AI spreadsheet cleaner guide: columns, duplicates, formats, and exports

Profile before cleaning

Start with dataset source, purpose, row count, columns, known issues, and target format. This prevents accidental cleanup that removes useful context.

Look for duplicate rows, mixed date formats, currency mismatches, empty required fields, inconsistent names, and merged cells.

Write explicit cleanup rules

A good cleanup rule is repeatable: normalize date to YYYY-MM-DD, trim spaces, map vendor aliases, dedupe by invoice number plus vendor, split full name into first and last name, or convert currency symbols into numeric values.

Save the rule and the reason. If you change the sheet later, you need to know why.

Export with caveats

Every cleaned spreadsheet should include caveats: fields not checked, rows excluded, assumptions, and manual review items. The cleanest file can still be wrong if the rules were wrong.

How this becomes an OmniSaver record

The practical output of this guide is not another long note. It is a compact record with enough structure to help you act later: the source, the situation, the important fields, the reason it mattered, and the next step.

For AI Context, that means choosing the smallest Saver that matches the moment and saving only the fields that will help you compare, repeat, prove, decide, or reuse the context later.

A simple workflow

Start by saving one real example while the context is fresh. Review it after the outcome is visible. Compare it with the next similar record. Reuse the best version when the situation appears again.

This save, review, compare, reuse loop is the reason OmniSaver uses tiny apps instead of one giant database. Each Saver keeps the record close to the behavior it supports.