用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/bobmatnyc/claude-mpm-skills --skill xlsx命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
LinkedIn automation via the Linked API CLI - fetch profiles, search people and companies, send messages, manage connections, create posts, react, comment, and run Sales Navigator and custom workflows. Use when the user wants to interact with LinkedIn.
Xquik X data automation API - Use REST or MCP for tweet search, user lookup, follower exports, media downloads, monitors, webhooks, giveaway draws, and confirmation-gated X actions.
MCP (Model Context Protocol) - Build AI-native servers with tools, resources, and prompts. TypeScript/Python SDKs for Claude Desktop integration.
基于 SOC 职业分类
正在显示 SKILL.md
| name | xlsx |
| description | Working with Excel files programmatically. |
| user-invocable | false |
| disable-model-invocation | true |
| updated_at | "2025-10-30T17:00:00.000Z" |
| tags | ["excel","xlsx","spreadsheet","data"] |
| progressive_disclosure | {"entry_point":{"summary":"Working with Excel files programmatically.","when_to_use":"When working with xlsx or related functionality.","quick_start":"1. Review the core concepts below. 2. Apply patterns to your use case. 3. Follow best practices for implementation."}} |
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)