document-xlsx（document XLSX 命令行）

Document XLSX Skill — Quick Reference

This skill enables creation, editing, and analysis of Excel spreadsheets programmatically. Claude should apply these patterns when users need to generate data reports, financial models, automate Excel workflows, or process spreadsheet data.

Modern Best Practices (Jan 2026):

• Treat spreadsheets as software: clear inputs/outputs, auditability, and versioning.
• Protect data integrity: control totals, validation, and traceability to sources.
• Accessibility: labels, contrast, structure; use Excel's Accessibility Checker; meet procurement/regulatory requirements when distributing externally.
• If distributing in the EU or regulated contexts, follow applicable accessibility requirements (often aligned with EN 301 549 / WCAG).
• Ship with a review loop and an owner (avoid "mystery models").
• Security: treat untrusted input/workbooks as hostile (formula injection, external links, hidden content, macros).

Quick Reference

Guardrails and Caveats

• Formula calculation: libraries write formulas; Excel computes results when opened. If you need computed values server-side, calculate in code and write values (or use a dedicated formula engine).
• Pivot tables: programmatic creation is limited. Prefer pandas summaries (pivot tables as data) or Excel automation (xlwings/Office Scripts/VBA) if you truly need native pivots.
• Macros: openpyxl can preserve existing VBA (keep_vba=True) but does not author macros; never generate or execute macros from untrusted input.
• Spreadsheet injection: never put untrusted strings into formula fields; write them as text values and validate/sanitize user-provided data used in exports.

Core Operations

Create Spreadsheet (Node.js - exceljs)

import ExcelJS from 'exceljs';

const workbook = new ExcelJS.Workbook();
const sheet = workbook.addWorksheet('Sales Report');

// Headers with styling
sheet.columns = [
  { header: 'Product', key: 'product', width: 20 },
  { header: 'Quantity', key: 'qty', width: 12 },
  { header: 'Price', key: 'price', width: 12 },
  { header: 'Total', key: 'total', width: 15 },
];

// Style header row
sheet.getRow(1).font = { bold: true };
sheet.getRow(1).fill = {
  type: 'pattern',
  pattern: 'solid',
  fgColor: { argb: 'FF4472C4' }
};

// Add data
const data = [
  { product: 'Widget A', qty: 100, price: 10 },
  { product: 'Widget B', qty: 50, price: 25 },
];

data.forEach((item, index) => {
  sheet.addRow({
    product: item.product,
    qty: item.qty,
    price: item.price,
    total: { formula: `B${index + 2}*C${index + 2}` }
  });
});

// Add totals row
const lastRow = sheet.rowCount + 1;
sheet.addRow({
  product: 'TOTAL',
  total: { formula: `SUM(D2:D${lastRow - 1})` }
});

// Currency formatting
sheet.getColumn('price').numFmt = '$#,##0.00';
sheet.getColumn('total').numFmt = '$#,##0.00';

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

Create Spreadsheet (Python - openpyxl)

from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill

wb = Workbook()
ws = wb.active
ws.title = 'Sales Report'

# Headers
headers = ['Product', 'Quantity', 'Price', 'Total']
for col, header in enumerate(headers, 1):
    cell = ws.cell(row=1, column=col, value=header)
    cell.font = Font(bold=True, color='FFFFFF')
    cell.fill = PatternFill(start_color='4472C4', end_color='4472C4', fill_type='solid')

# Data
data = [
    ('Widget A', 100, 10),
    ('Widget B', 50, 25),
    ('Widget C', 75, 15),
]

for row_idx, (product, qty, price) in enumerate(data, 2):
    ws.cell(row=row_idx, column=1, value=product)
    ws.cell(row=row_idx, column=2, value=qty)
    ws.cell(row=row_idx, column=3, value=price)
    ws.cell(row=row_idx, column=4, value=f'=B{row_idx}*C{row_idx}')

# Totals row
total_row = len(data) + 2
ws.cell(row=total_row, column=1, value='TOTAL')
ws.cell(row=total_row, column=4, value=f'=SUM(D2:D{total_row-1})')

# Number formatting
for row in range(2, total_row + 1):
    ws.cell(row=row, column=3).number_format = '$#,##0.00'
    ws.cell(row=row, column=4).number_format = '$#,##0.00'

wb.save('report.xlsx')

Read and Analyze (Python - pandas)

import pandas as pd

# Read Excel file
df = pd.read_excel('data.xlsx', sheet_name='Sheet1')

# Analysis
summary = df.groupby('Category').agg({
    'Sales': 'sum',
    'Quantity': 'mean'
}).round(2)

# Write to Excel with formatting
with pd.ExcelWriter('analysis.xlsx', engine='openpyxl') as writer:
    df.to_excel(writer, sheet_name='Raw Data', index=False)
    summary.to_excel(writer, sheet_name='Summary')

