Working with Excel files programmatically.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionxlsxExecute the skills CLI command in your project's root directory to begin installation:
Fetches xlsx from bobmatnyc/claude-mpm-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate xlsx. Access via /xlsx in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Working with Excel files programmatically.
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)
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')
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 decimals
# Add formula
ws['C2'] = '=A2+B2'
# Sum column
ws['D10'] = '=SUM(D2:D9)'
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)
# 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')
# 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')
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');
import pandas as pd
df = pd.read_csv('data.csv')
df.to_excel('data.xlsx', index=False)
df = pd.read_excel('data.xlsx')
df.to_csv('data.csv', index=False)
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)
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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xlsx reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend xlsx for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in xlsx — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for xlsx matched our evaluation — installs cleanly and behaves as described in the markdown.
xlsx is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added xlsx from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
xlsx reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for xlsx matched our evaluation — installs cleanly and behaves as described in the markdown.
xlsx reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: xlsx is the kind of skill you can hand to a new teammate without a long onboarding doc.
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