[ { "slug": "replace-ppc-saas-with-claude-and-amazon-apis", "title": "PPC Reporting with Claude for Amazon Sellers", "seller_task": "Replace a paid PPC reporting tool with a self-owned dashboard", "topic": "ppc", "topics": [ "ppc", "inventory" ], "agents": [ "Claude" ], "what_they_did": "Applied for Amazon Ads API access, had Claude write the auth script, then pulled Ads API campaign stats and every search term plus SP-API sales, traffic, FBA inventory and returns into one daily JSON bundle with plain Python. Reconciled one day against the old tool, then built a dashboard that splits sales drops into seasonal vs ad-driven, forecasts days of supply from last year's next 30 days, and flags size-return problems.", "how_it_went": "The author reports search-term coverage went from 39% (old tool) to 100% (API), the numbers reconciled to the cent against the old tool ($358.99 spend / 1,187 clicks / 50 orders / $1,104.46 sales on the test day), and cancelling the £73/mo tool saves £876 a year.", "worth_knowing": "Ads API approval took about 10 days. The author keeps a rule that Claude never changes anything in Seller Central or Ads without explicit approval (stated in a follow-up post).", "prompt": "You are my Amazon data engineer. Write a plain-Python script (standard library only) that:\n1. Uses my Amazon Ads API refresh token to pull daily Sponsored Products campaign stats and the full search-term report for the last 30 days, plus the same 30-day window last year.\n2. Uses SP-API to pull Sales & Traffic by ASIN, FBA inventory, and customer returns for the same windows.\n3. Saves everything as one JSON bundle per day.\nThen write a reconciliation check: for a date I choose, print total spend, clicks, orders and sales so I can compare them against my current PPC tool. Do not make any write calls to Amazon.", "prompt_origin": "starter", "source_url": "https://x.com/Hobbesandredbe1/status/2100975958161326141", "source_type": "x", "source_author": "deerhobbes", "source_handle": "@Hobbesandredbe1", "date": "2026-09-18", "verification": "", "added": "2026-10-28", "tools": [ "Claude" ], "url": "https://aiagentsforsellers.com/use-case/replace-ppc-saas-with-claude-and-amazon-apis/" }, { "slug": "launch-new-skus-end-to-end-with-claude", "title": "New SKU Launch (Keywords, Listing, Campaigns) with Claude for Amazon Sellers", "seller_task": "Launch new SKUs: keyword research, listing copy and campaign setup", "topic": "ppc", "topics": [ "ppc", "listings", "research" ], "agents": [ "Claude" ], "what_they_did": "The seller says Claude has run daily PPC ops for months (bids, negatives, campaign promotions, SKC creation). For a new denim outfit line, Claude pulled niche keyword and competitor data from a market research tool, wrote the listing copy from that research, and set up Auto, Discovery and Sales campaigns with niche-calibrated bids and budgets. The seller confirmed each step.", "how_it_went": "The author reports a dashboard of ACOS 25.3% (target ≤30%), TACOS 7.7% and margin 46.1% that day, with the week-old new line doing $585 in a single day while the flagship SKU was still being restocked.", "worth_knowing": "Human-in-the-loop: the author says 'I just confirmed each call.' The research tool used is not named.", "prompt": "Act as my Amazon launch operator for a new SKU. Step by step, and wait for my approval between steps:\n1. From the keyword and competitor data I paste below, pick the 15 highest-intent keywords and explain why.\n2. Write a title, 5 bullets and backend search terms using those keywords. No claims I haven't given you.\n3. Propose a launch campaign structure (Auto, a Discovery/broad campaign, and an exact-match Sales campaign) with starting bids and daily budgets calibrated to the CPCs in the data.\nProduct details: [paste]\nKeyword + competitor data: [paste]", "prompt_origin": "starter", "source_url": "https://x.com/AmazonAIOps/status/2049434001048215839", "source_type": "x", "source_author": "AI Ecom Operator", "source_handle": "@AmazonAIOps", "date": "2026-04-29", "verification": "", "added": "2026-10-28", "tools": [ "Claude" ], "url": "https://aiagentsforsellers.com/use-case/launch-new-skus-end-to-end-with-claude/" }, { "slug": "pause-and-restart-ads-around-stockouts-with-claude", "title": "Stockout Ad Pausing with Claude for Amazon Sellers", "seller_task": "Pause ads when a SKU goes out of stock and restart them before it returns", "topic": "ppc", "topics": [ "ppc", "inventory" ], "agents": [ "Claude", "Scale Insights" ], "what_they_did": "When the main SKU went out of stock, Claude paused all related campaigns the same morning. When the restock landed at FBA, Claude flagged it in the daily check and suggested restarting those exact campaigns before Amazon flipped the listing to in-stock; after approval it restarted them in Scale Insights as a batch action.", "how_it_went": "The author estimates the pause saved about $130/day in wasted spend and reports ACOS of 19.6% and 26.1% on the two days around the restock, with TACOS 7-8%. 'I clicked approve twice.'", "worth_knowing": "The author pairs this with a rule from another post: pausing isn't always right when restock is only days away (see the tiered-pause case).", "prompt": "Every morning, check my FBA inventory and my Sponsored Products campaigns.\n- If an advertised ASIN has 0 sellable units, list every campaign/ad group advertising it and recommend pausing them. Estimate the daily spend at risk.\n- If an ASIN I paused for stockout now shows inbound units received at FBA, recommend re-enabling exactly those campaigns before the listing goes back in stock.\nNever act without my explicit approval. Show me a table: ASIN, stock status, campaigns affected, recommended action.", "prompt_origin": "starter", "source_url": "https://x.com/AmazonAIOps/status/2049791103935590865", "source_type": "x", "source_author": "AI Ecom Operator", "source_handle": "@AmazonAIOps", "date": "2026-04-30", "verification": "", "added": "2026-10-28", "tools": [ "Claude", "Scale Insights" ], "url": "https://aiagentsforsellers.com/use-case/pause-and-restart-ads-around-stockouts-with-claude/" }, { "slug": "tiered-out-of-stock-ad-decisions-with-claude", "title": "Out-of-Stock PPC Decisions with Claude for Amazon Sellers", "seller_task": "Decide which out-of-stock ASINs to pause ads on", "topic": "ppc", "topics": [ "ppc", "inventory" ], "agents": [ "Claude" ], "what_they_did": "After a day of 116% ACOS ($137 spend, 4 orders) caused by a stockout, the seller asked Claude whether to pause ads on 6 OOS ASINs. Claude first said pause all 6. The seller pushed back that they were about to restock, gave Claude each ASIN's restock ETA, and Claude re-split the decision into three tiers.", "how_it_went": "Per the author, Claude's revised rule was: restock ETA ≤3 days, don't pause; 4-7 days, judgment call per ASIN; more than 7 days, pause. 'Pause all 6' became 3 different actions.", "worth_knowing": "The first answer was wrong for the account until the seller supplied context Claude didn't have (restock ETAs). The author frames it as a two-way conversation, not autopilot.", "prompt": "My ACOS spiked because some advertised ASINs are out of stock. For each ASIN below I'm giving the restock ETA in days. Recommend, per ASIN, whether to keep ads running, pause, or make a judgment call, using this logic: ETA ≤3 days keep running; 4-7 days decide per ASIN using its daily spend and ranking importance; >7 days pause. Explain each call in one line.\nASIN | daily ad spend | restock ETA (days)\n[paste]", "prompt_origin": "starter", "source_url": "https://x.com/AmazonAIOps/status/2048830826964459735", "source_type": "x", "source_author": "AI Ecom Operator", "source_handle": "@AmazonAIOps", "date": "2026-04-27", "verification": "", "added": "2026-10-28", "tools": [ "Claude" ], "url": "https://aiagentsforsellers.com/use-case/tiered-out-of-stock-ad-decisions-with-claude/" }, { "slug": "ppc-audit-prompts-with-amazon-ads-mcp", "title": "PPC Audits with the Amazon Ads MCP Server and Claude for Amazon Sellers", "seller_task": "Audit campaigns, mine search terms and clean up keywords with plain-language prompts", "topic": "ppc", "topics": [ "ppc" ], "agents": [ "Amazon Ads MCP Server", "Claude" ], "what_they_did": "Walks through connecting Claude Desktop to a hosted Amazon Ads MCP server, pointing it at a low-spend test profile, and running three audit prompts: a campaign audit, search-term mining, and keyword cleanup.", "how_it_went": "The author claims the three prompts replace '2-3 hours of senior PPC analysis' with '5 minutes of prompting.' He warns that reporting is asynchronous (10-30 minutes per report, so pull once and question the loaded dataset) and to start read-only before allowing writes with human approval.", "worth_knowing": "This is a how-to plus the author's estimate, not a measured before/after on a named account.", "prompt": "1. Campaign Audit\n\"Flag every campaign with ACoS above 35% and spend above $200 in the last 30 days. Rank them by wasted spend.\"\n\n2. Search Term Mining\n\"Pull 60 days of search-term data from every Auto campaign. Find terms with two or more orders and ACoS below 25% that aren't already running as Exact Match. Group them by parent ASIN.\"\n\n3. Keyword Cleanup\n\"Find every Exact Match keyword with more than 50 clicks in the last 30 days and zero conversions. Show me the bid, spend, and campaign.\"", "prompt_origin": "source", "source_url": "https://x.com/robingerardlobo/status/2064353623467868342", "source_type": "x", "source_author": "Robin Lobo", "source_handle": "@robingerardlobo", "date": "2026-06-09", "verification": "", "added": "2026-10-28", "tools": [ "Amazon Ads MCP Server", "Claude" ], "url": "https://aiagentsforsellers.com/use-case/ppc-audit-prompts-with-amazon-ads-mcp/" }, { "slug": "sponsored-products-campaign-creation-with-amazon-ads-mcp-server", "title": "Sponsored Products Campaign Creation with the Amazon Ads MCP Server for Amazon Sellers", "seller_task": "Create a full Sponsored Products campaign or expand to a new country from one prompt", "topic": "ppc", "topics": [ "ppc" ], "agents": [ "Amazon Ads MCP Server", "Claude", "ChatGPT", "Gemini" ], "what_they_did": "Amazon Ads opened its MCP server in beta. It connects AI agents such as Claude, ChatGPT or Gemini to Amazon Ads API functions (create, update or delete campaigns, reporting, account settings, billing) and adds pre-built multi-step tools. One tool creates an end-to-end Sponsored Products campaign (campaign, ad groups and ads) from a single prompt. Another expands existing campaigns to a new country.", "how_it_went": "Amazon says the single-prompt tool 'generates a ready-to-launch campaign that only needs review and approval.' Available globally in open beta to Amazon Ads partners with active API credentials. Amazon published no performance figures.", "worth_knowing": "Requires Amazon Ads API credentials. Amazon itself notes that connectivity alone 'doesn't guarantee reliable outcomes.'", "prompt": "Using the Amazon Ads MCP tools, draft (do not launch) a Sponsored Products campaign for ASIN [ASIN] in the [marketplace] marketplace:\n- Campaign name: [name], daily budget $[x]\n- One auto ad group and one manual exact ad group with these keywords: [list]\n- Starting bid $[x]\nShow me the full structure for review. Only create it after I reply \"approve\".", "prompt_origin": "starter", "source_url": "https://advertising.amazon.com/library/news/amazon-ads-mcp-server-open-beta", "source_type": "official", "source_author": "Amazon Ads (Paula Despins)", "source_handle": "Amazon Ads", "date": "2026-02-02", "verification": "", "added": "2026-10-28", "tools": [ "Amazon Ads MCP Server", "Claude", "ChatGPT", "Gemini" ], "url": "https://aiagentsforsellers.com/use-case/sponsored-products-campaign-creation-with-amazon-ads-mcp-server/" }, { "slug": "match-type-harvesting-with-claude-code", "title": "Match-Type Harvesting with Claude Code for Amazon Sellers", "seller_task": "Promote converting search terms from broad to phrase to exact and add negatives", "topic": "ppc", "topics": [ "ppc" ], "agents": [ "Claude Code" ], "what_they_did": "A Seller Labs video describes a solo seller who had paid an agency $37,000/yr for 3 years to run 8 broad campaigns, fired them, and rebuilt his structure with Claude Code using match-type harvesting: broad → phrase → exact single-keyword ad groups, with negatives added back on the broad. The video gives the full Claude Code prompt and explains how to apply the output by hand or via a Bulk Operations upload.", "how_it_went": "Per Seller Labs, three months later the seller had 40 campaigns and his ROAS had doubled. The seller isn't named and no account data is shown.", "worth_knowing": "Seller Labs sells an Amazon MCP server, and the video is narrated by an AI avatar. The prompt is designed so no promotion or negative goes live without approval.", "prompt": "Build me a match type harvester for my Amazon ad account. Ask me before any code: my target ACoS; my minimum spend floor (below this, ignore noise); my minimum conversion floor (below this, a term doesn't qualify for promotion); my conversion attribution window (7, 14, or 30 days); and the percentage above current CPC I want to bid when promoting a term to a tighter match type. Don't assume defaults.