XLSX Skill Reference

Overview

The XLSX skill provides comprehensive spreadsheet manipulation capabilities including opening, reading, editing, and creating .xlsx, .xlsm, .csv, and .tsv files. Use this skill for adding columns, computing formulas, formatting, charting, cleaning messy data, and converting between tabular file formats.

Use this skill when working with spreadsheet files as primary input or output. Trigger especially when users reference spreadsheet files by name or want to create, edit, or clean tabular data files.

Requirements for Outputs

All Excel Files

Professional Font:

Use consistent, professional font (e.g., Arial, Times New Roman) unless otherwise instructed

Zero Formula Errors:

Every Excel model MUST be delivered with ZERO formula errors
No #REF!, #DIV/0!, #VALUE!, #N/A, or #NAME? errors allowed

Preserve Existing Templates:

When updating files, study and EXACTLY match existing format, style, and conventions
Never impose standardized formatting on files with established patterns
Existing template conventions ALWAYS override guidelines

Financial Models

Color Coding Standards

Industry-Standard Color Conventions (unless otherwise stated):

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 requiring updates

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
- Format: $#,##0;($#,##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) 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 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: Bloomberg Terminal, 8/15/2025, AAPL US Equity”
- “Source: FactSet, 8/20/2025, Consensus Estimates Screen”

Reading and Analyzing Data

Data Analysis with pandas

For data analysis, visualization, and basic operations:

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)

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):
```
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
- Fix identified errors and recalculate again

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 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

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

Pandas Best Practices

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

Common Error Types

Error	Cause	Solution
#REF!	Invalid cell reference	Verify cell references exist
#DIV/0!	Division by zero	Add error handling or check denominators
#VALUE!	Wrong data type in formula	Verify cell contains expected data type
#N/A	Value not available (VLOOKUP failed)	Check lookup values exist in range
#NAME?	Unrecognized formula name	Check formula spelling and syntax

LibreOffice Required: LibreOffice is automatically configured for formula recalculation using the scripts/recalc.py script, including in sandboxed environments where Unix sockets are restricted (handled by scripts/office/soffice.py).

PPTX Skill Reference

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Document Skills

Creative & Design

Development & Technical

Enterprise & Communication

Overview

Requirements for Outputs

All Excel Files

Financial Models

Reading and Analyzing Data

Data Analysis with pandas

CRITICAL: Use Formulas, Not Hardcoded Values

❌ WRONG - Hardcoding Calculated Values

✅ CORRECT - Using Excel Formulas

Common Workflow

Creating New Excel Files

Editing Existing Excel Files

Recalculating Formulas

Formula Verification Checklist

Interpreting scripts/recalc.py Output

Best Practices

Library Selection

Working with openpyxl

Working with pandas

Code Style Guidelines

Common Error Types

Build docs developers (and LLMs) love

Document Skills

Creative & Design

Development & Technical

Enterprise & Communication

​Overview

​Requirements for Outputs

​All Excel Files

​Financial Models

​Reading and Analyzing Data

​Data Analysis with pandas

​CRITICAL: Use Formulas, Not Hardcoded Values

​❌ WRONG - Hardcoding Calculated Values

​✅ CORRECT - Using Excel Formulas

​Common Workflow

​Creating New Excel Files

​Editing Existing Excel Files

​Recalculating Formulas

​Formula Verification Checklist

​Interpreting scripts/recalc.py Output

​Best Practices

​Library Selection

​Working with openpyxl

​Working with pandas

​Code Style Guidelines

​Common Error Types

Build docs developers (and LLMs) love

Overview

Requirements for Outputs

All Excel Files

Financial Models

Reading and Analyzing Data

Data Analysis with pandas

CRITICAL: Use Formulas, Not Hardcoded Values

❌ WRONG - Hardcoding Calculated Values

✅ CORRECT - Using Excel Formulas

Common Workflow

Creating New Excel Files

Editing Existing Excel Files

Recalculating Formulas

Formula Verification Checklist

Interpreting scripts/recalc.py Output

Best Practices

Library Selection

Working with openpyxl

Working with pandas

Code Style Guidelines

Common Error Types