xlsx

name	xlsx
description	Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. Use when working with .xlsx, .csv, .tsv files.
license	Apache-2.0
metadata	{"author":"Agentman","version":"1.0"}
compatibility	Requires openpyxl, pandas, and LibreOffice (for recalculation).

xlsx Skill

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization.

When to use this skill

Use this skill when you need to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for:

1. Creating new spreadsheets with formulas and formatting.
2. Reading or analyzing data.
3. Modifying existing spreadsheets while preserving formulas.
4. Data analysis and visualization in spreadsheets.
5. Recalculating formulas.

All Excel files

Zero Formula Errors

• Every Excel model MUST be delivered with ZERO formula errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?)

Preserve Existing Templates

• Study and EXACTLY match existing format, style, and conventions when modifying files.
• Existing template conventions ALWAYS override these guidelines.

Financial models

Color Coding Standards

• Blue text (RGB: 0,0,255): Hardcoded inputs.
• Black text (RGB: 0,0,0): ALL formulas and calculations.
• Green text (RGB: 0,128,0): Links pulling from other worksheets within same workbook.
• Red text (RGB: 255,0,0): External links to other files.
• Yellow background (RGB: 255,255,0): Key assumptions.

Excel File Workflows

CRITICAL: Use Formulas, Not Hardcoded Values

Always use Excel formulas instead of calculating values in Python and hardcoding them.

Reading and analyzing data with pandas

import pandas as pd
df = pd.read_excel('file.xlsx')
df.head()
df.to_excel('output.xlsx', index=False)

Recalculating formulas

Excel files modified by openpyxl contain formulas as strings but not calculated values. Use scripts/recalc.py to recalculate formulas:

python scripts/recalc.py <excel_file> [timeout_seconds]

Best Practices

• pandas: Best for data analysis and bulk operations.
• openpyxl: Best for complex formatting and formulas.
• Cell indices are 1-based (row=1, column=1 is A1).
• Use data_only=True in load_workbook to read values, but be careful as saving will lose formulas.