\n\nAsk me to paste my Sponsored Products search term report CSV (Campaign Manager reports tab, last 30 days) or confirm I have live Amazon Ads data wired through an MCP. If I paste the CSV, treat the first row as headers and confirm which columns you're mapping to spend, clicks, orders inside the attribution window, ACoS, campaign, ad group, customer search term, match type of the targeting keyword, CPC, and advertised ASIN.\n\nClassify every customer search term. A term qualifies for promotion to phrase match if its current targeting keyword is broad match, has spend above my floor, has orders above my floor inside my attribution window, and has ACoS at or below my target. A term qualifies for promotion to exact match if its current targeting keyword is phrase match and meets the same volume and ACoS criteria. Anything draining spend with zero orders gets flagged for negative keyword addition to the original broad keyword.\n\nOutput one decision table, one row per customer search term. Columns: current match type, recommended match type, advertised ASIN, spend, clicks, orders, ACoS, current CPC, recommended starting bid (current CPC plus my premium), recommended negative keyword adds for the original broad keyword, one-line reason. Then bundle promotions by advertised ASIN: for each ASIN with three or more promotions, output the suggested ad group structure (one exact match ad group per SKAG, one phrase match ad group containing the phrase-tier terms, plus the negative keyword list for the original broad keyword). If I ask for it, also produce the same structure formatted for the Amazon Sponsored Products bulk operations template (entity type, operation = create, campaign name, ad group name, match type, keyword text, bid) so I can upload it directly. No auto action. Every promotion and every negative keyword add waits for my approval.", "prompt_origin": "source", "source_url": "https://www.youtube.com/watch?v=-Kll0Yvd5i8", "source_type": "youtube", "source_author": "Seller Labs (Nhan Dinh)", "source_handle": "Seller Labs", "date": "2026-05-14", "verification": "", "added": "2026-10-28", "tools": [ "Claude Code" ], "url": "https://aiagentsforsellers.com/use-case/match-type-harvesting-with-claude-code/" }, { "slug": "search-term-report-waste-analysis-with-claude", "title": "Wasted Ad Spend Analysis with Claude for Amazon Sellers", "seller_task": "Find wasted spend, negatives and scale-ready keywords in a search term report", "topic": "ppc", "topics": [ "ppc" ], "agents": [ "Claude" ], "what_they_did": "The author uploads a 7, 30 or 60-day search term report into Claude, which identifies zero-conversion keywords, partially working terms and scale-ready winners, ranks opportunities by recoverable budget, and says what to negate and what to monitor.", "how_it_went": "The author says work that used to take 'hours of pivot tables' is now done 'in 10 minutes.' No spend figures are given.", "worth_knowing": "The post closes with a 'comment CLAUDE' lead magnet. The author mainly runs Amazon brands on TikTok Shop and IG.", "prompt": "I'm uploading my Amazon Sponsored Products search term report (last 30 days). Analyze it and give me three lists:\n1. Negate now: search terms with spend above $[x] and zero orders. Include spend wasted.\n2. Watch: terms with some orders but ACoS above my target of [x]%.\n3. Scale: terms with ACoS below [x]% and at least 2 orders that aren't yet targeted as exact match.\nRank each list by recoverable or growable budget, and total the wasted spend at the top.", "prompt_origin": "starter", "source_url": "https://x.com/maverickecom/status/2036457923845513343", "source_type": "x", "source_author": "Noah Frydberg", "source_handle": "@maverickecom", "date": "2026-03-24", "verification": "", "added": "2026-10-28", "tools": [ "Claude" ], "url": "https://aiagentsforsellers.com/use-case/search-term-report-waste-analysis-with-claude/" }, { "slug": "multi-agent-ppc-system-with-claude-code", "title": "Multi-Agent PPC Management with Claude Code for Amazon Sellers", "seller_task": "Run separate bidding, inventory and strategy agents on an Amazon ad account", "topic": "ppc", "topics": [ "ppc", "inventory" ], "agents": [ "Claude Code", "Codex" ], "what_they_did": "A Reddit seller describes running a multi-agent setup on their own account for months, with separate agents for bidding, inventory and strategy and rules-based conflict priority between them. Data comes in through SP-API and the Ads API, with search-term and campaign data fed to the agents as context.", "how_it_went": "They report about a week of setup ('3 hrs per/day of sleep and a month of Claude Max') and had to work around SQP and search term report API timeouts. Their worst mistakes came from an agent reacting to one bad day of data and from two agents making conflicting moves. Manual tweaks made while the system was running 'tanked my entire system' and cost margin that month, which they say they're making back.", "worth_knowing": "Their advice: start in suggestion mode, keep the approve/dismiss history to tune the rules, and automate negative-keyword harvesting before bid changes. Another commenter in the thread warns that beyond PPC you need a proper data warehouse.", "prompt": "You are the strategy agent in my Amazon PPC system. You do NOT execute changes. Each day:\n1. Read the bidding agent's proposed bid changes and the inventory agent's stock alerts (pasted below).\n2. Resolve conflicts with this priority: inventory risk > profitability > growth. Example: never approve a bid increase on an ASIN with < 14 days of stock.\n3. Ignore any recommendation driven by a single day of data unless it's confirmed by the 7-day trend.\n4. Output an approval queue for me: change, agent, reason, conflict resolved (Y/N).\nBidding agent proposals: [paste]\nInventory agent alerts: [paste]", "prompt_origin": "starter", "source_url": "https://www.reddit.com/r/FulfillmentByAmazon/comments/1v93na7/codex_vs_claude_code_for_ppc/", "source_type": "reddit", "source_author": "u/ScribbleSpark", "source_handle": "u/ScribbleSpark", "date": "2026-07-28", "verification": "Reddit blocks automated fetching; text verified from the search-index copy of this thread on 2026-10-28. Open the link to read the full thread.", "added": "2026-10-28", "tools": [ "Claude Code", "Codex" ], "url": "https://aiagentsforsellers.com/use-case/multi-agent-ppc-system-with-claude-code/" }, { "slug": "listing-copy-chatgpt-vs-claude-vs-accio-cost", "title": "Listing Copy Cost Comparison: ChatGPT, Claude and Accio for Amazon Sellers", "seller_task": "Write a full listing (title, bullets, description, backend terms, price analysis)", "topic": "listings", "topics": [ "listings" ], "agents": [ "ChatGPT", "Claude", "Accio" ], "what_they_did": "The author gave three AI agents the same brief for a 500ml vacuum-insulated stainless steel bottle with a temperature display for Amazon US: a search-optimized title, 5 bullets, a description, backend search terms and a competitive price analysis. He tracked the token cost of each.", "how_it_went": "He reports ChatGPT (GPT-5.4) was 'solid… accurate' at about $0.09/task, Claude (Opus 4.6) had 'slightly more detailed bullet points' and better competitive analysis at about $0.09, and Accio had the 'same quality' with built-in Amazon formatting at about $0.03.", "worth_knowing": "The post reads as promotion for Accio (Alibaba) and quotes its president. Quality judgments are the author's.", "prompt": "Write an Amazon US listing for this product: [product facts].\nDeliver: (1) a title under 200 characters optimized for search, (2) 5 bullet points, (3) a full product description, (4) backend search terms under 249 bytes with no repeats from the title, (5) a short competitive price analysis based on these similar listings: [paste titles + prices]. Use only the facts I gave you.", "prompt_origin": "starter", "source_url": "https://x.com/heynavtoor/status/2098026439824347426", "source_type": "x", "source_author": "Nav Toor", "source_handle": "@heynavtoor", "date": "2026-09-10", "verification": "", "added": "2026-10-28", "tools": [ "ChatGPT", "Claude", "Accio" ], "url": "https://aiagentsforsellers.com/use-case/listing-copy-chatgpt-vs-claude-vs-accio-cost/" }, { "slug": "listing-optimization-with-amazon-enhance-my-listing", "title": "Listing Optimization with Amazon Enhance My Listing for Amazon Sellers", "seller_task": "Generate and refresh titles, attributes and descriptions across a catalog", "topic": "listings", "topics": [ "listings" ], "agents": [ "Amazon Enhance My Listing", "Amazon Gen AI listing tools" ], "what_they_did": "Amazon's built-in gen AI listing tools create listings from a few words, an image or a website URL, and Enhance My Listing suggests updates to live titles, attributes and descriptions that sellers can accept, edit or decline. C&M Personal Gifts used the tools on its personalized-glassware catalog.", "how_it_went": "C&M's founders say they 'created about 300 listings with the tool, and they're more discoverable and shoppable than ever.' In a separate quote, the co-owner says they redid the entire catalog before Christmas and 'had a great holiday season.' Amazon reports 900,000+ sellers have used the tools, accepting the output with little or no edits about 90% of the time.", "worth_knowing": "All figures and quotes come from Amazon. TechCrunch notes the 90% acceptance rate is 'not necessarily an endorsement of the tools' accuracy.' Access is in Seller Central: Manage All Inventory → Edit Listing → Enhance My Listing.", "prompt": "(Use inside Seller Central.) Before accepting Enhance My Listing suggestions, paste the old and suggested versions into any AI assistant and ask:\n\"Compare my current Amazon listing with Amazon's suggested version. List every factual claim in the suggestion and mark any I did not provide or can't verify. Then tell me which changes improve keyword coverage and which only change wording.