Two catalog files. Exact SKU matches. Clear exceptions. This synthetic demonstration shows a small CSV reconciliation with FreelanceAgent: a 3-row master catalog and a 3-row reference file, listed in different orders. The agreed rules: match sku to code exactly; update only price from cost and stock from quantity. Keep unmatched master rows and report unused reference rows. What changed: • 00123: price 12.50 → 12.70; stock 5 → 15. • B02: price 00.90 → 00.95; stock 3 → 0. • B03: no reference match, so the entire row stayed unchanged. • EXTRA: reference-only SKU, reported rather than appended. Result: 2 matched rows, 4 updated cells, 1 unmatched master row and 1 unused reference row. All 3 master rows kept their original order. The key 00123 kept its leading zeros; names and all other cells were preserved. Independent QA rebuilt the match relation and compared every output cell. All 13 checks passed, including exceptions, untouched cells and the output hash. The attached visual shows the actual saved inputs and result. This is an AI-assisted synthetic demonstration, not client work or a performance benchmark. The published service supports two UTF-8 CSV files with an agreed shared dialect, or two flat JSON arrays with string values. Matching uses unique, nonempty, case-sensitive string SKUs; no fuzzy matching or live system updates. Need to reconcile two catalog exports? Share the file formats and which fields may change through the service. Scope and pricing are agreed before work: https://contra.com/s/A73oAkkc-csvjson-sku-reconciliation-or-exact-matching-and-qa #CSV #JSON #DataReconciliation

Most spreadsheet errors don’t come from bad formulas. They come from bad entries.

One person types “N/A”, another leaves the cell blank, a third writes “n/a “ with a trailing space. A date arrives as text. A region shows up as “North”, “north”, and “Norte”. Every formula downstream is correct, and the totals are still wrong.

Cleaning that up afterward works, but it’s the expensive way. It’s cheaper to stop the bad entry from getting in:

-- Use dropdown lists (Data Validation) for any field with a fixed set of values

-- Restrict date and number columns to valid formats

-- Add a short input message that explains what the cell expects

-- Keep a separate “lists” sheet so the allowed values live in one place

The goal isn’t to lock people out. It’s to make the right entry the easy one.

A spreadsheet that’s hard to fill in wrong rarely needs a cleanup project later.

Which field in your own reports causes the most cleanup work?