFFFF")
ZEBRA_FILL_2 = PatternFill(fill_type="solid", fgColor="F7F9FC")
KPI_FILL = PatternFill(fill_type="solid", fgColor="EAF2FF")
THIN_BORDER = Border(
    left=Side(style="thin", color="D9DEE7"),
    right=Side(style="thin", color="D9DEE7"),
    top=Side(style="thin", color="D9DEE7"),
    bottom=Side(style="thin", color="D9DEE7"),
)
TOP_EMPHASIS_BORDER = Border(
    left=Side(style="thin", color="D9DEE7"),
    right=Side(style="thin", color="D9DEE7"),
    top=Side(style="medium", color="AAB4C5"),
    bottom=Side(style="thin", color="D9DEE7"),
)

def apply_zebra_style(ws, min_row, max_row, min_col, max_col):
    for r in range(min_row, max_row + 1):
        fill = ZEBRA_FILL_1 if (r - min_row) % 2 == 0 else ZEBRA_FILL_2
        for c in range(min_col, max_col + 1):
            ws.cell(r, c).fill = fill

def apply_light_borders(ws, min_row, max_row, min_col, max_col):
    for r in range(min_row, max_row + 1):
        for c in range(min_col, max_col + 1):
            ws.cell(r, c).border = THIN_BORDER

def highlight_kpis(ws, cells, label_col=None, label_text=None):
    # cells example: ["F5", "F12", "F20"]
    for ref in cells:
        cell = ws[ref]
        cell.fill = KPI_FILL
        cell.font = Font(bold=True, color="1F2937")
    if label_col and label_text and cells:
        ws[f"{label_col}{ws[cells[0]].row}"] = label_text

# Example usage:
# apply_zebra_style(ws, min_row=2, max_row=30, min_col=1, max_col=8)
# apply_light_borders(ws, min_row=1, max_row=30, min_col=1, max_col=8)
# highlight_kpis(ws, cells=["F5", "F12"], label_col="G", label_text="Core KPI")
# ws["A31"].border = TOP_EMPHASIS_BORDER  # subtotal/total row emphasis

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