\"", "prompt_origin": "starter", "source_url": "https://www.aboutamazon.com/news/innovation-at-amazon/amazon-generative-ai-seller-growth-shopping-experience", "source_type": "official", "source_author": "Amazon", "source_handle": "Amazon", "date": "2025-05-08", "verification": "", "added": "2026-10-28", "tools": [ "Amazon's built-in Gen AI tools" ], "url": "https://aiagentsforsellers.com/use-case/listing-optimization-with-amazon-enhance-my-listing/" }, { "slug": "keyword-gap-discovery-with-claude-skills", "title": "Keyword Gap Discovery with Claude for Amazon Sellers", "seller_task": "Find keyword gaps competitors aren't covering", "topic": "listings", "topics": [ "listings", "research" ], "agents": [ "Claude" ], "what_they_did": "Using two Claude skills he wrote (organic-traffic-scout and amazon-keyword-listing), the seller reverse-looked-up three competitors' catalogues and the keywords they rank and bid on. He found compound '-cessories' terms carrying organic traffic that no top seller was bidding on, and moved the product to a less competitive category where buyers were already searching (Toys & Games instead of Jewelry).", "how_it_went": "He reports sessions went from 558 to 981, units from 52 to 131, and conversion from 9.32% to 13.35%.", "worth_knowing": "The category change was part of the result, so the gain isn't from keywords alone. Check category rules before recategorizing.", "prompt": "Here are the keywords my top 3 competitors rank for organically and the ones they bid on: [paste both lists, with search volume].\n1. List high-volume terms with real organic traffic that none of them bid on.\n2. Group those terms by shopper language (aesthetic/style words vs product nouns).\n3. Tell me which of these terms my current title, bullets and backend terms miss, and draft a revised title and backend terms that cover the top gaps.", "prompt_origin": "starter", "source_url": "https://x.com/Hobbesandredbe1/status/2102051728556224769", "source_type": "x", "source_author": "deerhobbes", "source_handle": "@Hobbesandredbe1", "date": "2026-09-21", "verification": "", "added": "2026-10-28", "tools": [ "Claude" ], "url": "https://aiagentsforsellers.com/use-case/keyword-gap-discovery-with-claude-skills/" }, { "slug": "listing-and-a-plus-edits-via-sp-api-with-claude", "title": "Listing, Variation and A+ Edits via SP-API with Claude for Amazon Sellers", "seller_task": "Read account data and write listing, variation, A+ and ad changes through Amazon's APIs", "topic": "listings", "topics": [ "listings", "a-plus", "ppc", "inventory" ], "agents": [ "Claude" ], "what_they_did": "With Claude wired to SP-API and the Ads API on his own account and no third-party tool, the seller reads sales, sessions, CVR, inventory, returns with comments, every ad search term, SQP and finances. Claude writes changes to titles, bullets, backend keywords and size attributes, created new variations (6 sizes and 2 colors on existing parents), updates A+ modules, changes budgets and negatives, and creates campaigns. Every write is logged and verified by reading it back.", "how_it_went": "He reports Claude rebuilds a full dashboard daily in about 20 minutes, published to a private page he can open on his phone, with 100% search-term coverage where his old tool showed 39%.", "worth_knowing": "His rule is that Claude never changes anything in Seller Central or Ads without his explicit yes.", "prompt": "You have read/write access to my Amazon SP-API and Ads API. Rules: never write anything without my explicit \"yes\"; after every write, read the value back and confirm it changed; log every action.\nTask: for parent ASIN [ASIN], pull the current title, bullets, backend keywords and size attributes for each child. Propose fixes for any missing or inconsistent attributes and any keyword I'm not covering from this list: [paste]. Show the diff before writing.", "prompt_origin": "starter", "source_url": "https://x.com/Hobbesandredbe1/status/2101975149192822912", "source_type": "x", "source_author": "deerhobbes", "source_handle": "@Hobbesandredbe1", "date": "2026-09-21", "verification": "", "added": "2026-10-28", "tools": [ "Claude" ], "url": "https://aiagentsforsellers.com/use-case/listing-and-a-plus-edits-via-sp-api-with-claude/" }, { "slug": "rufus-ready-beauty-listing-with-claude", "title": "Rufus-Ready Listing Content with Claude for Amazon Sellers", "seller_task": "Rework a beauty listing for clarity and intent so it performs with Rufus", "topic": "listings", "topics": [ "listings" ], "agents": [ "Claude", "Amazon Rufus" ], "what_they_did": "An Amazon consultant reworked a beauty brand's listing (solid reviews, flat growth) around clear benefits and ingredients, education over selling, intent-driven keywords and better visuals, using Claude for 'faster insights.'", "how_it_went": "He reports +15% conversion, higher organic rankings and more efficient ads.", "worth_knowing": "Claude's exact role isn't detailed, and the gains came from the whole content rework.", "prompt": "Rewrite my Amazon beauty listing so an AI shopping assistant like Rufus can answer shopper questions from it. For each bullet: state the benefit, the ingredient or feature that delivers it, who it's for, and how to use it. Remove vague superlatives. Then list 10 questions a shopper might ask Rufus about this product and confirm each one is answered in the listing. Listing: [paste]. Ingredients and claims I can support: [paste].", "prompt_origin": "starter", "source_url": "https://x.com/AK_Amazon/status/2069081966297702581", "source_type": "x", "source_author": "Ananth Kuchimanchi", "source_handle": "@AK_Amazon", "date": "2026-06-22", "verification": "", "added": "2026-10-28", "tools": [ "Claude", "Amazon's built-in Gen AI tools" ], "url": "https://aiagentsforsellers.com/use-case/rufus-ready-beauty-listing-with-claude/" }, { "slug": "product-descriptions-chatgpt-drafts-claude-rewrites", "title": "Product Descriptions with ChatGPT and Claude for Amazon Sellers", "seller_task": "Draft listing copy and rework A+ angles", "topic": "listings", "topics": [ "listings", "a-plus" ], "agents": [ "ChatGPT", "Claude" ], "what_they_did": "A Reddit seller uses ChatGPT for listing drafts and Claude for cleaner rewrites. For A+ content they study top competitor listings first, then rewrite the angles instead of generating from scratch, and sometimes run A+ layouts through Runable.", "how_it_went": "They didn't see a huge difference between the tools: 'prompt quality mattered way more.'", "worth_knowing": "This is one practitioner's comment, with no metrics.", "prompt": "Here are the A+ modules and bullets from the top 3 competitor listings for [product]: [paste]. Summarize the angle each one leads with. Then propose 3 different angles for MY product that none of them use, based on these facts: [paste]. Draft A+ module headlines and body text for the strongest angle.", "prompt_origin": "starter", "source_url": "https://www.reddit.com/r/FulfillmentByAmazon/comments/1spqah8/best_ai_for_product_description/", "source_type": "reddit", "source_author": "u/Independent-Ant-7230", "source_handle": "u/Independent-Ant-7230", "date": "2026-04-20", "verification": "Reddit blocks automated fetching; text verified from the search-index copy of this thread on 2026-10-28. Open the link to read the full thread.", "added": "2026-10-28", "tools": [ "ChatGPT", "Claude" ], "url": "https://aiagentsforsellers.com/use-case/product-descriptions-chatgpt-drafts-claude-rewrites/" }, { "slug": "listing-translation-with-chatgpt", "title": "Listing Translation with ChatGPT for Amazon Sellers", "seller_task": "Translate listings for other marketplaces with product context", "topic": "listings", "topics": [ "listings" ], "agents": [ "ChatGPT" ], "what_they_did": "An Amazon agency uses ChatGPT for translations that include product details and context instead of relying on Amazon's auto-translations.", "how_it_went": "The agency says the AI translations are 'way better than Amazon's auto-translations, especially when tone or cultural nuances matter.'", "worth_knowing": "From a longer agency comment listing nine ways they use ChatGPT.", "prompt": "Translate this Amazon listing into [language] for the Amazon [marketplace] store. Context: [product, target customer, brand tone]. Don't translate word for word. Use the terms local shoppers actually search for, adapt units and sizes, and keep the title under the marketplace's character limit. After the translation, list any phrases you localized and why. Listing: [paste]", "prompt_origin": "starter", "source_url": "https://www.reddit.com/r/FulfillmentByAmazon/comments/1gv72zc/how_are_you_using_ai_chatgpt_to_make_selling_on/", "source_type": "reddit", "source_author": "u/HappyKapow", "source_handle": "u/HappyKapow", "date": "2024-11-19", "verification": "Reddit blocks automated fetching; text verified from the search-index copy of this thread on 2026-10-28. Open the link to read the full thread.", "added": "2026-10-28", "tools": [ "ChatGPT" ], "url": "https://aiagentsforsellers.com/use-case/listing-translation-with-chatgpt/" }, { "slug": "run-amazon-business-from-claude-with-selling-partner-plugin", "title": "Seller Central Tasks with the Amazon Selling Partner Plugin for Claude", "seller_task": "Check sales, inventory and listings and approve listing or price changes from Claude", "topic": "listings", "topics": [ "listings", "inventory" ], "agents": [ "Amazon Selling Partner plugin", "Claude", "Amazon Quick", "Amazon Seller Assistant" ], "what_they_did": "Amazon's Selling Partner plugin (beta in Claude, live in Amazon Quick, U.S. stores) connects a seller's listings, real-time metrics, inventory and sales analytics to the AI tool. The AI can act on the account, such as updating listings or prices, with human approval and full audit trails. Amazon says connecting Claude takes about 60 seconds with no coding.", "how_it_went": "Beta tester James Oliver (Atlas Bar) is quoted: 'I used to spend hours manually connecting my Amazon data to AI tools. Now it's all built in… What took an afternoon now happens instantly.' Amazon says Seller Assistant has hundreds of thousands of active users, who accept its recommendations over 90% of the time.", "worth_knowing": "Claude access opens in waves from a waitlist. A Seller Labs write-up notes the announced scope covers listings, inventory, metrics and sales analytics, not running ad campaigns.", "prompt": "Using my Amazon Selling Partner data: show my top 10 ASINs by sales for the last 30 days with units, conversion rate and current FBA inventory. For the one with the lowest conversion rate, review the title and bullets and propose edits. Don't change anything until I approve each edit.", "prompt_origin": "starter", "source_url": "https://www.aboutamazon.com/news/innovation-at-amazon/seller-assistant-plugin-amazon-quick-claude", "source_type": "official", "source_author": "Amazon (Mary Beth Westmoreland)", "source_handle": "Amazon", "date": "2026-09-23", "verification": "", "added": "2026-10-28", "tools": [ "Amazon Seller Assistant", "Claude" ], "url": "https://aiagentsforsellers.com/use-case/run-amazon-business-from-claude-with-selling-partner-plugin/" }, { "slug": "listing-image-set-with-chatgpt-images-2", "title": "Listing Image Sets with ChatGPT Images 2.0 for Amazon Sellers", "seller_task": "Generate a full set of Amazon listing images from one product photo", "topic": "creative", "topics": [ "creative" ], "agents": [ "ChatGPT Images 2.0" ], "what_they_did": "Fed ChatGPT Images 2.0 one product photo of a Barebells bar as reference and used 6 prompts covering Amazon listing image formats, with dense feature callouts, ingredient percentages, comparison tables and before/after labels.", "how_it_went": "He reports a full six-image listing set 'in 15 minutes,' with text rendering correctly