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clean-data-xls

Detect and fix whitespace, casing, date, and duplicate issues in Excel spreadsheet data.

Updated Sep 17, 2026
Search Tags:#openpyxl#spreadsheet#excel#xlsx#deduplication#data cleaning#data normalization
One-click install
npx skills add https://github.com/hkw2028/toy-project --skill clean-data-xls-hkw2028
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clean-data-xls
Source: https://github.com/hkw2028/toy-project/tree/main/.agents/skills/clean-data-xls
Command: npx skills add https://github.com/hkw2028/toy-project --skill clean-data-xls-hkw2028

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openpyxl.

What problem does it solve? Messy spreadsheet data — inconsistent casing, numbers stored as text, mixed date formats, duplicates, and encoding artifacts — breaks analysis and reporting. This Skill profiles each column, detects common data quality issues, and applies auditable fixes before you analyze or share the data. ## Core Features & Use Cases - Issue Detection: Profiles columns to find whitespace problems, inconsistent casing, numbers-stored-as-text, mixed date formats, duplicates, blanks, mojibake, and formula errors like #REF! or #N/A. - Formula-Based Fixes: Writes transparent helper-column formulas (e.g. =TRIM(A2), =VALUE(SUBSTITUTE(B2,"$","")), =DATEVALUE(D2)) instead of silently overwriting original values. - Dual Environment Support: Works inside Excel via Office JS (Excel.run) or on standalone .xlsx files via Python/openpyxl. - Use Case: You receive an export where the country column mixes "usa", "USA", and "Usa", and revenue is stored as text with $ signs. The Skill proposes a fix table, applies casing and number conversions step by step, and reports a before/after summary. ## Quick Start Clean up the messy data in this spreadsheet by fixing whitespace, casing, dates, and duplicates, showing me a summary of proposed fixes first.

Frequently Asked Questions about clean-data-xls

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I clean messy data in an Excel spreadsheet?▼

Profile each column to detect its dominant type, then fix issues in stages: trim whitespace, standardize casing, convert numbers stored as text, normalize dates, and remove duplicates. Prefer helper-column formulas like =TRIM(A2) so transformations stay auditable.

How to convert numbers stored as text in Excel?▼

Use a formula such as =VALUE(SUBSTITUTE(B2,"$","")) in a helper column to strip currency symbols and convert text to numeric values. This keeps the original column intact while producing clean numeric output.

Can I clean xlsx files with Python instead of Excel?▼

Yes, standalone .xlsx files can be processed with Python and the openpyxl library to read and write cell values. When running inside Excel as an add-in, use Office JS with Excel.run and range.values instead.

Should data cleaning overwrite original values or use formulas?▼

Prefer writing formulas in adjacent helper columns so the transformation remains transparent and auditable. Only overwrite in place when the user explicitly requests it or when no formula equivalent exists, such as mojibake encoding repair.

What data issues can be detected in a spreadsheet column?▼

Detectable issues include leading/trailing whitespace, inconsistent casing, numbers stored as text, mixed date formats, exact and near duplicates, blank cells, mixed-type columns, mojibake encoding artifacts, and errors like #REF!, #N/A, and #DIV/0!.

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