Apply — Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xls
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name
xlsx
description
Apply — Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xls
[{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true},{"name":"context","type":"string","description":"Additional context or background information","required":false}]
output_schema
[{"name":"report","type":"string","description":"Analysis report or summary from xlsx"}]
Requirements for Outputs
All Excel files
Zero Formula Errors
Every Excel model MUST be delivered with ZERO formula errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?)
A user may ask you to create, edit, or analyze the contents of an .xlsx file. You have different tools and workflows available for different tasks.
Important Requirements
LibreOffice Required for Formula Recalculation: You can assume LibreOffice is installed for recalculating formula values using the recalc.py script. The script automatically configures LibreOffice on first run
Reading and analyzing data
Data analysis with pandas
For data analysis, visualization, and basic operations, use pandas which provides powerful data manipulation capabilities:
import pandas as pd
# Read Excel
df = pd.read_excel('file.xlsx') # Default: first sheet
all_sheets = pd.read_excel('file.xlsx', sheet_name=None) # All sheets as dict# Analyze
df.head() # Preview data
df.info() # Column info
df.describe() # Statistics# Write Excel
df.to_excel('output.xlsx', index=False)
Excel File Workflows
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
# Bad: Calculating in Python and hardcoding result
total = df['Sales'].sum()
sheet['B10'] = total # Hardcodes 5000# Bad: Computing growth rate in Python
growth = (df.iloc[-1]['Revenue'] - df.iloc[0]['Revenue']) / df.iloc[0]['Revenue']
sheet['C5'] = growth # Hardcodes 0.15# Bad: Python calculation for average
avg = sum(values) / len(values)
sheet['D20'] = avg # Hardcodes 42.5
✅ CORRECT - Using Excel Formulas
# Good: Let Excel calculate the sum
sheet['B10'] = '=SUM(B2:B9)'# Good: Growth rate as Excel formula
sheet['C5'] = '=(C4-C2)/C2'# Good: Average using Excel function
sheet['D20'] = '=AVERAGE(D2:D19)'
This applies to ALL calculations - totals, percentages, ratios, differences, etc. The spreadsheet should be able to recalculate when source data changes.
Common Workflow
Choose tool: pandas for data, openpyxl for formulas/formatting
Create/Load: Create new workbook or load existing file
Modify: Add/edit data, formulas, and formatting
Save: Write to file
Recalculate formulas (MANDATORY IF USING FORMULAS): Use the recalc.py script
python recalc.py output.xlsx
Verify and fix any errors:
The script returns JSON with error details
If status is errors_found, check error_summary for specific error types and locations
Fix the identified errors and recalculate again
Common errors to fix:
#REF!: Invalid cell references
#DIV/0!: Division by zero
#VALUE!: Wrong data type in formula
#NAME?: Unrecognized formula name
Creating new Excel files
# Using openpyxl for formulas and formattingfrom 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('output.xlsx')
Editing existing Excel files
# Using openpyxl to preserve formulas and formattingfrom openpyxl import load_workbook
# Load existing file
wb = load_workbook('existing.xlsx')
sheet = wb.active # or wb['SheetName'] for specific sheet# Working with multiple sheetsfor sheet_name in wb.sheetnames:
sheet = wb[sheet_name]
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('modified.xlsx')
Recalculating formulas
Excel files created or modified by openpyxl contain formulas as strings but not calculated values. Use the provided recalc.py script to recalculate formulas:
python recalc.py <excel_file> [timeout_seconds]
Example:
python recalc.py output.xlsx 30
The script:
Automatically sets up LibreOffice macro on first run
Recalculates all formulas in all sheets
Scans ALL cells for Excel errors (#REF!, #DIV/0!, etc.)
Returns JSON with detailed error locations and counts
Works on both Linux and macOS
Formula Verification Checklist
Quick checks to ensure formulas work correctly:
Essential Verification
Test 2-3 sample references: Verify they pull correct values before building full model
Column mapping: Confirm Excel columns match (e.g., column 64 = BL, not BK)
NaN handling: Check for null values with pd.notna()
Far-right columns: FY data often in columns 50+
Multiple matches: Search all occurrences, not just first
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
Formula Testing Strategy
Start small: Test formulas on 2-3 cells before applying broadly
Verify dependencies: Check all cells referenced in formulas exist
Test edge cases: Include zero, negative, and very large values
Interpreting recalc.py Output
The script returns JSON with error details:
{"status":"success",// or "errors_found""total_errors":0,// Total error count"total_formulas":42,// Number of formulas in file"error_summary":{// Only present if errors found"#REF!":{"count":2,"locations":["Sheet1!B5","Sheet1!C10"]}}}
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)
Use data_only=True to read calculated values: load_workbook('file.xlsx', data_only=True)
Warning: If opened with data_only=True and saved, formulas are replaced with values and permanently lost
For large files: Use read_only=True for reading or write_only=True for writing
Formulas are preserved but not evaluated - use recalc.py to update values
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'])
IMPORTANT: When generating Python code for Excel operations:
Write minimal, concise Python code without unnecessary comments
Avoid verbose variable names and redundant operations
Avoid unnecessary print statements
For Excel files themselves:
Add comments to cells with complex formulas or important assumptions
Document data sources for hardcoded values
Include notes for key calculations and model sections
Why This Skill Exists
Apply — Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xls
When to Use
Use this skill when the task requires xlsx capabilities.
What If Fails
If this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.