and consistent brand colors and packaging.", "worth_knowing": "This was a demo on a brand he doesn't appear to run. The 6 prompts are gated behind a comment-for-DM offer, so they aren't reproduced here. Check Amazon image rules before uploading (the main image must be on a pure white background).", "prompt": "Using the attached product photo as the exact reference for packaging, colors and logo, create image 2 of an Amazon listing set: a 1:1 infographic with the product on the right and 3 benefit callouts on the left, using these claims only: [claim 1], [claim 2], [claim 3]. Clean sans-serif type, brand colors [hex], white-to-light-gray background. Keep every word spelled exactly as given.", "prompt_origin": "starter", "source_url": "https://x.com/mikefutia/status/2047344262275387486", "source_type": "x", "source_author": "Mike Futia", "source_handle": "@mikefutia", "date": "2026-04-23", "verification": "", "added": "2026-10-28", "tools": [ "ChatGPT" ], "url": "https://aiagentsforsellers.com/use-case/listing-image-set-with-chatgpt-images-2/" }, { "slug": "listing-image-generator-with-claude-code-and-nano-banana-pro", "title": "Listing Image Generator App with Claude Code and Nano Banana Pro for Amazon Sellers", "seller_task": "Build an internal app that writes image specs and generates infographic listing images", "topic": "creative", "topics": [ "creative" ], "agents": [ "Claude Code", "Gemini 3 Pro", "Nano Banana Pro" ], "what_they_did": "A seller who previously paid an Upwork designer $35 per listing image had Claude Code (Opus 4.5) build a local web app. It sends product info to Gemini 3 Pro to propose six image specs and a style guide, generates four style options with Nano Banana Pro, then generates the remaining images in the chosen style. He later added single-image generation, image-to-image restyling for A/B tests, and whitebox photo generation.", "how_it_went": "He says that apart from one bug it 'just worked.' Claude had hallucinated the Gemini model names and fixed them after being shown the terminal logs. He calls the output 'great' in the grand scheme of Amazon images, says he'll judge styles by Amazon A/B test results, and upgraded to Claude Max to keep building.", "worth_knowing": "Early generations reused the same photos, which he fixed by raising the upload limit to 20.", "prompt": "I would like to build an application that generates images for Amazon listings. These will be infographic-style images that have photos of the relevant product along with informational text and/or graphics.\n\nThe application should work as follows:\n\n1. User fills out a web form with the following info:\n- Text entry: Describe the product.\n- Text entry: How would you describe the desired brand look and feel?\n- Text entry: What are the main pieces of information you wish to convey about the product?\n- File picker: Select and upload up to 6 images. You should be able to select files at which point previews of them are shown. You can add more and delete existing ones.\n\n2. The app then sends all of the text to the Gemini API (mode: Gemini 3 Pro), which comes back with a written description of six different images that it proposes creating. The written descriptions should include any text (with high-level formatting notes about each piece of text - e.g. Title, subtitle, bullets). They should also describe any visuals that must be included in each image. Lastly, it should have a separate section that shows the proposed look and feel, including colors, font styles, and any illustration/iconography styles. Each of the image descriptions should be in its own text box, as should the look and feel info, and the user should be able to edit any of them.\n\n3. When the user is satisfied with everything, he clicks a \"Generate Style Examples\" image. This should create four versions of the first image in the list. These should each be created with independent calls to the Nano Banana Pro API. [...] When use this style is clicked, the app should make calls to Nano Banana Pro to generate the other five images. Those calls should include the selected image with instructions to NBP to match the style. Each generated image should have a Reroll and a Download button next to it.\n\nOther context:\n1. This is going to run locally on my machine so no concerns about auth, security, etc.\n2. All images generated should be in 2k resolution and 1:1 aspect ratio", "prompt_origin": "source", "source_url": "https://theautomatedoperator.substack.com/p/opus-45-codes-gemini-3-writes-the", "source_type": "blog", "source_author": "Alex Willen (The Automated Operator)", "source_handle": "@Willenation", "date": "2026-01-15", "verification": "", "added": "2026-10-28", "tools": [ "Claude Code", "Gemini" ], "url": "https://aiagentsforsellers.com/use-case/listing-image-generator-with-claude-code-and-nano-banana-pro/" }, { "slug": "main-image-ctr-with-claude-for-chrome-and-chatgpt-image-2", "title": "Main Image CTR Testing with Claude for Chrome and ChatGPT Image 2 for Amazon Sellers", "seller_task": "Research competitor main images and iterate new main-image concepts", "topic": "creative", "topics": [ "creative", "research" ], "agents": [ "Claude for Chrome", "ChatGPT Image 2" ], "what_they_did": "Step 1: Claude for Chrome visits competitor listings for the main keyword, analyzes their images and builds a comparison chart of what's visually prominent. Step 2: ChatGPT Image 2 generates main-image variations one change at a time (for example, adding a serving-count badge, then a certification callout). The best 2-3 of 8-10 concepts are tested with Manage Your Experiments.", "how_it_went": "The author says the research step now takes under 20 minutes and that clients running the process 'have seen 15-80% CTR improvements on their main images,' with the range depending on how under-optimized the starting image was.", "worth_knowing": "Results are aggregated across unnamed clients. Testing happens in Amazon's own Manage Your Experiments.", "prompt": "add a badge highlighting serving count in the style of this reference image, maintaining consistent branding", "prompt_origin": "source", "source_url": "https://x.com/Evan_Swanson_/status/2064048564683378969", "source_type": "x", "source_author": "Evan Swanson", "source_handle": "@Evan_Swanson_", "date": "2026-06-08", "verification": "", "added": "2026-10-28", "tools": [ "Claude", "ChatGPT" ], "url": "https://aiagentsforsellers.com/use-case/main-image-ctr-with-claude-for-chrome-and-chatgpt-image-2/" }, { "slug": "amazon-product-video-with-claude-cowork-and-google-flow", "title": "Product Video from Listing Images with Claude Cowork and Google Flow for Amazon Sellers", "seller_task": "Create a product video from an existing Amazon listing", "topic": "creative", "topics": [ "creative" ], "agents": [ "Claude Cowork", "Google Flow" ], "what_they_did": "Pasted one prompt into Claude Cowork with a link to an Amazon listing. Claude used only the listing's images and produced a full product video with Google Flow (Google's AI video tool).", "how_it_went": "He calls it 'round 1. No revisions. No feedback.' and 'No edits.'", "worth_knowing": "The prompt itself isn't shared in the post.", "prompt": "Open this Amazon listing: [URL]. Using ONLY the product images on that listing, plan a 20-30 second product video: shot list (6-8 shots), which listing image each shot starts from, the camera motion, and an on-screen text line per shot based only on claims visible in the listing. Then write the Google Flow prompt for each shot.", "prompt_origin": "starter", "source_url": "https://x.com/sumnerhobart/status/2112527780621529133", "source_type": "x", "source_author": "Sumner", "source_handle": "@sumnerhobart", "date": "2026-10-20", "verification": "", "added": "2026-10-28", "tools": [ "Claude", "Google Flow" ], "url": "https://aiagentsforsellers.com/use-case/amazon-product-video-with-claude-cowork-and-google-flow/" }, { "slug": "premium-a-plus-banners-with-codex", "title": "Premium A+ Content Banners with Codex for Amazon Sellers", "seller_task": "Turn existing designs into Premium A+ banners", "topic": "a-plus", "topics": [ "a-plus", "creative" ], "agents": [ "Codex" ], "what_they_did": "The seller, who says Claude Code runs his Amazon operations, uses Codex for images and A+ design. One line, 'make the purple and blue ones into Amazon Premium A+', turned his existing purple and blue designs into Premium A+ banners.", "how_it_went": "He reports four banners came back with the same type and palette, product shots dropped into built scenes, and detail crops labelled. 'Nobody touched Photoshop.'", "worth_knowing": "His split: 'Claude Code for anything with an API behind it. Codex for anything that has to look right.'", "prompt": "make the purple and blue ones into Amazon Premium A+", "prompt_origin": "source", "source_url": "https://x.com/Hobbesandredbe1/status/2101997671699382413", "source_type": "x", "source_author": "deerhobbes", "source_handle": "@Hobbesandredbe1", "date": "2026-09-21", "verification": "", "added": "2026-10-28", "tools": [ "Codex" ], "url": "https://aiagentsforsellers.com/use-case/premium-a-plus-banners-with-codex/" }, { "slug": "competitor-review-mining-and-ad-experiments-with-claude", "title": "Competitor Review Mining with Claude for Amazon Sellers", "seller_task": "Classify competitor reviews into patterns and turn them into tested ad angles", "topic": "reviews", "topics": [ "reviews", "creative" ], "agents": [ "Claude" ], "what_they_did": "Upload public Amazon reviews of competing products to Claude with a structured prompt that classifies each review, builds a complaint-pattern table with counts and supporting review IDs, compares negative and positive reviews, and drafts 12 ad experiments.", "how_it_went": "The post shares the prompt only and reports no results.", "worth_knowing": "The prompt explicitly forbids invented facts, frequencies or quotes and requires review IDs for every pattern.", "prompt": "You are a senior ecommerce customer-research and creative strategist.\n\nI am giving you public Amazon reviews from competing products.\n\nAnalyze only the supplied reviews. Do not invent facts, product capabilities, customer quotes, frequencies, or conclusions.\n\nFirst, classify each review as:\n1. Product defect\n2. Expectation gap\n3. Setup or usability friction\n4. Missing feature\n5. Wrong customer or use case\n6. Shipping, seller, or delivery noise\n7. Isolated complaint with insufficient evidence\n\nThen create a table showing:\n• Complaint pattern\n• Number of reviews mentioning it\n• Situation in which it happens\n• Consequence for the customer\n• Outcome the customer wanted\n• Objection our advertising must answer\n• Proof required before making the claim\n• Review IDs supporting the pattern\n\nDo not treat one-star reviewers as representative of the entire market. Identify repeated patterns separately from isolated complaints.\n\nNext, compare the negative reviews with any positive reviews I provide. Find where customers disagree and explain what product type, expectation, or use case may cause the difference.\n\nFinally, create 12 original ad experiments for my product.