Clanker CloudAn AI spreadsheet assistant can help explain a table, find inconsistencies, prepare a cleaned copy, and turn the results into a report. The most important instruction is what the columns mean. Without that, a seemingly sensible cleanup can change the business meaning of the data.
In Clanker Cloud Workspace, add the spreadsheet or CSV to a Project and describe the intended output. Keep the original file so you can compare the result with the source. For connected Google files, the Google connection guide explains the available access and copy behavior.
Define the data before changing it
Take this fictional office register:
| Request ID | Amount | Currency | Due date |
|---|---|---|---|
| 001 | 120 | USD | 2026-10-02 |
| 002 | 80 | USD | Not confirmed |
| 003 | 150 | EUR | 2026-10-05 |
The request ID is text, even though it contains digits. Removing its leading zeros would change the identifier. The unknown due date is missing information, not a reason to insert today's date. The amounts cannot be added into a meaningful single-currency total without an explicit conversion method.
Ask the assistant to summarize its understanding of the columns first. Correct any assumptions before requesting transformations.
Agree on a small set of cleanup rules
Useful rules might include trimming accidental spaces, checking required identifiers, identifying repeated rows, and standardizing dates only when the input format is unambiguous.
Distinguish a duplicate record from two legitimate records that happen to share a customer name. State the key used to identify duplicates. If there is no reliable key, ask for a review list instead of automatic removal.
The CSV cleaning guide covers missing values, dates, identifiers, and reconciliation. Apply the rules that match your file; do not ask for a generic “make this data clean” pass and hope the assumptions are harmless.
Request the change record with the output
A practical prompt is:
Check this request register. Keep request IDs as text, preserve unknown dates, and keep currencies separate. Identify possible duplicate IDs without deleting rows. Save a new cleaned copy and an issues table with source row, proposed change, reason, and anything needing my decision. Reconcile input and output row counts.
For the small example above, the separate totals are USD 200 and EUR 150. The unresolved date should still be unresolved. Those simple expected results make useful checks before trying a larger file.
Open the exported copy in the spreadsheet application you intend to use. A preview may look right while a later import interprets an identifier as a number or a date differently. Check representative rows and formulas, not only the overall appearance.
Turn the checked data into a useful view
Once the table is trustworthy, ask for a summary such as requests by status or overdue items based on a stated reference date. Require the report to identify excluded or unresolved rows.
If people need to explore the same data repeatedly, an operations dashboard template can provide a starting point for a private App. Test filters, totals, empty states, and access before sharing it. A generated dashboard is another result to verify, not a replacement for checking the underlying table.
Save the definitions and successful rules for the next import. That is where an office assistant becomes reusable: the next file arrives with a known method and a clear set of checks.
Sources
Give your assistant one useful job
Work from a sample CSV, keep the original intact, and review a cleaned copy with a record of the changes.