version: "1.0.0" name: xlsx description: "Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file; create a new spreadsheet from scratch or from other data sources; or convert tabular data. If the user mentions a spreadsheet file by name or path — e.g., 'the xlsx in my downloads' — use this skill."

XLSX Creation, Editing, and Analysis in EverFern

Overview

A user may ask you to create, edit, or analyze the contents of an .xlsx file. On Windows, use Python libraries like pandas and openpyxl. Use absolute Windows paths (e.g., C:\Users\Username\Downloads\data.xlsx).

Reading and Analyzing Data

Data Analysis with pandas

For data analysis, visualization, and basic operations, use pandas:

import pandas as pd
# Read Excel
df = pd.read_excel(r'C:\path\to\file.xlsx')  # Default: first sheet
all_sheets = pd.read_excel(r'C:\path\to\file.xlsx', sheet_name=None)  # All sheets as dict
# Analyze
print(df.head())      # Preview data
print(df.info())      # Column info
print(df.describe())  # Statistics
# Write Excel
df.to_excel(r'C:\path\to\output.xlsx', index=False)

Modifying Excel Files

If the user wants you to edit existing spreadsheets while keeping formatting, or write formulas, use openpyxl.

CRITICAL: Use Formulas, Not Hardcoded Values

Always use Excel formulas instead of calculating values in Python and hardcoding them. This ensures the spreadsheet remains dynamic and updateable.

❌ WRONG - Hardcoding Calculated Values

total = df['Sales'].sum()
sheet['B10'] = total  # Hardcodes 5000

✅ CORRECT - Using Excel Formulas

sheet['B10'] = '=SUM(B2:B9)'

Common Workflows

Creating New Excel Files

# pip install openpyxl
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment
wb = Workbook()
sheet = wb.active
# Add data
sheet['A1'] = 'Hello'
sheet['B1'] = 'World'
sheet.append(['Row', 'of', 'data'])
# Add formula
sheet['B2'] = '=SUM(A1:A10)'
# Formatting
sheet['A1'].font = Font(bold=True, color='FF0000')
sheet['A1'].fill = PatternFill('solid', start_color='FFFF00')
sheet['A1'].alignment = Alignment(horizontal='center')
# Column width
sheet.column_dimensions['A'].width = 20
wb.save(r'C:\path\to\output.xlsx')

Editing Existing Excel Files

# Using openpyxl to preserve formulas and formatting
from openpyxl import load_workbook
# Load existing file
wb = load_workbook(r'C:\path\to\existing.xlsx')
sheet = wb.active  # or wb['SheetName'] for specific sheet
# Working with multiple sheets
for sheet_name in wb.sheetnames:
    print(f"Sheet: {sheet_name}")
# Modify cells
sheet['A1'] = 'New Value'
sheet.insert_rows(2)  # Insert row at position 2
sheet.delete_cols(3)  # Delete column 3
# Add new sheet
new_sheet = wb.create_sheet('NewSheet')
new_sheet['A1'] = 'Data'
wb.save(r'C:\path\to\modified.xlsx')

Best Practices

Library Selection

• pandas: Best for data analysis, bulk operations, and simple data export
• openpyxl: Best for complex formatting, formulas, and Excel-specific features

Working with openpyxl

• Cell indices are 1-based (row=1, column=1 refers to cell A1)
• To read calculated values instead of formula strings: load_workbook('file.xlsx', data_only=True).
• Warning: If opened with data_only=True and saved, formulas are replaced with values and permanently lost. Only use this for reading.
• For large files: Use read_only=True for reading.

Working with pandas

• Specify data types to avoid inference issues: pd.read_excel('file.xlsx', dtype={'id': str})
• For large files, read specific columns: pd.read_excel('file.xlsx', usecols=['A', 'C', 'E'])
• Handle dates properly: pd.read_excel('file.xlsx', parse_dates=['date_column'])

Formula Checklist

• [ ] Division by zero: Check denominators before using / in formulas (#DIV/0!)
• [ ] Wrong references: Verify all cell references point to intended cells (#REF!)
• [ ] Cross-sheet references: Use correct format (Sheet1!A1) for linking sheets