\n\nFor every experiment, provide:\n• Hook\n• Body copy\n• Visual concept\n• Customer problem\n• Promise\n• Proof needed\n• Awareness stage\n• Hypothesis being tested\n• Result we should measure\n\nDo not copy review wording, use a reviewer as a testimonial, attack a named competitor, or claim our product solves a problem unless my product information supports it.", "prompt_origin": "source", "source_url": "https://x.com/wetracked/status/2093263244614144425", "source_type": "x", "source_author": "wetracked.io", "source_handle": "@wetracked", "date": "2026-08-28", "verification": "", "added": "2026-10-28", "tools": [ "Claude" ], "url": "https://aiagentsforsellers.com/use-case/competitor-review-mining-and-ad-experiments-with-claude/" }, { "slug": "review-analysis-to-lift-conversion-with-claude", "title": "Review Analysis for Listing Conversion with Claude for Amazon Sellers", "seller_task": "Find what buyers love in reviews and rebuild listing images around it", "topic": "reviews", "topics": [ "reviews", "creative", "listings" ], "agents": [ "Claude", "Data Dive" ], "what_they_did": "Four steps: (1) compare the ASIN's conversion rate with the category using the Search Query Performance report; (2) paste 5-10 pages of reviews into Claude and ask for the top five reasons people love the product, ranked by frequency; (3) review competitor images in Data Dive; (4) rebuild the listing images around those reasons and A/B test them.", "how_it_went": "The agency reports a client's conversion rate rose 19.6%, from 25.86% to 30.93%, after the creative update.", "worth_knowing": "Claude did the review analysis. The lift came from the new images built from it.", "prompt": "please analyze the attached reviews and write the top five reasons why people love this product based on the order of frequency these reasons appear within the reviews", "prompt_origin": "source", "source_url": "https://www.youtube.com/watch?v=eYD_tlaPQuU", "source_type": "youtube", "source_author": "George Schwartz (Extension Ecom)", "source_handle": "@George_AMZN", "date": "2025-05-09", "verification": "", "added": "2026-10-28", "tools": [ "Claude", "Data Dive" ], "url": "https://aiagentsforsellers.com/use-case/review-analysis-to-lift-conversion-with-claude/" }, { "slug": "one-star-review-pattern-before-ordering-inventory-with-claude-code", "title": "One-Star Review Pattern Detection with Claude Code for Amazon Sellers", "seller_task": "Read a best seller's 1-star reviews before committing to inventory", "topic": "reviews", "topics": [ "reviews", "research" ], "agents": [ "Claude Code" ], "what_they_did": "Before ordering inventory, the author asked Claude Code to read the 1-star reviews of a best-selling product.", "how_it_went": "He reports Claude found the pattern: 'The product dies around day 30. The return window closes at day 30.' The run cost $0.0477 with pay-as-you-go API usage.", "worth_knowing": "The headline claim that this replaced a $359/mo seller suite is the author's framing.", "prompt": "Here are all the 1-star and 2-star reviews for [ASIN] with their dates and any 'used for X days' mentions: [paste]. Find failure patterns, especially WHEN the product fails (days or weeks of use) and HOW. Count how many reviews support each pattern and quote short supporting snippets with review dates. Tell me whether the timing lines up with Amazon's return window.", "prompt_origin": "starter", "source_url": "https://x.com/Jasperli0122/status/2086952207849787877", "source_type": "x", "source_author": "Jasper Li", "source_handle": "@Jasperli0122", "date": "2026-08-10", "verification": "", "added": "2026-10-28", "tools": [ "Claude Code" ], "url": "https://aiagentsforsellers.com/use-case/one-star-review-pattern-before-ordering-inventory-with-claude-code/" }, { "slug": "review-sentiment-analysis-with-jungle-scout-cobalt-mcp", "title": "Review Sentiment Analysis with Jungle Scout (Ask Cobalt / MCP) for Amazon Sellers", "seller_task": "Group review text into complaint and praise themes and compare with competitors", "topic": "reviews", "topics": [ "reviews", "research" ], "agents": [ "Jungle Scout Cobalt", "Jungle Scout MCP", "Claude", "ChatGPT" ], "what_they_did": "Jungle Scout's Review Sentiment use case reads Amazon review text at scale and groups it into recurring complaints, praised features and feature requests. It can compare against competitors and before/after a listing change. It's available in Ask Cobalt and through the Jungle Scout MCP in Claude and ChatGPT.", "how_it_went": "This is a vendor feature page. It gives example questions but no customer results.", "worth_knowing": "Cobalt is aimed at established brands and agencies.", "prompt": "What features do customers wish this product had?\nCompare review sentiment for our product against the top 3 competitors — what do they praise that we get criticized for?\nHas a specific complaint theme increased in frequency since our last listing update, suggesting the change backfired?\nWhich negative review themes correlate most strongly with declining conversion rate, versus ones that are just noise?", "prompt_origin": "source", "source_url": "https://www.junglescout.com/ai-use-cases/review-sentiment/", "source_type": "docs", "source_author": "Jungle Scout", "source_handle": "Jungle Scout", "date": "", "verification": "", "added": "2026-10-28", "tools": [ "Jungle Scout", "Claude", "ChatGPT" ], "url": "https://aiagentsforsellers.com/use-case/review-sentiment-analysis-with-jungle-scout-cobalt-mcp/" }, { "slug": "rating-drop-alerts-with-amazon-seller-assistant-workflows", "title": "Rating-Drop Alerts with Amazon Seller Assistant Workflows for Amazon Sellers", "seller_task": "Monitor top products' star ratings around the clock and draft a response plan", "topic": "reviews", "topics": [ "reviews", "inventory" ], "agents": [ "Amazon Seller Assistant" ], "what_they_did": "Seller Assistant workflows are plain-language automations that keep running when the seller isn't logged in. Amazon's example: 'Alert me if any of my top 10 products drop below a 4-star rating and draft a response plan.' For each workflow, the seller chooses whether it only recommends or takes action with approval, and every action is logged.", "how_it_went": "Amazon announced the feature with prebuilt templates (account health, order summaries, pricing trends, expansion opportunities). It has published no outcome data for workflows.", "worth_knowing": "A widely shared critique: a 90% recommendation acceptance rate measures agreement, not outcomes.", "prompt": "Alert me if any of my top 10 products drop below a 4-star rating and draft a response plan.", "prompt_origin": "source", "source_url": "https://www.aboutamazon.com/news/innovation-at-amazon/seller-assistant-plugin-amazon-quick-claude", "source_type": "official", "source_author": "Amazon (Mary Beth Westmoreland)", "source_handle": "Amazon", "date": "2026-09-23", "verification": "", "added": "2026-10-28", "tools": [ "Amazon Seller Assistant" ], "url": "https://aiagentsforsellers.com/use-case/rating-drop-alerts-with-amazon-seller-assistant-workflows/" }, { "slug": "returns-reimbursement-case-with-chatgpt", "title": "Return Reimbursement Claims with ChatGPT for Amazon Sellers", "seller_task": "Match returns to orders and write reimbursement cases and appeals", "topic": "reimbursements", "topics": [ "reimbursements", "customer-service" ], "agents": [ "ChatGPT" ], "what_they_did": "Two buyers returned LEGO sets with the minifigures removed. Amazon refunded them in full, marked the returns 'unfulfillable,' and offered the seller $25 on a $60 sale. He gave ChatGPT images of the return labels, screenshots of the original orders and his explanation. ChatGPT matched each return label to the right order, wrote two case submissions, prepared follow-up appeals when Amazon lowballed, and quoted Amazon policy.", "how_it_went": "He says these returns went from something he dreaded to 'just another workflow.' The final reimbursement amount isn't stated.", "worth_knowing": "Amazon doesn't show the original order ID on returns, which is why the label-to-order matching mattered.", "prompt": "I'm an Amazon FBA seller. Attached: photos of return labels from a removal order, screenshots of the original customer orders, and my notes. Tasks:\n1. Match each return label to the correct customer order (explain the match).\n2. Write a concise Seller Support case for each requesting reimbursement for a customer return that came back incomplete/damaged, citing the relevant Amazon FBA customer returns policy.\n3. Draft a polite follow-up appeal for if Amazon offers less than the item's value.\nOnly use facts from my attachments.", "prompt_origin": "starter", "source_url": "https://x.com/BuyHoldCollect/status/1905605799264596417", "source_type": "x", "source_author": "Britton Jewell", "source_handle": "@BuyHoldCollect", "date": "2025-03-28", "verification": "", "added": "2026-10-28", "tools": [ "ChatGPT" ], "url": "https://aiagentsforsellers.com/use-case/returns-reimbursement-case-with-chatgpt/" }, { "slug": "order-and-shipping-confirmation-retrieval-with-claude-code", "title": "Order and Shipping Confirmation Retrieval with Claude Code for Amazon Sellers", "seller_task": "Pull purchase and shipping confirmations from email for a given ASIN", "topic": "reimbursements", "topics": [ "reimbursements" ], "agents": [ "Claude Code", "Trigger.dev" ], "what_they_did": "The seller built a tool with Claude Code and Trigger.dev that pulls order and shipping confirmations out of email when given an ASIN, a date and an optional quantity, and combines them 'ready to submit.'", "how_it_went": "He calls it an automation of 'one of the most annoying things about being an amazon seller' and offers to set it up for others for free.", "worth_knowing": "The post doesn't say which submission it's for (e.g. reimbursement or invoice requests). FBA seller Chris DiBlasi called pulling reimbursement documentation from email with an AI agent 'the coolest unlock for Amazon.'", "prompt": "Search my email for purchase/order confirmations and shipping confirmations related to [product name / ASIN] around [date] (± 14 days) for quantity [qty]. For each match, extract: retailer/supplier, order number, order date, item description, quantity, unit price, ship date, tracking number. Combine them into one summary table plus a list of the source emails, so I can attach them to a Seller Central case.", "prompt_origin": "starter", "source_url": "https://x.com/yuri_jew/status/2033243939105337518", "source_type": "x", "source_author": "Yuri Jew", "source_handle": "@yuri_jew", "date": "2026-03-15", "verification": "", "added": "2026-10-28", "tools": [ "Claude Code", "Trigger.dev" ], "url": "https://aiagentsforsellers.com/use-case/order-and-shipping-confirmation-retrieval-with-claude-code/" }, { "slug": "inventory-ledger-reimbursement-review-with-chatgpt", "title": "Inventory Ledger and Fee Reviews with ChatGPT for Amazon Sellers", "seller_task": "Review account charges and the inventory ledger for anomalies and reimbursements", "topic": "reimbursements", "topics": [ "reimbursements", "customer-service" ], "agents": [ "ChatGPT" ], "what_they_did": "The tip: tell ChatGPT it's an Amazon account specialist familiar with Amazon's terms of service and policies, then ask it to write POAs to get inventory reinstated, write account-health appeals, review all charges to find anomalies, review the inventory ledger to find reimbursements, and draft messages to support.", "how_it_went": "This is a tip post with no outcome reported. It's one of the most-liked practical Amazon ChatGPT tips found in this research (100+ likes).", "worth_knowing": "Check any policy claims against Seller Central help pages. LLMs can cite outdated policy.", "prompt": "You are an Amazon account specialist familiar with all of Amazon's terms of service and FBA policies. I'm pasting my Inventory Ledger (detailed view) and my fee/transaction report for the last 18 months.\n1. Find units that were lost, damaged, or missing at fulfillment centers or inbound and not later found or reimbursed.\n2. Find customer returns refunded more than 45 days ago where the unit was never returned or reimbursed.