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xlsx-manipulation — Claude Code skill: Excel via openpyxl

A skill that builds and edits Excel files from code: creates .xlsx workbooks with openpyxl, adds formulas, formatting and charts.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add claude-office-skills/skills --skill xlsx-manipulation -a claude-code
Open on GitHub

What xlsx-manipulation does

The skill turns a plain request into openpyxl code and runs it. You describe the spreadsheet you want or the change you need, and the agent writes the script: fills cells, adds formulas, applies styles, saves the file. No clicking through cells.

Inside are working examples for the core operations: reading and writing cells, formulas like =SUM(A1:C1) and named ranges, fonts, fills, borders, number formats, conditional formatting, BarChart, LineChart and PieChart, dropdown validation through DataValidation, plus sheet handling, column widths, frozen panes and auto-filter. There is a separate block for the office-mcp server with read_xlsx, create_xlsx, apply_formula and create_chart tools.

For heavy formatting the skill suggests starting from an existing .xlsx template, since that keeps styling intact better than rebuilding it in code. It also advises batching operations and saving periodically on large workbooks. If openpyxl is missing from your environment, none of the generated code will run, so check that first.

What it helps with

  • build a budget sheet with monthly tracking
  • highlight values above a threshold with conditional formatting
  • produce a pivot-table-style summary from raw data
  • assemble a dashboard with charts and KPIs
  • add dropdown lists and input validation

Example prompts for the agent

  • Create a budget spreadsheet with monthly tracking and sum formulas
  • Add conditional formatting to column B so values above 100 stand out
  • Build a bar chart of sales by region on the report sheet

How to install xlsx-manipulation

1Skills CLI (Vercel)

npx skills add claude-office-skills/skills --skill xlsx-manipulation -a claude-code

2Manually: put the files in place

~/.claude/skills/xlsx-manipulation/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/xlsx-manipulation/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about xlsx-manipulation

How do I install xlsx-manipulation in Claude?

Run npx skills add claude-office-skills/skills --skill xlsx-manipulation -a claude-code. The skill file then lands at ~/.claude/skills/xlsx-manipulation/SKILL.md.

What does xlsx-manipulation do?

It creates, edits and manipulates Excel spreadsheets through the openpyxl library: cells, formulas, styles, charts, data validation and sheet operations.

What does the skill need to run?

The generated code runs on Python with openpyxl. For complex formatting the skill suggests starting from an existing .xlsx template.

Source text

SKILL.md · MIT
---
# ═══════════════════════════════════════════════════════════════════════════════
# CLAUDE OFFICE SKILL - Enhanced Metadata v2.0
# ═══════════════════════════════════════════════════════════════════════════════

# Basic Information
name: xlsx-manipulation
description: "Create, edit, and manipulate Excel spreadsheets programmatically using openpyxl"
version: "1.0"
author: claude-office-skills
license: MIT

# Categorization
category: spreadsheet
tags:
  - xlsx
  - excel
  - manipulation
  - editing
department: All

# AI Model Compatibility
models:
  recommended:
    - claude-sonnet-4
    - claude-opus-4
  compatible:
    - claude-3-5-sonnet
    - gpt-4
    - gpt-4o

# MCP Tools Integration
mcp:
  server: office-mcp
  tools:
    - read_xlsx
    - create_xlsx
    - apply_formula
    - create_chart

# Skill Capabilities
capabilities:
  - spreadsheet_creation
  - formula_handling
  - charting

# Language Support
languages:
  - en
  - zh
---

# XLSX Manipulation Skill

## Overview

This skill enables programmatic creation, editing, and manipulation of Microsoft Excel (.xlsx) spreadsheets using the **openpyxl** library. Create professional spreadsheets with formulas, formatting, charts, and data validation without manual editing.

## How to Use

1. Describe the spreadsheet you want to create or modify
2. Provide data, formulas, or formatting requirements
3. I'll generate openpyxl code and execute it

**Example prompts:**
- "Create a budget spreadsheet with monthly tracking"
- "Add conditional formatting to highlight values above threshold"
- "Generate a pivot-table-like summary from this data"
- "Create a dashboard with charts and KPIs"

## Domain Knowledge

### openpyxl Fundamentals

```python
from openpyxl import Workbook, load_workbook
from openpyxl.styles import Font, Fill, Border, Alignment
from openpyxl.chart import BarChart, Reference

# Create new workbook
wb = Workbook()
ws = wb.active

# Or open existing
wb = load_workbook('existing.xlsx')
ws = wb.active
```

### Workbook Structure
```
Workbook
├── worksheets (sheets/tabs)
│   ├── cells (data storage)
│   ├── rows/columns (formatting)
│   ├── merged_cells
│   └── charts
├── defined_names (named ranges)
└── styles (formatting templates)
```

### Working with Cells

#### Basic Cell Operations
```python
# By cell reference
ws['A1'] = 'Header'
ws['B1'] = 42

# By row, column
ws.cell(row=1, column=3, value='Data')

# Multiple cells
ws['A1:C1'] = [['Col1', 'Col2', 'Col3']]

# Append rows
ws.append(['Row', 'Data', 'Here'])
```

