clean-data-xls
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".
Best for Data preparation tasksWorks with GitHubLow risk
⌘source
- author
@anthropics- language
- Python
★11kstars
1.5kforks
May 6, 2026
✦overview.md
Key Features
- ·Trims whitespace and fixes casing
- ·Converts text numbers to numeric
- ·Standardizes date formats
- ·Removes duplicate rows
- ·Flags mixed-type columns
Use Cases
- →Clean messy CSV before loading into analysis tool
- →Normalize inconsistent customer names in CRM export
- →Prepare data for merging with other datasets
Best for
- ✓Data preparation tasks
- ✓Pre-analysis cleanup
Not ideal for
- !Streaming data
- !Real-time data cleaning
FAQs
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 viarange.values, write helper-column formulas viarange.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 |
$install
1-click copynpx skills add anthropics/financial-services --skill clean-data-xlsSafety assessment
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Clarity score
How clear and easy to understand the SKILL.md instructions are, rated from 1 to 5.
4/ 5
very goodClear and well structured, with only minor parts that might need a second read.
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Actionability score
How directly an agent can act on the SKILL.md instructions, rated from 1 to 5.
4/ 5
highMostly actionable with clear steps; only a few small gaps remain.