\n3. Flag any fee charges that look inconsistent with the product's size tier.\nOutput a table per finding (FNSKU, date, quantity, reason, evidence row) and draft the Seller Support message for each.", "prompt_origin": "starter", "source_url": "https://x.com/ArbitrageOps/status/1932812796900548688", "source_type": "x", "source_author": "Arbitrage Ops", "source_handle": "@ArbitrageOps", "date": "2025-06-11", "verification": "", "added": "2026-10-28", "tools": [ "ChatGPT" ], "url": "https://aiagentsforsellers.com/use-case/inventory-ledger-reimbursement-review-with-chatgpt/" }, { "slug": "support-case-log-triage-with-cajari-mcp", "title": "Support Case Triage Across Client Accounts with Claude and an MCP for Amazon Sellers", "seller_task": "Find open and stalled Seller Central support cases across many accounts", "topic": "reimbursements", "topics": [ "reimbursements", "customer-service" ], "agents": [ "Claude", "Cajari MCP" ], "what_they_did": "Amazon has no support-case API, so Cajari collects each connected account's Seller Central Case Log and exposes it to AI assistants such as Claude via an MCP connector. Agencies then ask one question instead of logging into every client's Case Log on Monday.", "how_it_went": "The author reports that across three agencies (89 client accounts, 1,714 cases), the question surfaced 23 open cases. Six were waiting on the agency, twelve had gone more than a week without a reply, and the oldest had been open 109 days.", "worth_knowing": "The author discloses a personal stake in Cajari. Case Log is in beta, and the AI can read cases but not reply to them.", "prompt": "Across all my client accounts, which cases are open, and which have had no reply from us in the last 7 days?", "prompt_origin": "source", "source_url": "https://x.com/NhanTDinh/status/2113262015087067183", "source_type": "x", "source_author": "Nhan Dinh", "source_handle": "@NhanTDinh", "date": "2026-10-22", "verification": "", "added": "2026-10-28", "tools": [ "Claude", "Cajari MCP" ], "url": "https://aiagentsforsellers.com/use-case/support-case-log-triage-with-cajari-mcp/" }, { "slug": "plan-of-action-appeal-with-chatgpt", "title": "Plan of Action Appeals with ChatGPT for Amazon Sellers", "seller_task": "Write a Plan of Action to save an account from deactivation", "topic": "customer-service", "topics": [ "customer-service" ], "agents": [ "ChatGPT" ], "what_they_did": "Facing deactivation for a cancellation rate above 50%, the seller's first Plan of Action was declined on a call with an Account Health specialist. He gave ChatGPT a brief explanation of the problem, got about 10 detailed POA steps back, and told the specialist a few solid points from that list.", "how_it_went": "He says his appeal 'got immediately approved.'", "worth_knowing": "It was a live call, so he used ChatGPT's output as talking points rather than pasting it.", "prompt": "My Amazon seller account is at risk of deactivation because my order cancellation rate is [x]% (target < 2.5%). Root cause: [what actually happened]. Write a Plan of Action with three sections: (1) root cause, specific and owned; (2) immediate actions already taken; (3) preventive measures with owners and dates. Keep it factual and concise. No excuses, no blaming Amazon. Then give me 5 short talking points I can say on a call with Account Health.", "prompt_origin": "starter", "source_url": "https://x.com/WooWiFI/status/1784290312426582297", "source_type": "x", "source_author": "Woo", "source_handle": "@WooWiFI", "date": "2024-04-27", "verification": "", "added": "2026-10-28", "tools": [ "ChatGPT" ], "url": "https://aiagentsforsellers.com/use-case/plan-of-action-appeal-with-chatgpt/" }, { "slug": "seller-support-cases-and-appeals-with-chatgpt", "title": "Seller Support Cases and Appeals with ChatGPT for Amazon Sellers", "seller_task": "Draft and escalate Seller Support cases, POAs and appeals in one thread per issue", "topic": "customer-service", "topics": [ "customer-service", "reimbursements" ], "agents": [ "ChatGPT" ], "what_they_did": "An Amazon agency uses ChatGPT to draft responses for opening or escalating cases, Plans of Action and appeals, sometimes talking the issue through with voice. It keeps a single thread per issue: if an appeal is rejected, they paste the rejection and Account Health call notes into the same thread for a new draft. They also pair AI with analytics tools to find lost-inventory and overcharge errors and draft reimbursement cases, and keep templates for common customer inquiries.", "how_it_went": "The agency calls it a 'lifesaver' that keeps replies professional 'especially when frustrated with inexperienced agents' and 'saves so much time and mental energy.'", "worth_knowing": "The one-thread-per-issue habit is the reusable trick here.", "prompt": "This thread is only for my Seller Central case about [issue]. Here's the history so far: [original case text], [Amazon's replies], [my Account Health call notes]. Amazon just rejected my appeal with: \"[paste]\". Draft a new response that addresses exactly what they asked for, lists the attached evidence, and stays professional and under 250 words.", "prompt_origin": "starter", "source_url": "https://www.reddit.com/r/FulfillmentByAmazon/comments/1gv72zc/how_are_you_using_ai_chatgpt_to_make_selling_on/", "source_type": "reddit", "source_author": "u/HappyKapow", "source_handle": "u/HappyKapow", "date": "2024-11-19", "verification": "Reddit blocks automated fetching; text verified from the search-index copy of this thread on 2026-10-28. Open the link to read the full thread.", "added": "2026-10-28", "tools": [ "ChatGPT" ], "url": "https://aiagentsforsellers.com/use-case/seller-support-cases-and-appeals-with-chatgpt/" }, { "slug": "account-health-questions-with-amazon-seller-assistant-beta", "title": "Account Health and Listing Questions with Amazon Seller Assistant (Beta Review)", "seller_task": "Ask Amazon's built-in assistant about account health, listings, reports and launches", "topic": "customer-service", "topics": [ "customer-service", "listings" ], "agents": [ "Amazon Seller Assistant" ], "what_they_did": "An Amazon consultant tested Seller Assistant (formerly codenamed Amelia) during its beta. It covered understanding account health, simple listing creation, pricing and ad advice, image guidelines, title and bullet optimization, finding specific Seller Central reports, and organizing reports into spreadsheets.", "how_it_went": "He found it 'pretty basic,' with some general answers, and said it pulled from old templates, citing 'outdated company policies and non-existent emails for resolving seller support issues.' He sees it as a good pairing for overseas VAs, but says it won't find money Amazon owes you or raise margins.", "worth_knowing": "This is a 2025 beta review. Amazon has since added agentic actions, memory and workflows (see the plugin and workflow cases). He warns that Amazon logs everything you tell the tool.", "prompt": "(Ask Seller Assistant in Seller Central.) \"Explain why my Account Health Rating changed in the last 30 days, list each policy violation or metric affecting it, and link me to the exact Seller Central page where I can address each one.\"", "prompt_origin": "starter", "source_url": "https://x.com/BlackLabelAdvsr/status/1956365937826730191", "source_type": "x", "source_author": "Jon Elder", "source_handle": "@BlackLabelAdvsr", "date": "2025-08-15", "verification": "", "added": "2026-10-28", "tools": [ "Amazon Seller Assistant" ], "url": "https://aiagentsforsellers.com/use-case/account-health-questions-with-amazon-seller-assistant-beta/" }, { "slug": "inventorylab-buy-cost-updates-with-claude-cowork", "title": "InventoryLab Buy-Cost Updates with Claude Cowork for Amazon Sellers", "seller_task": "Find and fill in missing ($0) buy costs in InventoryLab", "topic": "inventory", "topics": [ "inventory" ], "agents": [ "Claude Cowork" ], "what_they_did": "On his first day with Claude Cowork, the seller had it open InventoryLab, review the active buy costs for FBM items, and update the $0 ones using the cost encoded in his SKUs.", "how_it_went": "He reports it navigated the site correctly, learned his SKU structure, updated items systematically, double-checked by searching retailer names, and returned a list of ASINs whose SKU didn't contain a cost. He called it 'a no-brainer' at $100/month.", "worth_knowing": "It works in your logged-in browser session, so watch the first runs.", "prompt": "Go to InventoryLab and open my active inventory. Filter to FBM items. For every item with a buy cost of $0, read the cost encoded in the SKU (format: [describe your SKU format]) and update the buy cost to that value. When you can't find more, double-check by searching each retailer name from my SKUs. At the end, give me a list of ASINs still at $0 because the SKU has no cost in it.", "prompt_origin": "starter", "source_url": "https://x.com/SaulSellsStuff/status/2011485422954705295", "source_type": "x", "source_author": "Saul", "source_handle": "@SaulSellsStuff", "date": "2026-01-14", "verification": "", "added": "2026-10-28", "tools": [ "Claude" ], "url": "https://aiagentsforsellers.com/use-case/inventorylab-buy-cost-updates-with-claude-cowork/" }, { "slug": "replen-entries-and-reconciliation-with-claude-cowork", "title": "Replen Entries and Reconciliation with Claude Cowork for Amazon Sellers", "seller_task": "Enter COGS and replen lines and run reconciliation checks", "topic": "inventory", "topics": [ "inventory" ], "agents": [ "Claude Cowork", "ChatGPT" ], "what_they_did": "An online-arbitrage seller has Claude Cowork pull purchases from the team's Buy Sheet and create replen lines in InventoryLab, with an SOP that walks through every button. Cowork also double-checks prep-center portal entries, monitors aged inbound inventory, and summarizes retailer promos from his inbox.", "how_it_went": "He describes InventoryLab entries as 'super slow' but 'flawless' and basically autonomous after setup. He thinks reviews and reconciliation are 'Claude's strong suit' for Amazon. Extracting tracking and order numbers from email was 'slow,' and gift-card balance checks got stalled by captchas.", "worth_knowing": "This is honest about limits (speed, captchas). He also uses ChatGPT for lead and supplier legitimacy checks (see the sourcing case).", "prompt": "SOP: Open my Buy Sheet (Google Sheet). For each row added since [date] with status \"Purchased\", go to InventoryLab → Inventory → Replenish, search the ASIN, and create a replen line with quantity = Qty column and buy cost = Unit Cost column. Mark the row \"Entered in IL\" in the sheet. If an ASIN isn't found, skip it and add it to a list. Don't submit any shipment. Report every line you created.", "prompt_origin": "starter", "source_url": "https://x.com/CookingCashflow/status/2018117933700993258", "source_type": "x", "source_author": "Ryan", "source_handle": "@CookingCashflow", "date": "2026-02-02", "verification": "", "added": "2026-10-28", "tools": [ "Claude", "ChatGPT" ], "url": "https://aiagentsforsellers.com/use-case/replen-entries-and-reconciliation-with-claude-cowork/" }, { "slug": "forecast-spreadsheet-audit-with-ai", "title": "Inventory Forecast Spreadsheet Audits with AI for Amazon Sellers", "seller_task": "Audit a reorder forecasting spreadsheet and rebuild the reorder logic", "topic": "inventory", "topics": [ "inventory" ], "agents": [ "AI assistant (unnamed)", "Seller Labs MCP" ], "what_they_did": "Before an AI lunch-and-learn for a tactical gear maker that also sells on Amazon, the presenter gave AI the company's forecasting spreadsheet (dozens of tabs, more than a thousand rows), the planner's walkthrough video and his own notes, and asked it to read everything.", "how_it_went": "He reports the AI found a row forecasting negative one knife for months, most vendors missing lead times (so the reorder trigger was buffer plus nothing), excess inventory sitting in parts rather than finished goods, and two years of tabs copied forward with drift.", "worth_knowing": "His takeaway: build the model in a sheet first, then hand it to AI. 