---

name: document-xlsx description: Create, edit, and analyze Excel spreadsheets with formulas, formatting, charts, pivot tables, and data validation. Supports xlsx, exceljs, openpyxl, and pandas for comprehensive spreadsheet workflows in Node.js and Python.

Document XLSX Skill — Quick Reference

This skill enables creation, editing, and analysis of Excel spreadsheets programmatically. Claude should apply these patterns when users need to generate data reports, financial models, automate Excel workflows, or process spreadsheet data.

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Quick Reference

Task	Tool/Library	Language	When to Use
Create XLSX	exceljs	Node.js	Reports, data exports
Create XLSX	openpyxl	Python	Formatted workbooks
Data analysis	pandas	Python	DataFrame to Excel
Read XLSX	xlsx (SheetJS)	Node.js	Parse spreadsheets
Charts	openpyxl	Python	Embedded visualizations
Styling	exceljs/openpyxl	Both	Conditional formatting

When to Use This Skill

Claude should invoke this skill when a user requests:

• Generate Excel reports from data
• Create spreadsheets with formulas and formatting
• Add charts and pivot tables
• Parse and extract data from Excel files
• Implement conditional formatting
• Create data validation rules
• Automate Excel-based workflows

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Core Operations

Create Spreadsheet (Node.js - exceljs)

import ExcelJS from 'exceljs';

const workbook = new ExcelJS.Workbook();
const sheet = workbook.addWorksheet('Sales Report');

// Headers with styling
sheet.columns = [
  { header: 'Product', key: 'product', width: 20 },
  { header: 'Quantity', key: 'qty', width: 12 },
  { header: 'Price', key: 'price', width: 12 },
  { header: 'Total', key: 'total', width: 15 },
];

// Style header row
sheet.getRow(1).font = { bold: true };
sheet.getRow(1).fill = {
  type: 'pattern',
  pattern: 'solid',
  fgColor: { argb: 'FF4472C4' }
};

// Add data
const data = [
  { product: 'Widget A', qty: 100, price: 10 },
  { product: 'Widget B', qty: 50, price: 25 },
];

data.forEach((item, index) => {
  const row = sheet.addRow({
    product: item.product,
    qty: item.qty,
    price: item.price,
    total: { formula: `B${index + 2}*C${index + 2}` }
  });
});

// Add totals row
const lastRow = sheet.rowCount + 1;
sheet.addRow({
  product: 'TOTAL',
  total: { formula: `SUM(D2:D${lastRow - 1})` }
});

// Currency formatting
sheet.getColumn('price').numFmt = '$#,##0.00';
sheet.getColumn('total').numFmt = '$#,##0.00';

await workbook.xlsx.writeFile('report.xlsx');

Create Spreadsheet (Python - openpyxl)

from openpyxl import Workbook
from openpyxl.styles import Font, Fill, PatternFill, Alignment
from openpyxl.utils import get_column_letter
from openpyxl.chart import BarChart, Reference

wb = Workbook()
ws = wb.active
ws.title = 'Sales Report'

# Headers
headers = ['Product', 'Quantity', 'Price', 'Total']
for col, header in enumerate(headers, 1):
    cell = ws.cell(row=1, column=col, value=header)
    cell.font = Font(bold=True, color='FFFFFF')
    cell.fill = PatternFill(start_color='4472C4', fill_type='solid')

# Data
data = [
    ('Widget A', 100, 10),
    ('Widget B', 50, 25),
    ('Widget C', 75, 15),
]

for row_idx, (product, qty, price) in enumerate(data, 2):
    ws.cell(row=row_idx, column=1, value=product)
    ws.cell(row=row_idx, column=2, value=qty)
    ws.cell(row=row_idx, column=3, value=price)
    ws.cell(row=row_idx, column=4, value=f'=B{row_idx}*C{row_idx}')

# Totals row
total_row = len(data) + 2
ws.cell(row=total_row, column=1, value='TOTAL')
ws.cell(row=total_row, column=4, value=f'=SUM(D2:D{total_row-1})')

