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clean-data-xls

针对活动工作表、指定单元格区域或独立 .xlsx 文件,先分析各列类型与异常,列出问题及拟修复项,优先通过辅助列公式完成去空白、统一大小写、数字和日期格式标准化,确认后再处理重复项与空值,并报告修改前后结果。

anthropics
热度 20.68
最近更新 2026-10-19 02:04:02
分类:效率依赖:代码执行 / 读写文件应用场景:数据处理 / 工作流

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add anthropics/financial-services/clean-data-xls
Metadata
name
clean-data-xls
description
Clean up messy spreadsheet data — trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates, and flag mixed-type columns. Use when data is messy, inconsistent, or needs prep before analysis. Triggers on "clean this data", "clean up this sheet", "normalize this data", "fix formatting", "dedupe", "standardize this column", "this data is messy".

Clean Data

Clean messy data in the active sheet or a specified range.

Environment

  • If running inside Excel (Office Add-in / Office JS): Use Office JS directly (Excel.run(async (context) => {...})). Read via range.values, write helper-column formulas via range.formulas = [["=TRIM(A2)"]]. The in-place vs helper-column decision still applies.
  • If operating on a standalone .xlsx file: Use Python/openpyxl.

Workflow

Step 1: Scope

  • If a range is given (e.g. A1:F200), use it
  • Otherwise use the full used range of the active sheet
  • Profile each column: detect its dominant type (text / number / date) and identify outliers

Step 2: Detect issues

Issue What to look for
Whitespace leading/trailing spaces, double spaces
Casing inconsistent casing in categorical columns (usa / USA / Usa)
Number-as-text numeric values stored as text; stray $, ,, % in number cells
Dates mixed formats in the same column (3/8/26, 2026-03-08, March 8 2026)
Duplicates exact-duplicate rows and near-duplicates (case/whitespace differences)
Blanks empty cells in otherwise-populated columns
Mixed types a column that's 98% numbers but has 3 text entries
Encoding mojibake (é, ’), non-printing characters
Errors #REF!, #N/A, #VALUE!, #DIV/0!

Step 3: Propose fixes

Show a summary table before changing anything:

Column Issue Count Proposed Fix

Step 4: Apply

  • Prefer formulas over hardcoded cleaned values — where the cleaned output can be expressed as a formula (e.g. =TRIM(A2), =VALUE(SUBSTITUTE(B2,"$","")), =UPPER(C2), =DATEVALUE(D2)), write the formula in an adjacent helper column rather than computing the result in Python and overwriting the original. This keeps the transformation transparent and auditable.
  • Only overwrite in place with computed values when the user explicitly asks for it, or when no sensible formula equivalent exists (e.g. encoding/mojibake repair)
  • For destructive operations (removing duplicates, filling blanks, overwriting originals), confirm with the user first
  • After each category of fix (whitespace → casing → number conversion → dates → dedup), show the user a sample of what changed and get confirmation before moving to the next category
  • Report a before/after summary of what changed