'AI can run the model. A person owns the number.' The author works at Seller Labs, which sells an MCP for reorder math.", "prompt": "I'm attaching my inventory forecasting spreadsheet. Read every tab before answering. Report: (1) any formulas producing impossible values (negative or blank forecasts), (2) vendors or SKUs with missing lead times, (3) where the reorder point = buffer + lead-time demand is broken, (4) tabs that look copied forward with inconsistent numbers, (5) where excess inventory is sitting (components vs finished goods). Cite the tab and cell for each finding. Don't change anything.", "prompt_origin": "starter", "source_url": "https://x.com/NhanTDinh/status/2110362811041771862", "source_type": "x", "source_author": "Nhan Dinh", "source_handle": "@NhanTDinh", "date": "2026-10-14", "verification": "", "added": "2026-10-28", "tools": [ "Seller Labs MCP" ], "url": "https://aiagentsforsellers.com/use-case/forecast-spreadsheet-audit-with-ai/" }, { "slug": "custom-bookkeeping-and-shipments-app-with-claude", "title": "Bookkeeping and FBA Shipment App with Claude for Amazon Sellers", "seller_task": "Replace InventoryLab and Sellerboard with a custom-built tool", "topic": "inventory", "topics": [ "inventory", "reimbursements" ], "agents": [ "Claude" ], "what_they_did": "Cancelled InventoryLab and Sellerboard and built his own bookkeeping software with Claude (mostly Opus 4.5, perfected with Opus 5.5). It tracks Amazon, Walmart and eBay sales, refunds, reimbursements, expenses, shipping costs, inventory and cash flow, and he creates FBA and WFS shipments inside it.", "how_it_went": "He says the best part is that 'when something breaks, Claude fixes it that day.'", "worth_knowing": "You become the maintainer. A Reddit seller made a similar point about building your own versus paying for SaaS.", "prompt": "Help me design (don't code yet) a small bookkeeping app for my Amazon + Walmart + eBay business. It must track: sales by channel, refunds, Amazon reimbursements, expenses, shipping costs, inventory on hand by SKU with COGS, and cash flow. Propose the data model (tables and fields), what each channel's settlement/transaction report maps to, and the 5 screens I need. Ask me about anything you're unsure of.", "prompt_origin": "starter", "source_url": "https://x.com/pmcommerce/status/2109435502856384695", "source_type": "x", "source_author": "Parker", "source_handle": "@pmcommerce", "date": "2026-10-12", "verification": "", "added": "2026-10-28", "tools": [ "Claude" ], "url": "https://aiagentsforsellers.com/use-case/custom-bookkeeping-and-shipments-app-with-claude/" }, { "slug": "true-cogs-batch-tracker-with-claude-code", "title": "True COGS per Batch Tracking with Claude Code for Amazon Sellers", "seller_task": "Track ingredient and packaging costs per production batch across 3PL and FBA", "topic": "inventory", "topics": [ "inventory" ], "agents": [ "Claude Code" ], "what_they_did": "A solo CPG founder with inventory split between a 3PL and Amazon FBA built an inventory tracker with Claude Code over a weekend. It tracks ingredients, packaging, purchase dates, costs and BOMs across SKUs and production runs to calculate true COGS per batch.", "how_it_went": "He says he 'immediately saw my real COGS' and had more margin than he thought, so he could spend more on advertising. He estimates he saves at least $300/month on software.", "worth_knowing": "This replaced an Excel workflow that broke down as production runs got closer together.", "prompt": "Build me a simple local web app to track COGS per production batch. Inputs: purchases (date, item, quantity, total cost) for ingredients and packaging; BOMs per SKU; production runs (date, SKU, units made, which purchase lots were consumed). Output: COGS per unit for each batch, plus a current weighted COGS per SKU across inventory at my 3PL and at Amazon FBA. Use SQLite. Ask me questions before you start coding.", "prompt_origin": "starter", "source_url": "https://x.com/theonlyhaitham/status/2018328804460986789", "source_type": "x", "source_author": "haitham", "source_handle": "@theonlyhaitham", "date": "2026-02-02", "verification": "", "added": "2026-10-28", "tools": [ "Claude Code" ], "url": "https://aiagentsforsellers.com/use-case/true-cogs-batch-tracker-with-claude-code/" }, { "slug": "seller-central-monitoring-to-slack-with-claude", "title": "Seller Central Monitoring and PPC Rules with Claude for Amazon Sellers", "seller_task": "Monitor account health, hijackers, stranded inventory and suppressed listings across brands", "topic": "inventory", "topics": [ "inventory", "ppc", "customer-service" ], "agents": [ "Claude" ], "what_they_did": "In the first week after giving 100 employees paid Claude plans, a multi-brand company's Amazon team rebuilt its PPC bidding rules from the ground up. Daily Seller Central monitoring across every brand (account health, hijackers, stranded inventory, suppressed listings) now runs itself and auto-posts to Slack.", "how_it_went": "He says the PPC rebuild 'Closed gaps that were bleeding spend' and that the daily monitoring 'now runs itself.' No figures are given.", "worth_knowing": "This is a company-wide week-one recap, and the Amazon details are brief.", "prompt": "Every morning at 8am, for each of my brands' Seller Central accounts, check: Account Health rating and any new policy warnings; any new sellers on my brand's ASINs (possible hijackers); stranded inventory; suppressed or inactive listings. Post one Slack message per brand with only what changed since yesterday, marked [urgent] or [FYI], and a link to the Seller Central page for each item.", "prompt_origin": "starter", "source_url": "https://x.com/keithrichman/status/2052785770595790882", "source_type": "x", "source_author": "Keith Richman", "source_handle": "@keithrichman", "date": "2026-05-08", "verification": "", "added": "2026-10-28", "tools": [ "Claude" ], "url": "https://aiagentsforsellers.com/use-case/seller-central-monitoring-to-slack-with-claude/" }, { "slug": "profit-and-cogs-questions-with-sellerlegend-mcp", "title": "Profit and COGS Questions with the SellerLegend MCP for Amazon Sellers", "seller_task": "Ask profit, margin and ACoS questions and update COGS from ChatGPT or Claude", "topic": "inventory", "topics": [ "inventory", "ppc" ], "agents": [ "SellerLegend MCP", "ChatGPT", "Claude" ], "what_they_did": "SellerLegend's MCP server (OAuth, 21 tools) lets ChatGPT (Plus and up) and Claude (Pro and up) read your SellerLegend data in near real time: orders, sales, transactions, inventory, COGS, products, feedback, PPC, reports and notifications. It can also update a product's COGS after you confirm the change.", "how_it_went": "This is a vendor feature page with example questions and no customer results.", "worth_knowing": "Updating COGS replaces that product's cost periods, so confirm carefully.", "prompt": "What's my profit by SKU this month?", "prompt_origin": "source", "source_url": "https://sellerlegend.com/features/mcp-ai-access", "source_type": "docs", "source_author": "SellerLegend", "source_handle": "@SellerLegend", "date": "", "verification": "", "added": "2026-10-28", "tools": [ "SellerLegend MCP", "ChatGPT", "Claude" ], "url": "https://aiagentsforsellers.com/use-case/profit-and-cogs-questions-with-sellerlegend-mcp/" }, { "slug": "restock-plans-and-account-fixes-with-amazon-seller-assistant-agent", "title": "Restock Plans and Account Fixes with Amazon Seller Assistant's Agent for Amazon Sellers", "seller_task": "Coordinate inventory orders, growth plans and account-issue fixes", "topic": "inventory", "topics": [ "inventory", "customer-service", "listings" ], "agents": [ "Amazon Seller Assistant" ], "what_they_did": "At Accelerate 2025, Amazon added agentic capabilities to Seller Assistant (built on Bedrock), so it can act on a merchant's behalf with permission. That includes coordinating inventory orders and growth plans and implementing fixes for account issues. Its 2024 predecessor, Project Amelia, handled troubleshooting, inventory planning advice and listing-title brainstorming.", "how_it_went": "Amazon told CNBC that 1.3 million sellers have used its gen AI listing tools, which can produce about 70% of a product listing, and that it didn't plan to charge for Seller Assistant.", "worth_knowing": "All adoption figures come from Amazon. See the 2026 plugin and workflow cases for later updates.", "prompt": "(Ask Seller Assistant in Seller Central.) \"Draft a restock plan for my top 20 ASINs by sales: for each, show current FBA inventory, inbound, days of cover, and a recommended send-in quantity for the next 60 days. Don't create any shipment. Wait for my approval.\"", "prompt_origin": "starter", "source_url": "https://www.cnbc.com/2025/09/17/amazon-ai-agent-sellers.html", "source_type": "news", "source_author": "CNBC (Annie Palmer)", "source_handle": "CNBC", "date": "2025-09-17", "verification": "", "added": "2026-10-28", "tools": [ "Amazon Seller Assistant" ], "url": "https://aiagentsforsellers.com/use-case/restock-plans-and-account-fixes-with-amazon-seller-assistant-agent/" }, { "slug": "a-plus-brand-story-with-amazon-gen-ai", "title": "A+ Content Narrative and Lifestyle Images with Amazon's Gen AI for Amazon Sellers", "seller_task": "Generate A+ narrative copy and lifestyle backgrounds from a few product terms and one image", "topic": "a-plus", "topics": [ "a-plus", "creative" ], "agents": [ "Amazon A+ Content gen AI", "Amazon Video Generator" ], "what_they_did": "Amazon's A+ Content tool includes generative AI. Brands enter a few descriptive terms they want highlighted (Amazon's examples: 'quality leather', 'versatile for any occasion'), and it writes narrative A+ copy. Uploading a product image generates background scenes and lifestyle imagery. The same Amazon post describes Video Generator, which makes ad videos from a single product image at no extra cost.", "how_it_went": "Amazon says A+ Content in general 'can drive a sales lift of up to 20%.' It published no figures for the AI-generated version specifically.", "worth_knowing": "The 20% figure is Amazon's claim for A+ Content overall, not for AI-generated A+. Review generated copy for claims you can't support.", "prompt": "Before using Amazon's A+ generator, write down your inputs with any AI assistant:\n\"Here are my product facts and reviews: [paste]. Give me the 5 short descriptive phrases (2-4 words each) that best capture what buyers value, ranked by how often reviews mention them, and one sentence on why each matters. Use only my facts.