| 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.
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
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) |
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)
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