#### Reading Cells
```python
# Single cell
value = ws['A1'].value

# Cell range
for row in ws['A1:C3']:
    for cell in row:
        print(cell.value)

# Iterate rows
for row in ws.iter_rows(min_row=1, max_row=10, min_col=1, max_col=3):
    for cell in row:
        print(cell.value)
```

### Formulas
```python
# Basic formulas
ws['D1'] = '=SUM(A1:C1)'
ws['D2'] = '=AVERAGE(A2:C2)'
ws['E1'] = '=IF(D1>100,"High","Low")'

# Named ranges
from openpyxl.workbook.defined_name import DefinedName
ref = "Sheet!$A$1:$C$10"
defn = DefinedName("SalesData", attr_text=ref)
wb.defined_names.add(defn)

# Use named range
ws['F1'] = '=SUM(SalesData)'
```

### Formatting

#### Cell Styles
```python
from openpyxl.styles import Font, Fill, PatternFill, Border, Side, Alignment

# Font
ws['A1'].font = Font(
    name='Arial',
    size=14,
    bold=True,
    italic=False,
    color='FF0000'  # Red
)

# Fill (background)
ws['A1'].fill = PatternFill(
    start_color='FFFF00',  # Yellow
    end_color='FFFF00',
    fill_type='solid'
)

# Border
thin_border = Border(
    left=Side(style='thin'),
    right=Side(style='thin'),
    top=Side(style='thin'),
    bottom=Side(style='thin')
)
ws['A1'].border = thin_border

# Alignment
ws['A1'].alignment = Alignment(
    horizontal='center',
    vertical='center',
    wrap_text=True
)
```

#### Number Formats
```python
# Currency
ws['B2'].number_format = '$#,##0.00'

# Percentage
ws['C2'].number_format = '0.00%'

# Date
ws['D2'].number_format = 'YYYY-MM-DD'

# Custom
ws['E2'].number_format = '#,##0.00 "units"'
```

#### Conditional Formatting
```python
from openpyxl.formatting.rule import ColorScaleRule, CellIsRule, FormulaRule
from openpyxl.styles import PatternFill

# Color scale (heatmap)
color_scale = ColorScaleRule(
    start_type='min', start_color='FF0000',
    end_type='max', end_color='00FF00'
)
ws.conditional_formatting.add('A1:A10', color_scale)

# Cell value rule
red_fill = PatternFill(start_color='FFCCCC', end_color='FFCCCC', fill_type='solid')
rule = CellIsRule(operator='greaterThan', formula=['100'], fill=red_fill)
ws.conditional_formatting.add('B1:B10', rule)
```

### Charts
```python
from openpyxl.chart import BarChart, LineChart, PieChart, Reference

# Prepare data
data = Reference(ws, min_col=2, min_row=1, max_col=3, max_row=5)
categories = Reference(ws, min_col=1, min_row=2, max_row=5)

# Bar Chart
chart = BarChart()
chart.type = "col"  # or "bar" for horizontal
chart.title = "Sales by Region"
chart.add_data(data, titles_from_data=True)
chart.set_categories(categories)
chart.shape = 4
ws.add_chart(chart, "E1")

# Line Chart
line = LineChart()
line.title = "Trend Analysis"
line.add_data(data, titles_from_data=True)
line.set_categories(categories)
ws.add_chart(line, "E15")

# Pie Chart
pie = PieChart()
pie.add_data(data, titles_from_data=True)
pie.set_categories(categories)
ws.add_chart(pie, "M1")
```

### Data Validation
```python
from openpyxl.worksheet.datavalidation import DataValidation

# Dropdown list
dv = DataValidation(
    type="list",
    formula1='"Option1,Option2,Option3"',
    allow_blank=True
)
dv.error = "Please select from list"
dv.errorTitle = "Invalid Input"
ws.add_data_validation(dv)
dv.add('A1:A100')

# Number range
dv_num = DataValidation(
    type="whole",
    operator="between",
    formula1="1",
    formula2="100"
)
ws.add_data_validation(dv_num)
dv_num.add('B1:B100')
```

### Sheet Operations
```python
# Create new sheet
ws2 = wb.create_sheet("Data")
ws3 = wb.create_sheet("Summary", 0)  # At position 0

# Rename
ws.title = "Main Report"

# Delete
del wb["Sheet2"]

# Copy
source = wb["Template"]
target = wb.copy_worksheet(source)
```

### Row/Column Operations
```python
# Set column width
ws.column_dimensions['A'].width = 20

# Set row height
ws.row_dimensions[1].height = 30

# Hide column
ws.column_dimensions['C'].hidden = True

# Freeze panes
ws.freeze_panes = 'B2'  # Freeze row 1 and column A

# Auto-filter
ws.auto_filter.ref = "A1:D100"
```

## Best Practices

1. **Use Templates**: Start with a .xlsx template for complex formatting
2. **Batch Operations**: Minimize cell-by-cell operations for speed
3. **Named Ranges**: Use defined names for clearer formulas
4. **Data Validation**: Add validation to prevent input errors
5. **Save Incrementally**: For large files, save periodically

