excel-plus

Excel Plus

A skill that covers spreadsheet work in general and how to handle locks, IRM, and format mismatches during updates.

Excel Plus Extensions

When to Use (Troubleshooting)

PermissionError, BadZipFile, file is open by another application appears
A save conflict occurs in a workbook under OneDrive/SharePoint
An auto-update script fails on an IRM/protected file
Keywords: excel update plan, 1on1 excel, IRM, BadZipFile, openpyxl, lock

Procedure (Troubleshooting)

Check the target update script and required arguments
Validate arguments such as date, target path, and output destination before workbook open / COM launch
Even if the workbook is open, first --dry-run run
Branch by error type (Branches below)
Run the actual update after a successful dry-run
Check the update result (created sheet name, number of changes, save success)
Delete temporary files/temporary scripts and exit

Branches

PermissionError: First try snapshot export or open-workbook COM update. Also read unsaved changes in an open workbook via snapshot. Closing is a last resort
BadZipFile: Check the format by the leading bytes (PK=xlsx, D0 CF= possibly legacy format/OLE/IRM). If necessary, .xlsx save it again with . If the workbook is open, create a snapshot via COM and continue
IRM/protection: Do not open it arbitrarily in hidden Excel. Have the user open it interactively in Excel Desktop, and attach only to a workbook whose protection/IRM dialog has already been resolved. If hidden open is allowed, require an explicit flag, a short timeout, fail-fast, and cleanup
Hang: Avoid long one-line commands; split into short commands or a temporary script. For the COM helper, Workbooks.Open / SaveAs / Save set a timeout on the assumption that it will not return, and do not rely only on finally
Residual hidden Excel: EXCEL.EXE In a dry-run, enumerate processes whose window title/handle is empty, and only if the user confirms it is okay to close Excel, narrow down the PID and terminate it.taskkill /IM excel.exe /F Avoid killing everything like

COM Guardrails (Troubleshooting)

For workbooks suspected of being protected, IRM, or format-mismatched,PK fail-fast the non-interactive library processing at the point when it is not
RequireOpenWorkbook Provide a guard equivalent to , and do not create a new hidden Excel if the target workbook is not already open
--dry-run / --plan-output Prefer a machine-readable preview such as , and check changed rows, unresolved rows, and addition candidates before the actual update
For URL columns, handle the display string and the actual hyperlink metadata separately.RMOT... SCOP... ROSS... Even if the ID of is visible, it may not be clickable, so reattach inferable link destinations in a repair pass, and do not judge completion based only on the displayed value
Delete temporary snapshots / temp copies on all paths: success, failure, and timeout
In regression tests, do not use a real workbook or Excel COM update; use a temporary OpenXML workbook to verify dry-run / actual update / argument validation / non-OpenXML fail-fast / no hidden Excel creation
If the COM connection AttributeError: Excel.Application.Workbooks や GetActiveObject is continuously rejected due to failure, before retrying on the Python/PS side, Excel-side stateSuspect: cell being edited / modal dialog displayed ("It has been changed by another process. Do you want to reload?", "Do you want to save?", etc.) / Excel not responding. Order of handling: press Esc to cancel cell editing → close the dialog → retry. If it still does not recover, exit Excel once → restart → retry
PowerShell 7 (pwsh) then [System.Runtime.InteropServices.Marshal]::GetActiveObject has been deleted. For PS scripts that connect to Excel COM, explicitly specify powershell.exe(PowerShell 5.1)
PowerShell 5.1 の .ps1 reading files as UTF-8 breaks(Japanese strings get garbled like EDE COMPLETE (縲・026-05-31) and cause syntax errors). For payloads containing Japanese, on the Python side base64.b64encode(json.dumps(payload, ensure_ascii=False).encode("utf-8")) and pass them via environment variables, and on the PS side decode with [Text.Encoding]::UTF8.GetString([Convert]::FromBase64String($env:VAR)) | ConvertFrom-Json decode with

Quality Gates (Troubleshooting)

dry-run has succeeded
the actual update has succeeded and the save completion message has been confirmed
the expected sheet (e.g., YYYYMMDD) has been created/updated
the number of changes has been confirmed (even if 0, the reason can be explained)
if URL / ID columns were repaired, the presence/absence of hyperlinks and their targets were spot-checked in representative cells
temporary files have been deleted
it has been confirmed that the workbook that was open has not been closed, or that it has not been unintentionally saved under a different name
EXCEL.EXE it has been confirmed that there is no residual hidden state and no workbook lock file

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

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'])