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

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

Use officecli for formula values, and for looking at the sheet. It is a single self-contained binary that evaluates Excel formulas itself (350+ functions) and renders the workbook - no LibreOffice, no Excel, no Office install. Verify it is present before relying on it:

officecli --version

If missing, install it (open a new shell if the binary still is not found):

# macOS / Linux
curl -fsSL https://d.officecli.ai/install.sh | bash
# Windows (PowerShell)
irm https://d.officecli.ai/install.ps1 | iex

Runs on macOS, Linux and Windows (x64 and arm64 on each, plus musl/Alpine Linux) - the installer picks the right binary. Write output next to the workbook or into a directory you created, not /tmp: the examples below use /tmp for brevity, but it does not exist on Windows - use $env:TEMP there, or a relative out\ folder.

Supported formats are exactly .docx, .xlsx, .pptx. Legacy .xls is NOT supported - ask for an .xlsx. .csv/.tsv are handled with pandas, or imported with officecli import.

Do NOT assume LibreOffice exists. It is not installed on most machines this skill runs on, and nothing here installs it.

Reading and analyzing data

Reading with officecli

Fastest way to see what is actually in a workbook, including COMPUTED formula values:

# Structure: sheets, dimensions, formula counts
officecli view file.xlsx outline

# Cell values as text (A1=value, tab-separated; empty cells omitted)
officecli view file.xlsx text
officecli view file.xlsx text --cols A,B,C --max-lines 50
officecli view file.xlsx text --range "Sheet1!A1:C10"

# One cell, with formula AND cached/computed value
officecli get file.xlsx /Sheet1/B10 --json

# Find cells: CSS-like selectors, incl. row-by-column-name
officecli query file.xlsx 'cell:has(formula)'
officecli query file.xlsx 'Sheet1!row[Salary>5000]'

# Formula errors and other problems, mechanically
officecli view file.xlsx issues --json

# SEE the sheet (PNG needs a headless browser: Playwright/Chrome/Edge/Firefox)
officecli view file.xlsx screenshot -o /tmp/sheet.png
officecli view file.xlsx screenshot --range "Sheet1!A1:H40" -o /tmp/region.png
officecli view file.xlsx html -o /tmp/sheet.html
officecli watch file.xlsx        # live preview at http://localhost:26315

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

Two routes. Prefer the first when formulas matter.

Route A - write formulas with officecli (evaluated as you write)

officecli set evaluates a formula at write time and caches the result, so there is no separate recalculation pass:

officecli create output.xlsx
officecli set output.xlsx /Sheet1/A1 --prop value="Revenue" --prop bold=true
officecli set output.xlsx /Sheet1/B10 --prop formula="=SUM(B2:B9)"

# read the computed value back
officecli get output.xlsx /Sheet1/B10 --json

Then verify (see Verifying formula values below).

Route B - build with openpyxl/pandas, then fix up the values

openpyxl writes formulas as STRINGS with no cached values, so a reader that does not recalculate sees blanks.

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. Populate formula values (MANDATORY IF USING FORMULAS): re-write each formula cell through officecli set, which evaluates it:

   officecli set output.xlsx /Sheet1/B10 --prop formula="=SUM(B2:B9)"
   

   For many cells, do it in one pass with batch:

   officecli batch output.xlsx --commands '[
     {"op":"set","path":"/Sheet1/B10","props":{"formula":"=SUM(B2:B9)"}},
     {"op":"set","path":"/Sheet1/C10","props":{"formula":"=AVERAGE(C2:C9)"}}
   ]' --json
   

6. Verify and fix any errors: see Verifying formula values below. Common errors to fix:
   • #REF!: Invalid cell references
   • #DIV/0!: Division by zero
   • #VALUE!: Wrong data type in formula
   • #NAME?: Unrecognized formula name

Flush before a non-officecli program reads the file. officecli keeps a resident process, so openpyxl/pandas/an upload may otherwise read a stale file:

officecli save output.xlsx     # flush, keep resident warm
officecli close output.xlsx    # flush + release

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')

Verifying formula values

Excel files created or modified by openpyxl contain formulas as strings but no calculated values. officecli evaluates a formula when you WRITE it (set ... --prop formula=...), and exposes three separate readback keys so you can tell a real value from a stale one:

# one cell
officecli get output.xlsx /Sheet1/B10 --json

# just the cached value
officecli get output.xlsx /Sheet1/B10 --json | jq '.data.results[0].format.cachedValue'

# every formula cell
officecli query output.xlsx 'cell:has(formula)' --json

# formulas shown inline with their resolved values
officecli view output.xlsx annotated

# error scan across the workbook (#REF!, #DIV/0!, #VALUE!, #NAME?, ...)
officecli view output.xlsx issues --json

cachedValue != computedValue means the file on disk is lying. That is a stale cache, reported by view issues as formula_cache_stale. Fix it by re-writing the formula (see below), not by ignoring it.

The staleness trap - read this before shipping a multi-formula model

A formula is evaluated at the moment it is written, using whatever its precedents had cached AT THAT MOMENT. So a downstream formula written before its upstream was computed caches a wrong value - often 0 - and that wrong value survives into every reader that does not recalculate.

After any multi-formula build - especially SUMPRODUCT, SUMIFS with dynamic criteria, INDEX/MATCH, or cross-sheet chains - re-touch every downstream cell by running the same set again so it recomputes from the now-correct upstream:

# second pass: re-write the dependent formulas in dependency order
officecli set model.xlsx /Summary/B2 --prop formula="=SUMPRODUCT(Data!B2:B50,Data!C2:C50)"

Do the re-touch pass WITHOUT a resident open (officecli close model.xlsx first) - re-touching cross-sheet chains through a resident is unreliable.

Two more limits worth knowing:

• A cell the evaluator could not compute carries the sentinel #OCLI_NOTEVAL!. Remedy: close residents, then set the cell again.
• Dynamic arrays: only the anchor (top-left) cell is evaluated; the spilled cells are produced by Excel when it opens the file.

If you have LibreOffice and want a full-workbook recalculation

scripts/recalc.py still exists and does a true calculateAll() across every sheet, then scans all cells for Excel errors. It requires LibreOffice (soffice), which is usually NOT installed - check first and do not make it part of the default path:

# macOS / Linux
command -v soffice && python scripts/recalc.py output.xlsx 30

# Windows PowerShell
if (Get-Command soffice -ErrorAction SilentlyContinue) { python scripts/recalc.py output.xlsx 30 }

Note the helper it uses (scripts/office/soffice.py) carries a Linux-only sandbox shim (LD_PRELOAD + gcc), so on macOS/Windows this path works only if soffice is already on PATH.

Prefer the officecli route above; reach for this only when you specifically need a whole-workbook recalculation by a real spreadsheet engine.

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

Checking for formula errors

Primary check - no LibreOffice needed:

officecli view output.xlsx issues --json

It reports #REF! / #VALUE! / #NAME? / #DIV/0! and the formula_cache_stale subtype (a cachedValue that disagrees with the evaluator). Fix what it lists, re-write the affected formulas, re-check.

Interpreting scripts/recalc.py Output (LibreOffice route only)

When you deliberately used the scripts/recalc.py fallback, it 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 - re-write each formula cell through officecli set ... --prop formula="..." to populate its cached value (see Verifying formula 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