The #1 Amazon Merch Listing Hack You Need to Know (Merch Listing GPT)

About this video

He demonstrates a free custom GPT he built called Merch Listing, which he uses with a paid ChatGPT Plus account (he says he pays $20 a month). Uploading a design prompts the GPT, built around buyer intent — how a customer would search on Amazon — to generate a complete Merch by Amazon listing: brand, title, two bullet points and a product description, using target character limits he set (title 60, brand 50, bullets about 256, description 2,000), though he notes it sometimes goes over and needs manual trimming. He walks through a ‘Master of the Lawn’ lawn-mower design, then repeatedly checks the generated text for trademarks and copyright using TM Hunt and the GPT’s own trademark-check function, stressing sellers must still double-check themselves and watch the 025 clothing classification. The GPT can process several designs at once, flagging low/moderate risk (for example a Hugo Boss reference in a batch), and produce either individual listings or one general listing for all of them. He also promotes his Top Research Design Vault, which he says provides 20 low-competition niches weekly and access to over 1,300 niches.

Key takeaways

• Build listings around buyer intent: write the title, brand and bullets the way a shopper would search for the product.
• A custom GPT can draft a full Merch listing from an uploaded design, but character limits still need manual checking and editing.
• Always double-check generated text for trademarks and copyright yourself, including the 025 clothing classification.
• Batch-uploading designs lets the tool flag trademark risk per design and create individual or shared listings.
• Risk flags are guidance only — moderate-risk phrases should be reviewed before publishing, not auto-approved.

Episode details: published September 6, 2025 · 12:27 long. Summary written by Self Growth Videos from the episode itself. Business results and any income figures mentioned are RJ Martinez’s own claims, shared for education — they are not typical or guaranteed.

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