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Excel/XLSX Chinese Assistant

Office Efficiency Updated 2026.08.30

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Please follow https://skillhub.cn/install/skillhub.md to install @kunlungrowth/cn-xlsx-helper

About this skill

Problem

In daily office work, Excel is often used for daily reports, ledgers, sales sheets, and reconciliation. Manually creating templates, filling formulas, and cleaning fields is time-consuming and error-prone. When asking a model for a table, the answer may lack a runnable generation script.

How It Works

The skill turns requirements into Python scripts based on openpyxl. A typical workflow includes:
- Define fields: confirm headers, purpose, formulas, charts, or validation.
- Generate script: write headers and sample data, add formulas like =A2*B2, insert BarChart charts, or use DataValidation for dropdown lists.
- Validate locally: run the script to create a .xlsx file, then open it in Excel to trigger formula calculation.
- Clean data: iterate cells to trim spaces, normalize formats, and remove duplicates.

Boundaries

It fits structured sheets, basic formulas, bar charts, reconciliation, and report scripts. For complex pivot tables, advanced functions, or desktop-only Excel processing, generate a base file first and refine it manually. To read calculated values from an existing workbook, use load_workbook(path, data_only=True).

Use Cases

  • An ops user needs a sales daily report template with date and revenue fields, a growth formula, and a bar chart.
  • A finance user cleans a reconciliation sheet by trimming cell spaces, normalizing amount formats, and removing duplicates.
  • A project staff member builds an attendance ledger with date, name, and status fields plus yes/no validation.
  • A sales analyst generates a ledger, appends rows, and fills a quantity-times-price formula.

Best For

  • Ops staff who produce weekly sales reports, campaign ledgers, or inventory sheets and want runnable openpyxl scripts.
  • Finance or settlement staff who clean reconciliation sheets, normalize field formats, and add basic formulas.
  • Project or administrative staff who build attendance and ledger templates with headers, sample data, and dropdown validation.
  • Data support or junior developers who turn table requirements into Python-based workbook generation logic.