    # Auto-adjust column widths
    for sheet in writer.sheets.values():
        for column in sheet.columns:
            max_length = max(len(str(cell.value)) for cell in column)
            sheet.column_dimensions[column[0].column_letter].width = max_length + 2

Add Charts (Python)

from openpyxl.chart import BarChart, Reference

chart = BarChart()
chart.title = 'Sales by Product'
chart.x_axis.title = 'Product'
chart.y_axis.title = 'Sales'

# Data range (assumes column D contains the series and row 1 is headers)
max_row = ws.max_row
data_ref = Reference(ws, min_col=4, min_row=1, max_row=max_row, max_col=4)
categories = Reference(ws, min_col=1, min_row=2, max_row=max_row)

chart.add_data(data_ref, titles_from_data=True)
chart.set_categories(categories)
chart.shape = 4

ws.add_chart(chart, 'F2')

Conditional Formatting

from openpyxl.formatting.rule import ColorScaleRule, FormulaRule
from openpyxl.styles import PatternFill

# Color scale (heatmap)
ws.conditional_formatting.add(
    'D2:D100',
    ColorScaleRule(
        start_type='min', start_color='FF0000',
        end_type='max', end_color='00FF00'
    )
)

# Highlight cells above threshold
red_fill = PatternFill(start_color='FFCCCC', fill_type='solid')
ws.conditional_formatting.add(
    'D2:D100',
    FormulaRule(formula=['D2>1000'], fill=red_fill)
)

Common Formulas Reference

Decision Tree

Excel Task: [What do you need?]
    ├─ Create new spreadsheet?
    │   ├─ Simple data export → pandas to_excel()
    │   ├─ Formatted report → exceljs or openpyxl
    │   └─ With charts → openpyxl charts module
    │
    ├─ Read/analyze existing?
    │   ├─ Data analysis → pandas read_excel()
    │   ├─ Preserve formatting → openpyxl load_workbook()
    │   └─ Fast parsing → xlsx (SheetJS)
    │
    ├─ Modify existing?
    │   ├─ Add data → openpyxl (preserves formatting)
    │   └─ Update formulas → openpyxl
    │
    └─ Complex features?
        ├─ Pivot tables → pandas summary tables or xlwings (native pivots)
        ├─ Data validation → openpyxl DataValidation
        └─ Macros → preserve only; use xlwings for Excel automation

Do / Avoid (Jan 2026)

Do

• Separate Inputs / Calculations / Outputs (tabs or clear sections).
• Keep assumptions explicit (value + unit + source + date).
• Add control totals and reconciliation checks for imported data.

Avoid

• Hardcoded constants inside formulas without a documented assumption.
• Hidden rows/columns that change results without documentation.
• Sharing sheets with customer PII or secrets.

What Good Looks Like

• Structure: clear Inputs/Assumptions, Calculations, and Outputs separation (tabs or sections).
• Integrity: no #REF!, broken named ranges, or hardcoded constants hidden in formulas.
• Traceability: every key output ties back to labeled inputs (units + source + date).
• Checks: control totals, reconciliations, and error flags that fail loudly.
• Review: independent review pass using assets/spreadsheet-model-review-checklist.md.

Optional: AI / Automation

Use only when explicitly requested and policy-compliant.

• Generate first-pass formulas/charts; humans verify correctness and edge cases.
• Draft documentation tabs (assumptions, glossary); do not invent source data.

Navigation

Resources

• references/excel-formulas.md — Formula reference and patterns
• references/excel-formatting.md — Styling, conditional formatting
• references/excel-charts.md — Chart types and customization
• references/excel-data-validation.md — Dropdowns, input constraints, cascading validation
• references/excel-pivot-tables.md — Pivot workarounds, summary patterns, pandas
• references/excel-security-protection.md — Sheet protection, formula injection prevention
• data/sources.json — Library documentation links

Templates

• assets/financial-report.md — Financial statement template
• assets/data-dashboard.md — Dashboard with charts
• assets/spreadsheet-model-review-checklist.md — Model QA checklist (assumptions, formulas, traceability)

Related Skills

• ../document-pdf/SKILL.md — PDF generation from data
• ../ai-ml-data-science/SKILL.md — Data analysis patterns
• ../data-sql-optimization/SKILL.md — Database to Excel workflows

Fact-Checking

• Use web search/web fetch to verify current external facts, versions, pricing, deadlines, regulations, or platform behavior before final answers.
• Prefer primary sources; report source links and dates for volatile information.
• If web access is unavailable, state the limitation and mark guidance as unverified.