
Xlsx
- 470 installs
- 63 repo stars
- Updated July 18, 2026
- bobmatnyc/claude-mpm-skills
xlsx is a Claude Code skill that opens, edits, cleans, and exports Excel spreadsheet deliverables when tabular .xlsx files are the primary input or output for reporting, ops, or client handoffs.
About
xlsx is an agent skill from bobmatnyc/claude-mpm-skills for working directly with Excel spreadsheet files as first-class artifacts. The skill supports opening .xlsx workbooks, cleaning and restructuring tabular data, applying edits agents cannot express in plain text alone, and exporting finished spreadsheets for reporting, operations, or client deliverables. Developers reach for xlsx when pipeline output or stakeholder requests require a polished Excel handoff instead of raw CSV dumps. The skill bridges agent reasoning and spreadsheet-native formats teams still expect in finance, ops, and consulting workflows.
- Read and write xlsx and csv files
- Formula, formatting, and chart edits
- Messy tabular data cleanup
- Format conversion between spreadsheets
- Agent-friendly spreadsheet deliverables
Xlsx by the numbers
- 470 all-time installs (skills.sh)
- Ranked #156 of 688 Office & Documents skills by installs in the Skillselion catalog
- Data as of Aug 1, 2026 (Skillselion catalog sync)
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| Installs | 470 |
|---|---|
| repo stars | ★ 63 |
| Last updated | July 18, 2026 |
| Repository | bobmatnyc/claude-mpm-skills ↗ |
How do agents edit and export Excel xlsx files?
Open, edit, clean, and export spreadsheet deliverables when tabular files are the primary input or output for reporting, ops, or client handoffs.
Who is it for?
Developers or technical operators whose workflows produce or consume .xlsx files as primary reporting or client deliverable artifacts.
Skip if: Developers who need database ETL, programmatic CSV-only pipelines, or spreadsheet tasks better handled by dedicated Python pandas automation skills.
When should I use this skill?
An agent must open, modify, clean, or export an .xlsx file as the main deliverable for reporting, ops, or a client handoff.
What you get
Edited .xlsx workbook, cleaned tabular sheets, and exported spreadsheet deliverable ready for handoff.
- Edited .xlsx workbook
- Cleaned tabular sheets
Files
Excel/XLSX Manipulation
Working with Excel files programmatically.
Python (openpyxl)
Reading Excel
from openpyxl import load_workbook
wb = load_workbook('data.xlsx')
ws = wb.active # Get active sheet
# Read cell
value = ws['A1'].value
# Iterate rows
for row in ws.iter_rows(min_row=2, values_only=True):
print(row)Writing Excel
from openpyxl import Workbook
wb = Workbook()
ws = wb.active
ws.title = "Data"
# Write data
ws['A1'] = 'Name'
ws['B1'] = 'Age'
ws.append(['John', 30])
ws.append(['Jane', 25])
wb.save('output.xlsx')Formatting
from openpyxl.styles import Font, PatternFill
# Bold header
ws['A1'].font = Font(bold=True)
# Background color
ws['A1'].fill = PatternFill(start_color="FFFF00", fill_type="solid")
# Number format
ws['B2'].number_format = '0.00' # Two decimalsFormulas
# Add formula
ws['C2'] = '=A2+B2'
# Sum column
ws['D10'] = '=SUM(D2:D9)'Python (pandas)
Reading Excel
import pandas as pd
# Read sheet
df = pd.read_excel('data.xlsx', sheet_name='Sheet1')
# Read multiple sheets
dfs = pd.read_excel('data.xlsx', sheet_name=None)Writing Excel
# Write DataFrame
df.to_excel('output.xlsx', index=False)
# Multiple sheets
with pd.ExcelWriter('output.xlsx') as writer:
df1.to_excel(writer, sheet_name='Sheet1')
df2.to_excel(writer, sheet_name='Sheet2')Data Transformation
# Filter
filtered = df[df['Age'] > 25]
# Group by
grouped = df.groupby('Department')['Salary'].mean()
# Pivot
pivot = df.pivot_table(values='Sales', index='Region', columns='Product')JavaScript (xlsx)
import XLSX from 'xlsx';
// Read file
const workbook = XLSX.readFile('data.xlsx');
const sheetName = workbook.SheetNames[0];
const worksheet = workbook.Sheets[sheetName];
// Convert to JSON
const data = XLSX.utils.sheet_to_json(worksheet);
// Write file
const newWorksheet = XLSX.utils.json_to_sheet(data);
const newWorkbook = XLSX.utils.book_new();
XLSX.utils.book_append_sheet(newWorkbook, newWorksheet, 'Data');
XLSX.writeFile(newWorkbook, 'output.xlsx');Common Operations
CSV to Excel
import pandas as pd
df = pd.read_csv('data.csv')
df.to_excel('data.xlsx', index=False)Excel to CSV
df = pd.read_excel('data.xlsx')
df.to_csv('data.csv', index=False)Merging Excel Files
dfs = []
for file in ['file1.xlsx', 'file2.xlsx', 'file3.xlsx']:
df = pd.read_excel(file)
dfs.append(df)
combined = pd.concat(dfs, ignore_index=True)
combined.to_excel('merged.xlsx', index=False)Remember
- Close workbooks after use
- Handle large files in chunks
- Validate data before writing
- Use pandas for data analysis, openpyxl for formatting
{
"name": "xlsx",
"version": "1.0.0",
"category": "universal",
"toolchain": null,
"framework": null,
"tags": [],
"entry_point_tokens": 60,
"full_tokens": 882,
"author": "bobmatnyc",
"license": "MIT",
"requires": [],
"updated": "2025-11-21",
"source_path": "xlsx.md",
"source": "https://github.com/bobmatnyc/claude-mpm",
"created": "2025-11-21",
"modified": "2025-11-21",
"maintainer": "Claude MPM Team",
"attribution_required": true,
"repository": "https://github.com/bobmatnyc/claude-mpm-skills"
}
Related skills
FAQ
What file format does the xlsx skill target?
The xlsx skill targets Excel .xlsx workbook files, supporting open, edit, clean, and export workflows when spreadsheets are the primary input or output for reporting, ops, or client deliverables.
When should developers choose xlsx over CSV-focused skills?
Developers should choose xlsx when stakeholders require Excel-native workbooks with sheet structure and formatting preserved, rather than plain CSV exports suited to programmatic ETL pipelines.