The builder writes an import-ready CSV in each platform’s common template format, but exact column names and required fields vary by category, country and account — and change often. Amazon’s real flat file is category-specific (often 100+ columns from your chosen browse node), eBay’s category and condition are numeric IDs, Etsy needs a “who made it / when made / is supply” answer to publish, and Walmart’s official spec ships as a category-specific XLSM. So treat this as a fast way to prepare the values, then validate against the channel’s current official template / flat file, add any category-specific attributes, and run the channel’s diagnostics after uploading. It prepares the file; it does not list or publish for you.

Your catalog can be commercially sensitive (cost-adjacent pricing, SKUs, suppliers, GTINs), so where the mapping happens matters. AnyTool builds every per-channel CSV and the ZIP 100% client-side — there is no upload, no server, no CDN and no tracking, nothing is logged or stored, and the page works offline once cached. That is a real privacy win over cloud “listing tools” that upload your whole catalog to a server just to remap a few columns.

There is one canonical product model (sku, title, description, brand, category, price, sale price, currency, quantity, condition, GTIN, image URLs, color/size/material/weight, tags, product URL, vendor). Each channel defines an ordered column list plus a mapper function that turns the product into that channel’s row: Shopify slugs a Handle from the title, moves a sale price into Variant Price with the original as Compare At, and converts weight to grams; Amazon splits your description into bullet-point1..5 and turns the GTIN into product-id with a numeric product-id-type; Google writes price with the currency and derives availability from quantity; eBay sets Action=Add, trims the title to 80 characters and pipe-joins images into PicURL; Etsy strips HTML to plain text and comma-joins up to 13 tags. Edit the product once and every selected channel’s CSV updates live.

The import step parses a generic product CSV with PapaParse and maps each header to a canonical field using alias-tolerant matching — sku/id, title/name, price, sale price, qty/stock, brand, gtin/upc/ean/barcode, image/images/image_link, link/url, color, size, material, weight, tags/keywords and category all map automatically, and multiple images or tags in one cell can be comma- or pipe-separated. It tells you which columns were mapped and which were ignored. From there you tick your marketplaces, fix any per-channel required-field gaps it flags, and download each channel’s CSV or all of them as a ZIP — without ever uploading the file.