\"", "prompt_origin": "starter", "source_url": "https://www.aboutamazon.com/news/innovation-at-amazon/amazon-generative-ai-seller-growth-shopping-experience", "source_type": "official", "source_author": "Amazon (Mary Beth Westmoreland)", "source_handle": "Amazon", "date": "2025-05-08", "verification": "", "added": "2026-10-28", "tools": [ "Amazon's built-in Gen AI tools" ], "url": "https://aiagentsforsellers.com/use-case/a-plus-brand-story-with-amazon-gen-ai/" }, { "slug": "product-research-pipeline-with-claude-skills", "title": "Product Research (Niche, Margin, Reviews) with Claude Skills for Amazon Sellers", "seller_task": "Go from a category to a GO/WATCH/SKIP product verdict", "topic": "research", "topics": [ "research", "reviews" ], "agents": [ "Claude", "Claude in Chrome", "Helium 10", "Profitlee" ], "what_they_did": "product-research-skills is an open-source bundle of three Claude skills that drive your own logged-in browser via Claude in Chrome. Opportunity Finder takes a category through Helium 10 Black Box and Best-Sellers data to scored niches. Cost Analyze reverse-image-searches 1688 for FOB cost and runs Profitlee's fee math. Review Analyze clusters complaints and praise into a GO/WATCH/SKIP verdict and a single product dossier.", "how_it_went": "The publisher says the skills 'halt and ask rather than quietly inventing a number,' cap Helium 10 lookups at three per run, and cache artifacts for 14 days. No user outcomes are reported.", "worth_knowing": "Published by Profitlee, whose calculator powers the margin step. Needs a 1688 account, and Helium 10 is strongly recommended.", "prompt": "Run the Opportunity Finder skill on the category \"Pet Supplies\": filter Black Box for price $20-45, monthly revenue > $10k, < 500 reviews, weight < 2 lb. Give me the top 3 scored niches with the evidence behind each score. Stop and ask me if any source blocks access. Do not estimate missing numbers.", "prompt_origin": "starter", "source_url": "https://profitlee.com/en/blog/amazon-product-research-pipeline", "source_type": "blog", "source_author": "Profitlee", "source_handle": "@AronLEEDev", "date": "2026-07-16", "verification": "", "added": "2026-10-28", "tools": [ "Claude", "Helium 10", "Profitlee" ], "url": "https://aiagentsforsellers.com/use-case/product-research-pipeline-with-claude-skills/" }, { "slug": "fba-niche-research-in-one-prompt-with-launch-fast-mcp", "title": "FBA Niche Research in One Prompt with Claude and the Launch Fast MCP for Amazon Sellers", "seller_task": "Research a niche: keywords, trademarks and suppliers in one conversation", "topic": "research", "topics": [ "research" ], "agents": [ "Claude", "Launch Fast MCP" ], "what_they_did": "The Launch Fast team built an MCP server so Claude can run Amazon product research in one conversation, pulling competitor keywords, checking trademarks and finding suppliers under an MOQ.", "how_it_went": "The builder says Claude runs 4 tool calls and returns real data in about 2 minutes. A related post claims market analysis, 200+ keywords, verified Alibaba suppliers and trademark checks in under 2 minutes on a real niche.", "worth_knowing": "Posted by the tool's builder.", "prompt": "Research silicone spatulas. Pull keywords for the top 3 competitors. Check trademarks on FlexiChef. Find suppliers under 500 MOQ.", "prompt_origin": "source", "source_url": "https://x.com/hasaamb/status/2039375958021476696", "source_type": "x", "source_author": "Hasaam", "source_handle": "@hasaamb", "date": "2026-04-01", "verification": "", "added": "2026-10-28", "tools": [ "Claude", "Launch Fast MCP" ], "url": "https://aiagentsforsellers.com/use-case/fba-niche-research-in-one-prompt-with-launch-fast-mcp/" }, { "slug": "keyword-and-listing-research-with-helium-10-mcp", "title": "Keyword and Competitor Research with the Helium 10 MCP in Claude or ChatGPT for Amazon Sellers", "seller_task": "Query Helium 10 keyword, listing, P&L and inventory data from an AI assistant", "topic": "research", "topics": [ "research", "listings", "ppc", "inventory" ], "agents": [ "Helium 10 MCP", "Claude", "ChatGPT", "Codex" ], "what_they_did": "Helium 10's MCP connects a Diamond-plan account to Claude, ChatGPT or Codex. Tools include keyword analysis (Cerebro and Magnet equivalents), listing details and scores, competitor overviews, rank history, P&L breakdowns, FBA inventory health and ads queries. Helium 10 frames it as 'Connect once → Ask your AI assistant → Receive insights based on live data.'", "how_it_went": "These are vendor docs (158 tools; 1,000 MCP calls/month included, $50 per 500 extra). Helium 10 lists use cases (competitor analysis, keyword research, product research reports, profitability) but no customer results. Campaign actions and Ads Rules are draft-first and approval-gated.", "worth_knowing": "Requires Diamond or higher. The ads features need the profile in 'Managed' mode, which Helium 10 says incurs a 2% fee on ad spend. Free third-party Claude skills built on it exist (e.g. WAYAMZ).", "prompt": "What keywords is ASIN B0XXXXXXXX ranking for right now in the US?\nCompare the keyword strategy of these three competitor ASINs.\nWhich of my products had the lowest net margin this month?\nShow me my campaign performance broken down by ad group for last month. Pause campaigns with ACoS over 50%.", "prompt_origin": "source", "source_url": "https://kb.helium10.com/hc/en-us/articles/51580564409883-Getting-Started-with-Helium-10-MCP", "source_type": "docs", "source_author": "Helium 10", "source_handle": "Helium 10", "date": "2026-10-15", "verification": "", "added": "2026-10-28", "tools": [ "Helium 10", "Claude", "ChatGPT", "Codex" ], "url": "https://aiagentsforsellers.com/use-case/keyword-and-listing-research-with-helium-10-mcp/" }, { "slug": "reddit-product-research-with-perplexity", "title": "Reddit Product Research with Perplexity for Amazon Sellers", "seller_task": "Mine Reddit discussions for product insights and cross-check with Amazon reviews", "topic": "research", "topics": [ "research", "reviews" ], "agents": [ "Perplexity" ], "what_they_did": "In Perplexity, search 'best [product]' with the focus set to social/Reddit, analyze the recommended brands, ask follow-up questions (e.g. specific feature combinations), then verify against Amazon reviews.", "how_it_went": "The author says he uses the approach 'on the daily,' because Reddit is 'where tons of shopper insights are.' No metrics are given.", "worth_knowing": "This is from 2024, and Perplexity's UI has changed since. The method still applies.", "prompt": "Search Reddit discussions only. What do people recommend as the best [product], and why? List the top brands mentioned, the specific features people praise, and the most common complaints, with links to the threads. Then tell me which unmet needs come up repeatedly that none of the recommended products solve.", "prompt_origin": "starter", "source_url": "https://x.com/itschadrubin/status/1816095287741735232", "source_type": "x", "source_author": "Chad Rubin", "source_handle": "@itschadrubin", "date": "2024-07-24", "verification": "", "added": "2026-10-28", "tools": [ "Perplexity" ], "url": "https://aiagentsforsellers.com/use-case/reddit-product-research-with-perplexity/" }, { "slug": "sourcing-lead-matching-with-chatgpt", "title": "Sourcing Lead Verification with ChatGPT for Amazon Sellers", "seller_task": "Check Amazon vs source product matches and retailer legitimacy before buying", "topic": "research", "topics": [ "research" ], "agents": [ "ChatGPT" ], "what_they_did": "An online-arbitrage team trains a ChatGPT prompt with examples of acceptable perfect matches. Team members submit the Amazon link and the source link, and ChatGPT says whether the product is a safe match by his criteria. A second prompt, trained on examples of safe and sketchy retailers, rates unfamiliar sources as safe, risky or a pass.", "how_it_went": "He says it saves minutes per lead across 'dozens of times each week.' He calls the legitimacy check 'Not perfect so use your own judgement as the final decision especially when money is at stake.'", "worth_knowing": "From the same post as the replen-entries case.", "prompt": "You verify online-arbitrage leads. A MATCH means the source item is identical to the Amazon listing in brand, model/part number, size/count, variation, and condition. Examples of perfect matches: [paste 3]. Examples of near-misses to reject: [paste 3].\nAmazon link: [url] Source link: [url]\nAnswer: MATCH / NOT A MATCH / UNSURE, then list each attribute compared and any mismatch.", "prompt_origin": "starter", "source_url": "https://x.com/CookingCashflow/status/2018117933700993258", "source_type": "x", "source_author": "Ryan", "source_handle": "@CookingCashflow", "date": "2026-02-02", "verification": "", "added": "2026-10-28", "tools": [ "ChatGPT" ], "url": "https://aiagentsforsellers.com/use-case/sourcing-lead-matching-with-chatgpt/" }, { "slug": "competitor-price-monitor-with-claude-code-and-playwright", "title": "Competitor Price Monitoring with Claude Code and Playwright for Amazon Sellers", "seller_task": "Monitor competitor prices daily and get notified", "topic": "research", "topics": [ "research" ], "agents": [ "Claude Code", "Playwright" ], "what_they_did": "With zero prior coding experience, a supplement brand owner built an automated competitor price monitor in one afternoon using Claude Code, Playwright and a cron job. It covers 15 products across 3 brands and runs every morning at 10am with automatic notifications.", "how_it_went": "He reports the 'First run caught a $5 price gap I didn't know existed.'", "worth_knowing": "Amazon's Conditions of Use restrict automated agents. Check the terms and consider official APIs before scraping.", "prompt": "Build me a small script with Playwright that, for each ASIN in prices.csv, opens the Amazon product page, reads the current buy-box price, and compares it with the previous run. Save results to a CSV. If any competitor's price changes by more than $1 or 5%, send me an email/Slack message with the ASIN, old price, new price. Set it up to run daily at 10am with cron. Explain each step; I've never coded.", "prompt_origin": "starter", "source_url": "https://x.com/DerrestWilliams/status/2029007920793362656", "source_type": "x", "source_author": "Derrest", "source_handle": "@DerrestWilliams", "date": "2026-03-04", "verification": "", "added": "2026-10-28", "tools": [ "Claude Code", "Playwright" ], "url": "https://aiagentsforsellers.com/use-case/competitor-price-monitor-with-claude-code-and-playwright/" }, { "slug": "find-seller-central-reports-with-claude-code-and-playwright-mcp", "title": "Finding Seller Central Reports with Claude Code and Playwright MCP for Amazon Sellers", "seller_task": "Find hard-to-locate reports in the redesigned Seller Central", "topic": "research", "topics": [ "research", "inventory" ], "agents": [ "Claude Code", "Playwright MCP" ], "what_they_did": "On a live account in the new Seller Central, the author used Claude Code with the Playwright MCP, read-only and in a visible browser he logged into himself, to find the 'parent and child sales by product' report a forum seller couldn't locate.", "how_it_went": "He reports the agent found it in four moves. It read every menu label and searched the page (no match), found the 'All reports' button on the dashboard, opened Business Reports → Detail Page Sales and Traffic By Child Item, and closed a tour modal and a survey along the way. It changed nothing.", "worth_knowing": "His rules: you log in yourself, look-don't-touch by default, watch the first runs, and crop screenshots. The author works at Seller Labs.", "prompt": "Clone https://github.com/sellerlabs/slt-toolbox-public and set up ONLY the Playwright MCP, following mcps/playwright-mcp/README.md. In that folder run npm install and npx playwright install. Then register playwright-mcp-visible.js with yourself as an MCP server named playwright-visible, using the file's absolute path. Skip Lazy Hub and every other tool in the repo. Tell me when it is done.", "prompt_origin": "source", "source_url": "https://x.com/NhanTDinh/status/2108550933827186875", "source_type": "x", "source_author": "Nhan Dinh", "source_handle": "@NhanTDinh", "date": "2026-10-09", "verification": "", "added": "2026-10-28", "tools": [ "Claude Code", "Playwright" ], "url": "https://aiagentsforsellers.com/use-case/find-seller-central-reports-with-claude-code-and-playwright-mcp/" } ]