🎯

excel-processor

🎯Skill

from ntaksh42/agents

VibeIndex|
What it does

Automates Excel file processing by manipulating data, generating formulas, creating charts, and transforming spreadsheets across multiple programming languages.

📦

Part of

ntaksh42/agents(78 items)

excel-processor

Installation

📋 No install commands found in docs. Showing default command. Check GitHub for actual instructions.
Quick InstallInstall with npx
npx skills add ntaksh42/agents --skill excel-processor
2Installs
-
AddedFeb 4, 2026

Skill Details

SKILL.md

Process Excel files with data manipulation, formula generation, and chart creation. Use when working with spreadsheets or Excel data.

Overview

# Excel Processor Skill

Excelファイルの作成、編集、解析を行うスキルです。

概要

Excelの読み書き、数式、グラフ、スタイル設定を自動化します。

主な機能

  • データ読み書き: セル、行、列の操作
  • 数式: SUM、VLOOKUP等の自動生成
  • グラフ: 折れ線、棒、円グラフ
  • スタイル: 色、フォント、罫線
  • 条件付き書式: ルールベースの書式
  • ピボットテーブル: 集計表作成
  • CSV/JSON変換: データ変換

使用方法

Python (openpyxl)

```python

from openpyxl import Workbook, load_workbook

from openpyxl.styles import Font, PatternFill

from openpyxl.chart import BarChart, Reference

# 新規作成

wb = Workbook()

ws = wb.active

ws.title = "Sales Data"

# データ入力

ws['A1'] = "Product"

ws['B1'] = "Sales"

ws.append(["iPhone", 1000])

ws.append(["MacBook", 800])

# スタイル設定

ws['A1'].font = Font(bold=True)

ws['A1'].fill = PatternFill(start_color="FFFF00", fill_type="solid")

# グラフ作成

chart = BarChart()

data = Reference(ws, min_col=2, min_row=1, max_row=3)

cats = Reference(ws, min_col=1, min_row=2, max_row=3)

chart.add_data(data, titles_from_data=True)

chart.set_categories(cats)

ws.add_chart(chart, "D1")

wb.save("sales.xlsx")

# 読み込み

wb = load_workbook("sales.xlsx")

ws = wb.active

for row in ws.iter_rows(values_only=True):

print(row)

```

JavaScript (ExcelJS)

```javascript

const ExcelJS = require('exceljs');

async function createExcel() {

const workbook = new ExcelJS.Workbook();

const worksheet = workbook.addWorksheet('Sales');

// ヘッダー

worksheet.columns = [

{ header: 'Product', key: 'product', width: 15 },

{ header: 'Sales', key: 'sales', width: 10 }

];

// データ

worksheet.addRow({ product: 'iPhone', sales: 1000 });

worksheet.addRow({ product: 'MacBook', sales: 800 });

// スタイル

worksheet.getRow(1).font = { bold: true };

worksheet.getRow(1).fill = {

type: 'pattern',

pattern: 'solid',

fgColor: { argb: 'FFFFFF00' }

};

// 数式

worksheet.getCell('B4').value = { formula: 'SUM(B2:B3)' };

await workbook.xlsx.writeFile('sales.xlsx');

}

```

データ分析 (pandas)

```python

import pandas as pd

# 読み込み

df = pd.read_excel('data.xlsx', sheet_name='Sheet1')

# 分析

summary = df.groupby('Category')['Sales'].sum()

# 書き込み

with pd.ExcelWriter('output.xlsx', engine='openpyxl') as writer:

df.to_excel(writer, sheet_name='Data', index=False)

summary.to_excel(writer, sheet_name='Summary')

```

高度な機能

```python

from openpyxl.utils.dataframe import dataframe_to_rows

from openpyxl.chart import LineChart, Reference

# データフレームから

import pandas as pd

df = pd.DataFrame({

'Month': ['Jan', 'Feb', 'Mar'],

'Sales': [100, 150, 120]

})

wb = Workbook()

ws = wb.active

for r in dataframe_to_rows(df, index=False, header=True):

ws.append(r)

# 条件付き書式

from openpyxl.formatting.rule import ColorScaleRule

ws.conditional_formatting.add('B2:B4',

ColorScaleRule(start_type='min', start_color='AA0000',

end_type='max', end_color='00AA00'))

# 数式

ws['B5'] = '=AVERAGE(B2:B4)'

ws['B6'] = '=MAX(B2:B4)'

wb.save('advanced.xlsx')

```

ライブラリ

Python

  • openpyxl: .xlsx読み書き
  • xlrd/xlwt: .xls (旧形式)
  • pandas: データ分析
  • xlsxwriter: 高速書き込み

JavaScript

  • ExcelJS: 完全機能
  • xlsx: SheetJS、読み込み特化
  • node-xlsx: シンプル

Go

  • excelize: 高性能

Java

  • Apache POI: 標準ライブラリ

バージョン情報

  • スキルバージョン: 1.0.0
  • 最終更新: 2025-01-22

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