# Number formatting
for row in range(2, total_row + 1):
    ws.cell(row=row, column=3).number_format = '$#,##0.00'
    ws.cell(row=row, column=4).number_format = '$#,##0.00'

wb.save('report.xlsx')

Read and Analyze (Python - pandas)

import pandas as pd

# Read Excel file
df = pd.read_excel('data.xlsx', sheet_name='Sheet1')

# Analysis
summary = df.groupby('Category').agg({
    'Sales': 'sum',
    'Quantity': 'mean'
}).round(2)

# Write to Excel with formatting
with pd.ExcelWriter('analysis.xlsx', engine='openpyxl') as writer:
    df.to_excel(writer, sheet_name='Raw Data', index=False)
    summary.to_excel(writer, sheet_name='Summary')

    # Auto-adjust column widths
    for sheet in writer.sheets.values():
        for column in sheet.columns:
            max_length = max(len(str(cell.value)) for cell in column)
            sheet.column_dimensions[column[0].column_letter].width = max_length + 2

Add Charts (Python)

from openpyxl.chart import BarChart, Reference

chart = BarChart()
chart.title = 'Sales by Product'
chart.x_axis.title = 'Product'
chart.y_axis.title = 'Sales'

# Data range
data = Reference(ws, min_col=4, min_row=1, max_row=len(data)+1, max_col=4)
categories = Reference(ws, min_col=1, min_row=2, max_row=len(data)+1)

chart.add_data(data, titles_from_data=True)
chart.set_categories(categories)
chart.shape = 4

ws.add_chart(chart, 'F2')

Conditional Formatting

from openpyxl.formatting.rule import ColorScaleRule, FormulaRule
from openpyxl.styles import PatternFill

# Color scale (heatmap)
ws.conditional_formatting.add(
    'D2:D100',
    ColorScaleRule(
        start_type='min', start_color='FF0000',
        end_type='max', end_color='00FF00'
    )
)

# Highlight cells above threshold
red_fill = PatternFill(start_color='FFCCCC', fill_type='solid')
ws.conditional_formatting.add(
    'D2:D100',
    FormulaRule(formula=['D2>1000'], fill=red_fill)
)

---

Common Formulas Reference

Purpose	Formula	Example
Sum	=SUM(range)	=SUM(A1:A10)
Average	=AVERAGE(range)	=AVERAGE(B2:B100)
Count	=COUNT(range)	=COUNT(C:C)
Conditional sum	=SUMIF(range,criteria,sum_range)	=SUMIF(A:A,"Widget",B:B)
Lookup	=VLOOKUP(value,range,col,FALSE)	=VLOOKUP(A2,Data!A:C,3,FALSE)
If	=IF(condition,true,false)	=IF(B2>100,"High","Low")
Percentage	=value/total	=B2/SUM(B:B)

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Decision Tree

Excel Task: [What do you need?]
    ├─ Create new spreadsheet?
    │   ├─ Simple data export → pandas to_excel()
    │   ├─ Formatted report → exceljs or openpyxl
    │   └─ With charts → openpyxl charts module
    │
    ├─ Read/analyze existing?
    │   ├─ Data analysis → pandas read_excel()
    │   ├─ Preserve formatting → openpyxl load_workbook()
    │   └─ Fast parsing → xlsx (SheetJS)
    │
    ├─ Modify existing?
    │   ├─ Add data → openpyxl (preserves formatting)
    │   └─ Update formulas → openpyxl
    │
    └─ Complex features?
        ├─ Pivot tables → openpyxl or xlwings
        ├─ Data validation → openpyxl DataValidation
        └─ Macros → xlwings (Python-Excel bridge)

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Navigation

Resources

• resources/excel-formulas.md — Formula reference and patterns
• resources/excel-formatting.md — Styling, conditional formatting
• resources/excel-charts.md — Chart types and customization
• data/sources.json — Library documentation links

Templates

• templates/financial-report.md — Financial statement template
• templates/data-dashboard.md — Dashboard with charts

Related Skills

• ../document-pdf/SKILL.md — PDF generation from data
• ../ai-ml-data-science/SKILL.md — Data analysis patterns
• ../data-sql-optimization/SKILL.md — Database to Excel workflows