Direct Python Execution for Spreadsheet Tasks

When to Use This Skill

Use direct

run_shell

• Reading or writing complex Excel files with multiple sheets
• Applying formulas, formatting, or data transformations
• Working with

  openpyxl

  ,

  pandas

  , or similar libraries
• The operation involves multiple steps that could exceed agent step limits
• You need precise control over error handling and debugging
• Complex scripts benefit from file-based execution for better reliability

Why Direct Execution?

The

shell_agent

• Hit maximum step limits on complex multi-step operations
• Produce unexplained errors on formatting operations
• Fail on intricate spreadsheet reads/writes due to iterative parsing
• Fail to parse heredoc syntax correctly, causing 'unknown error' failures

Direct

run_shell

• Executes in a single step with no iteration limits
• Provides clearer, immediate error messages
• Handles complex operations without step constraints
• Gives full control over library imports and execution flow
• Writing scripts to

  .py

  files first avoids shell_agent parsing issues with heredocs

How to Use

Recommended Pattern: Write Script to File First

For complex multi-line scripts, especially when using shell_agent as executor:

# Step 1: Write the Python script to a file
cat > process_spreadsheet.py << 'EOF'
import openpyxl
from openpyxl import Workbook

# Your spreadsheet code here
wb = openpyxl.load_workbook('file.xlsx')
# ... operations ...
wb.save('output.xlsx')
print('Success')
EOF

# Step 2: Execute the script
python3 process_spreadsheet.py

Alternative Pattern: Inline Heredoc (Simple Scripts Only)

For short, simple scripts when NOT using shell_agent as the executor:

python3 << 'EOF'
import openpyxl
from openpyxl import Workbook

# Your spreadsheet code here
wb = openpyxl.load_workbook('file.xlsx')
# ... operations ...
wb.save('output.xlsx')
print('Success')
EOF

Example 1: Read and Transform Excel Data

Write to file first, then execute:

import pandas as pd

# Load data from specific sheet
df = pd.read_excel('input.xlsx', sheet_name='Revenue')

# Apply transformations
df['Net_Revenue'] = df['Gross_Revenue'] * (1 - df['Tax_Rate'])

# Save results
df.to_excel('output.xlsx', index=False, sheet_name='Processed')

Example 2: Multi-Sheet Operations with openpyxl

Write to file first, then execute:

from openpyxl import load_workbook

wb = load_workbook('tour_data.xlsx')

# Iterate through sheets
for sheet_name in wb.sheetnames:
    ws = wb[sheet_name]
    # Apply formatting or calculations
    for row in ws.iter_rows(min_row=2, max_col=5):
        # Process cells
        pass

wb.save('tour_data_processed.xlsx')

Example 3: Complex Formatting Operations

Write to file first, then execute:

from openpyxl import load_workbook
from openpyxl.styles import Font, PatternFill, Alignment

wb = load_workbook('report.xlsx')
ws = wb.active

# Apply header styling
header_fill = PatternFill(start_color='4472C4', fill_type='solid')
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')

wb.save('report_formatted.xlsx')

Example 4: Error Handling Pattern

Write to file first, then execute:

import sys
from openpyxl import load_workbook

try:
    wb = load_workbook('data.xlsx')
    ws = wb.active
    
    # Your operations here
    value = ws['A1'].value
    
    wb.save('output.xlsx')
    print(f"Success: Processed {ws.max_row} rows")
    
except Exception as e:
    print(f"Error: {str(e)}", file=sys.stderr)
    sys.exit(1)

Best Practices

1. Prefer file-based execution for complex scripts: write to

   .py

   file first, then execute via

   run_shell

2. Import only needed libraries to reduce execution time
3. Print clear success/error messages for debugging
4. Save intermediate results for complex multi-step transformations
5. Test with small data before scaling to large spreadsheets
6. Use pandas for data manipulation and openpyxl for formatting when both are needed
7. Clean up temporary script files after execution if they won't be reused

When NOT to Use This Skill

• Simple single-cell reads/writes (use shell_agent or basic commands)
• Operations that require interactive user input
• Tasks where you need the agent to iteratively refine the approach

Common Libraries

openpyxl

pandas

xlrd

xlsxwriter

Troubleshooting

Issue: Heredoc syntax fails with 'unknown error' when using shell_agent

• Solution: Write the Python script to a

  .py

  file first, then execute it with

  python3 script.py

  . This pattern is significantly more reliable than inline heredoc execution when shell_agent is the executor.

Issue: FileNotFoundError

• Solution: Verify the file path is absolute or relative to the working directory

Issue: PermissionError

• Solution: Ensure the file is not open in another application

Issue: MemoryError on large files

• Solution: Process data in chunks using pandas

  chunksize

  parameter

Issue: Formatting not applying

• Solution: Ensure you're modifying cell styles before saving, and use

  .copy()

  for style objects