Spreadsheet Cleanup and Normalization

Messy data quietly corrupts every decision made from it. This skill turns inconsistent, duplicated, badly formatted tabular data into a clean, analysis-ready version, with a clear log of every change so the result is trustworthy and auditable. The audit log matters as much as the cleaning, because silent changes to data destroy trust.

1. Profile before touching anything

Report row and column counts, detect the header row, and flag the issues: blank rows, merged cells, inconsistent types, duplicate keys, mixed date or number formats, stray characters. The user should see the state before any change.

2. Agree the rules for anything destructive

Confirm how to handle duplicates, blanks, and ambiguous values before removing or overwriting. Destructive changes made on assumption are how good data gets ruined.

3. Clean systematically

Trim whitespace, standardize casing and formats, split or merge columns as needed, normalize dates and numbers to one format, and deduplicate on the agreed key. Keep each transformation reversible in principle.

4. Validate

Re-profile and confirm the issues are resolved without dropping legitimate data. Spot-check that totals and counts still reconcile.

5. Log everything

Produce a change log so every transformation is auditable and nothing changed silently.

Deliver the cleaned file plus:

## Cleaning report
- Starting shape: [rows x columns]
- Ending shape: [rows x columns]
- Changes applied (each transformation and how many cells or rows it touched)
- Rows removed and why
- Items needing human review (ambiguous values, possible duplicates)

• Deleting data silently.
• Guessing on ambiguous values (a date that could be US or international, a possible duplicate) instead of flagging.
• Overwriting the original file instead of cleaning into a copy.
• No change log, so the result cannot be trusted.

A contact export with mixed casing, three date formats, and duplicate emails is normalized to one date format, title-cased names, and deduplicated on email, with 42 changes and 5 ambiguous rows flagged for review, all logged.