xlsx

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

Important: All scripts/ paths are relative to this skill directory. Run with: cd {this_skill_dir} && python scripts/... Or use the cwd parameter of execute_shell_command.

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.

Design Principles

Spreadsheets must be auditable. The reader should understand every number's origin.

Layout Principles

• Left-to-right, top-to-bottom: Inputs → calculations → outputs. A spreadsheet that jumps back and forth confuses.
• Headers always: Every column/row region needs a header. Never assume the reader knows what column D contains.
• One table per sheet: Mixing unrelated data on one sheet causes formula errors and confusion.
• Summary → Detail: First sheet = executive summary / dashboard. Subsequent sheets = supporting calculations.

Formula Hygiene

• Assumptions in one place: Growth rates, tax rates, discount rates — all in a named Assumptions sheet or region. Never scattered.
• Named ranges for complex logic: =NPV(discount_rate, cash_flows) is readable; =NPV('Inputs'!$B$5, 'Sheet2'!C10:C20) is not.
• No magic numbers: Every hardcoded number must have a comment or source reference.

Color Coding

(Applied automatically when financial model is detected, per Requirements for Outputs above.)

Content-to-Structure Mapping

Data Type	Recommended Structure	Why
Time series (monthly/quarterly/annual)	Columns = time periods, Rows = metrics	Standard financial layout, easy formula fill
Lookup/reference data	Dedicated sheet with named table	Prevents errors from manual lookup formulas
Scenario comparison	Side-by-side columns (Base / Upside / Downside)	Visual diff at a glance
Supporting calculation	Dedicated sheet, output summary sheet	Keeps main sheet clean
Data input form	Structured table (not raw range)	Table auto-expands with new rows
Dashboard	PivotTable + charts on first sheet	Reader sees the answer first

Prerequisites

• openpyxl: Excel file creation and editing
• pandas: data analysis and bulk operations
• LibreOffice (soffice): formula recalculation via scripts/recalc.py
• git is optional but improves redlining diff output in validation workflows.
• On Windows, dependencies must be installed and available in PATH; if missing, report the dependency issue and stop (do not keep retrying).

Important Requirements

LibreOffice Required for Formula Recalculation: Use scripts/recalc.py to recalculate formula values. The script auto-configures LibreOffice on first run and handles sandboxed environments where Unix sockets are restricted (via 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

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. Recalculate formulas (MANDATORY IF USING FORMULAS): Use the scripts/recalc.py script

   python scripts/recalc.py output.xlsx
   

6. 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"]
    }
  }
}

QA (Required)

Assume there are problems. Your job is to find them.

Your first spreadsheet is almost never correct. Approach QA as a bug hunt, not a confirmation step. If you found zero issues on first inspection, check harder.

The Three-Pass QA

1. Structural pass: Are the columns/rows labeled? Does the layout make sense? Any orphaned data?
2. Formula pass: Run scripts/recalc.py. Fix all #REF!, #DIV/0!, #VALUE!, #NAME? errors before delivery.
3. Content pass: Spot-check 3-5 sample values manually. Do the computed results make sense? Try a known input → expected output.

Pre-Delivery Checklist

• Formulas recalculated via scripts/recalc.py — zero errors
• All assumptions pulled to separate cells (no magic numbers)
• Headers present on every column and row region
• Color coding follows financial model conventions (if applicable)
• Number formats consistent (currency, percentage, decimal places)
• Sheet names are meaningful (not "Sheet1", "Sheet2")
• Print area set if the sheet is meant to be printed
• Frozen panes on header row for scrollable sheets
• All zeros display as "-" (not "0" or "$0")
• Units specified in column headers

Verification Loop

1. Generate spreadsheet
2. Run scripts/recalc.py — fix errors
3. Spot-check 3-5 values manually
4. Check structural layout
5. Fix issues
6. Recalculate and re-verify
7. Repeat until clean

Do not declare success until you've completed at least one fix-and-verify cycle.

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

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Common Pitfalls

Pitfall	Correct Approach
Hardcoding values instead of formulas	Always use =SUM(...), =A1*B1 — let Excel calculate
Forgetting data_only=True on read	Use load_workbook(file, data_only=True) to read cached values
Saving after data_only=True read	Opens with data_only=True but never saves back — formulas lost
0-index vs 1-index confusion	openpyxl: row 1, col 1 = A1; pandas: row 0 = Excel row 2
Missing header=None on pandas read	pd.read_excel(..., header=None) when file has no header row
Empty cells in formula ranges	Wrap with IFERROR(value, 0) or IF(ISBLANK(cell), 0, formula)
Wrong column reference in wide sheets	Column 64 = BL, not BK. Double-check far-right columns.
Cross-sheet reference format	Correct: 'Sheet Name'!A1. Single quotes if sheet name contains spaces.

Known Issues

Issue	Workaround / Note
No formula auto-calc in openpyxl	openpyxl stores formulas as strings. Run scripts/recalc.py to evaluate.
Chart data must exist before creation	Create data range first, then reference it in the chart. Cannot add series after chart creation.
Conditional formatting not evaluated	openpyxl can write CF rules but not evaluate them. Open in Excel/LibreOffice to verify.
Large file performance	Files >50MB slow down. Use write_only=True for creation, read_only=True for reads.
Data validation lists break on recalc	Run validation check after recalc — references can shift.
Merged cells cause formula issues	Avoid merged cells in calculation regions. Use "Center Across Selection" instead.