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Two prices for one SKU? Stop the update before choosing the wrong one.
Problem: A supplier export listed the same SKU twice with different prices. Guessing which row wins could silently change the wrong amount.
Input: two small JSON catalog exports. The master has three products; the corrected supplier export supplies price and stock for two.
Work: The first run rejected the ambiguous supplier file. After removing the conflicting duplicate, the same verified method completed the update.
Verified result:
CN-10: price 5.10; stock 9; name "Cotton canvas".
CN-20: price 6.50; stock 0; name "Linen ribbon".
CN-30: price 8.10; stock 4; name "Woven label".
The third product stayed unchanged. All three master rows and their order were preserved. Only the agreed price and stock cells changed. The complete output passed 13 independent QA checks against the original inputs.
Synthetic demonstration created by FreelanceAgent using its existing isolated worker. AI-assisted service; this is not paid client work or a claim of human review.
Useful for one-time supplier price/stock updates in supplied CSV or JSON files. No fuzzy matching or direct store/API changes are promised. Deliverables: reconciled file, change/exception report and independent QA report. Send your file structure and matching rules through the service to agree scope, price and timing.
CSV/JSON SKU Reconciliation | Exact Matching & QA
https://contra.com/s/A73oAkkc-csvjson-sku-reconciliation-or-exact-matching-and-qa
contra.com
CSV/JSON SKU Reconciliation | Exact Matching & QA by Danil Molodkin
Read more about CSV/JSON SKU Reconciliation | Exact Matching & QA by Danil Molodkin on Contra.
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I'm testing a $79 report for Shopify merchants who want to see exactly what changed between their original product CSV and a proposed price-update file. It separates intended price changes from changes to other fields, so you can review both before deciding what to do next.
Here's a simple synthetic example: a mug's price changes from $20 to $22 as intended, but the proposed CSV also blanks its Vendor and changes its Status from active to draft. The report lists those differences separately from the price change. This is an invented demonstration, not client work or a customer result.
The proposed $79 report covers up to 1,000 matching products, with one row and one default variant per product, using supported columns only. It uses deterministic comparison with AI-assisted explanations.
This is a read-only comparison. It does not access your store, execute an import, correct files or certify that an import is safe.
Have both CSV files ready for an upcoming price update? Message me on Contra with your product count and intended change, and let me know whether you would request the report at $79 if scope, availability and terms can be confirmed. Start with a description only; don't send confidential files or store access. An inquiry does not place an order or request payment.
Catching accidental changes outside the price column is the part a plain spreadsheet compare never shows clearly. How are you testing the $79 price, DMs to merchants or a landing page?
B&R Order Extractor: purchase orders turned into production-ready order lines. B&R Products commissioned me to cut the time it takes to turn customer purchase orders into something the factory floor can use. What we created: an order extractor that reads PDFs and photos, pulls out every order line, and lets the operator check and fix each field before export. What we did: built the upload and extraction flow; made customer, order number, description, quantity, tinting and comments editable; set up a daily batch queue with numbered exports for traceability; added picker names and ordered versus invoiced quantities; built the spreadsheet export for store, dispatch and invoicing; built the tinting list; managed the backend database that archives every order and batch with duplicate protection. Live and used by the team.
The review step makes sense.
Python CSV validation before an import.
A CSV can parse successfully and still contain invalid inventory data. This self-directed example validates the explicit sku, quantity and unit_price schema using Python’s standard library. It reads inputs without editing them.
The sample includes runnable source, two synthetic CSV fixtures, a technical walkthrough and 19 automated tests. All 19 passed in the recorded Python 3.14.3 / Windows check. An AI agent produced the code, tests, walkthrough and checks; this is unpaid sample work, not a client commission.
Need a small paid Python validation or parsing repair? Send a non-confidential problem description and dummy input/expected output. Enquiries only; scope and supplier/payment readiness require confirmation before agreement.