## Common Patterns

### Data Import
```python
def import_csv_to_xlsx(csv_path, xlsx_path):
    import csv
    wb = Workbook()
    ws = wb.active
    
    with open(csv_path) as f:
        reader = csv.reader(f)
        for row in reader:
            ws.append(row)
    
    wb.save(xlsx_path)
```

### Report Template
```python
def create_monthly_report(data, output_path):
    wb = Workbook()
    ws = wb.active
    ws.title = "Monthly Report"
    
    # Headers
    headers = ['Date', 'Revenue', 'Expenses', 'Profit']
    ws.append(headers)
    
    # Style headers
    for col in range(1, 5):
        cell = ws.cell(1, col)
        cell.font = Font(bold=True)
        cell.fill = PatternFill('solid', fgColor='4472C4')
        cell.font = Font(bold=True, color='FFFFFF')
    
    # Data
    for row in data:
        ws.append(row)
    
    # Add totals
    last_row = len(data) + 1
    ws.cell(last_row + 1, 1, 'TOTAL')
    ws.cell(last_row + 1, 2, f'=SUM(B2:B{last_row})')
    ws.cell(last_row + 1, 3, f'=SUM(C2:C{last_row})')
    ws.cell(last_row + 1, 4, f'=SUM(D2:D{last_row})')
    
    wb.save(output_path)
```

## Examples

### Example 1: Budget Tracker
```python
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
from openpyxl.utils import get_column_letter

wb = Workbook()
ws = wb.active
ws.title = "Budget 2024"

# Headers
months = ['Category', 'Jan', 'Feb', 'Mar', 'Q1 Total']
ws.append(months)

# Categories and data
budget_data = [
    ['Salary', 5000, 5000, 5000],
    ['Rent', -1500, -1500, -1500],
    ['Utilities', -200, -180, -220],
    ['Food', -400, -450, -380],
    ['Transport', -150, -160, -140],
    ['Entertainment', -200, -250, -200],
]

for row in budget_data:
    ws.append(row + [f'=SUM(B{ws.max_row + 1}:D{ws.max_row + 1})'])

# Total row
ws.append(['TOTAL', 
    f'=SUM(B2:B{ws.max_row})',
    f'=SUM(C2:C{ws.max_row})',
    f'=SUM(D2:D{ws.max_row})',
    f'=SUM(E2:E{ws.max_row})'
])

# Formatting
header_fill = PatternFill('solid', fgColor='366092')
header_font = Font(bold=True, color='FFFFFF')

for cell in ws[1]:
    cell.fill = header_fill
    cell.font = header_font
    cell.alignment = Alignment(horizontal='center')

# Currency format
for row in ws.iter_rows(min_row=2, min_col=2, max_col=5):
    for cell in row:
        cell.number_format = '$#,##0.00'

# Column widths
ws.column_dimensions['A'].width = 15
for col in range(2, 6):
    ws.column_dimensions[get_column_letter(col)].width = 12

wb.save('budget_2024.xlsx')
```

### Example 2: Sales Dashboard
```python
from openpyxl import Workbook
from openpyxl.chart import BarChart, PieChart, Reference
from openpyxl.styles import Font, PatternFill

wb = Workbook()
ws = wb.active
ws.title = "Sales Dashboard"

# Data
ws.append(['Region', 'Q1', 'Q2', 'Q3', 'Q4'])
data = [
    ['North', 150000, 165000, 180000, 195000],
    ['South', 120000, 125000, 140000, 155000],
    ['East', 180000, 190000, 210000, 225000],
    ['West', 95000, 110000, 125000, 140000],
]
for row in data:
    ws.append(row)

# Bar Chart
data_ref = Reference(ws, min_col=2, min_row=1, max_col=5, max_row=5)
cats_ref = Reference(ws, min_col=1, min_row=2, max_row=5)

bar = BarChart()
bar.type = "col"
bar.title = "Quarterly Sales by Region"
bar.add_data(data_ref, titles_from_data=True)
bar.set_categories(cats_ref)
bar.height = 10
bar.width = 15
ws.add_chart(bar, "A8")

# Pie Chart - Q4 breakdown
pie_data = Reference(ws, min_col=5, min_row=1, max_row=5)
pie = PieChart()
pie.title = "Q4 Market Share"
pie.add_data(pie_data, titles_from_data=True)
pie.set_categories(cats_ref)
ws.add_chart(pie, "J8")

wb.save('sales_dashboard.xlsx')
```

## Limitations

- Cannot execute VBA macros
- Complex pivot tables not fully supported
- Limited sparkline support
- External data connections not supported
- Some advanced chart types unavailable

## Installation

```bash
pip install openpyxl
```

## Resources

- [openpyxl Documentation](https://openpyxl.readthedocs.io/)
- [GitHub Repository](https://github.com/theorchard/openpyxl)
- [Working with Styles](https://openpyxl.readthedocs.io/en/stable/styles.html)

© claude-office-skills · MIT · claude-office-skills/skills

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