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name: excel-automation description: Automate Excel operations including reading, writing, formatting, and formula manipulation version: 1.0.0 author: Shared Agent Toolkit tags: [excel, automation, data-processing]

Excel Automation Skill

This skill provides comprehensive Excel automation capabilities using Python's openpyxl library.

Capabilities

• Read Excel files - Extract data from worksheets, cells, ranges
• Write Excel files - Create new workbooks, add data, save files
• Format cells - Apply fonts, colors, borders, number formats
• Formulas - Insert and calculate formulas
• Charts - Create basic charts and graphs
• Data validation - Add dropdown lists and validation rules

Prerequisites

The skill requires Python 3.7+ with the following packages:

• openpyxl - Excel file manipulation
• pandas - Data analysis and manipulation (optional but recommended)

Usage Instructions

When a user requests Excel automation, follow this process:

1. Understand the Request

Clarify:

• What operation is needed (read, write, format, analyze)?
• Which file(s) are involved?
• What specific data or ranges?
• What is the desired output?

2. Use Helper Scripts

The scripts/ directory contains ready-to-use utilities:

• excel_reader.py - Read data from Excel files
• excel_writer.py - Write data to new or existing files
• excel_formatter.py - Apply formatting and styles
• excel_formulas.py - Insert and calculate formulas

3. Execute Operations

Use the appropriate script with the user's requirements:

Copy# Read data from a worksheet
python scripts/excel_reader.py --file "data.xlsx" --sheet "Sheet1" --range "A1:D10"

# Write data to Excel
python scripts/excel_writer.py --file "output.xlsx" --data "data.json"

# Format cells
python scripts/excel_formatter.py --file "report.xlsx" --format-header

4. Provide Results

• Show the user what was accomplished
• Provide file paths for generated files
• Explain any data transformations
• Suggest next steps if applicable

Common Patterns

Pattern 1: Read and Analyze

Copyimport openpyxl

# Load workbook
wb = openpyxl.load_workbook('data.xlsx')
ws = wb['Sheet1']

# Read data
data = []
for row in ws.iter_rows(min_row=2, values_only=True):
    data.append(row)

# Analyze
print(f"Total rows: {len(data)}")

Pattern 2: Create Report

Copyimport openpyxl
from openpyxl.styles import Font, Alignment

# Create workbook
wb = openpyxl.Workbook()
ws = wb.active
ws.title = "Report"

# Add header
ws['A1'] = "Report Title"
ws['A1'].font = Font(size=14, bold=True)
ws['A1'].alignment = Alignment(horizontal='center')

# Add data
data = [["Name", "Value"], ["Item 1", 100], ["Item 2", 200]]
for row in data:
    ws.append(row)

wb.save('report.xlsx')

Pattern 3: Formulas and Calculations

Copyimport openpyxl

wb = openpyxl.load_workbook('budget.xlsx')
ws = wb['Budget']

# Add SUM formula
ws['D10'] = '=SUM(D2:D9)'

# Add formula for each row
for row in range(2, 10):
    ws[f'E{row}'] = f'=C{row}*D{row}'

wb.save('budget.xlsx')

Error Handling

Always handle common errors:

• File not found
• Sheet doesn't exist
• Invalid cell reference
• Permission denied (file open in Excel)
• Data type mismatches

Best Practices

1. Backup files before modifying existing workbooks
2. Validate input - check file exists, sheet names are correct
3. Close files properly - ensure workbooks are saved and closed
4. Use data_only=True when reading calculated values
5. Consider memory - for large files, use read_only=True

Example Usage

User Request: "Read the quarterly sales data from Q4_Sales.xlsx and create a summary report"

Your Response:

1. Read the data using excel_reader.py
2. Analyze the data (calculate totals, averages)
3. Create a new formatted summary report
4. Save as "Q4_Sales_Summary.xlsx"
5. Inform user of completion with file location

Limitations

• Very large files (>100MB) may be slow
• Complex formatting may not be preserved perfectly
• Charts are basic - complex charts may require manual creation
• Macros (VBA) are not supported

Resources

• openpyxl documentation
• Excel formula reference