AI agents for Amazon sellers.
Real cases, sorted by the job.

Every morning, review my repricer and sales data and send me three lists:
1. Not moving: ASINs with zero sales in the last 14 days, with current price, min/max and Buy Box status.
2. Slowing: ASINs with no sales in the last 7-10 days.
3. Selling fast: ASINs averaging 5+ units a day, with days of stock left and a suggested higher min price.
Suggest changes only. Don't change any min/max or repricing rule until I approve it.

Pick the next listing in the queue. Keep the main image as it is. Rebuild images 2-7 and the A+ modules from the product files in Drive: one benefit per image, short headline, the brand's fonts and colours, and no claim that isn't in the spec sheet.
Post the pack to the team channel for approval before anything is uploaded.

Here is my search term report for the last [14] days [attach].
Give me, in plain English: the search terms wasting money (spend over $[x] with no orders) and the ones worth scaling (orders with ACoS under [y]%).
For each, the exact change you'd make. Don't make any change until I confirm it.

Using the Amazon seller connector, pull my orders, FBA orders and settlements for the last 24 months into a local database with one row per sale.
For each item, show units sold, average and lowest sale price, and the max buy price that leaves 50% profit after Amazon fees and shipping. Read-only: don't change any listing or price.

I'm an Amazon FBA seller doing retail arbitrage. The folder [folder] has photos of one receipt batch.
1. Read every receipt. For each item give store, date, item title, quantity and unit cost (include tax if it's on the receipt). Mark any line you can't read clearly instead of guessing.
2. Match each item to an ASIN and show me the match before listing anything.
3. After I approve, create the listings, then reconcile my quantity on hand against the receipt totals and flag any mismatch.
4. Add one row per item to my Google Sheet: date, item title, COGS, quantity, ASIN.
5. Draft the FBA shipping plan for the items I mark as prepped and packed. Don't submit it until I say so.

Build me a simple purchase tracker for my Amazon resale business. I want to log each sourcing buy with: date, source, item, quantity, cost per unit, shipping and tax, and the ASIN I plan to sell it under. Show running totals per month and a list of items bought but not yet sent to FBA. Keep the data in a file I own and let me export it as CSV. Start with the smallest version and ask me before adding features.

Across all my client accounts, which cases are open, and which have had no reply from us in the last 7 days?

I want to update my Seller Central catalog. First, export every parent and child ASIN with its title, bullets, key attributes and variation theme into a sheet.
Then propose updates following these rules: [your rules]. Show me before/after for 5 sample variants.
After I approve the samples, apply the rest in batches of [20], and read each listing back afterwards to confirm the change saved. Stop and tell me if Amazon blocks or rejects anything.

Using my WisePPC data, compare units shipped vs units received for every FBA inbound shipment closed in the last [60] days.
List each shipment with a shortfall: shipment ID, SKU, units sent, units received, units missing, and estimated value.
Skip anything Amazon already reimbursed. For each remaining gap, draft the Seller Central case text with the evidence I'll need (invoice, packing list, carrier proof of delivery).
Ask me before filing anything, then track each case until it is resolved.

Pull every Amazon reimbursement for the last 30 days and trace each one to its source: automatic reimbursement, a case my team filed (closed shipment, lost or damaged in warehouse, customer return), or my third-party reimbursement service.
Give me a table with totals and % share by source, and the service's payout before and after its fee.
Then list closed shipments from the last 30 days with missing units that have no open or resolved case yet, so my VA can file them.
Show how each reimbursement appears in my Sellerboard profit report.

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.

For my 5 ASINs with the highest return cost, read the FBA customer returns report, the product's reviews and the current listing side by side. For each ASIN give:
- the likely root cause, with return counts, reason-code shares, 2-3 buyer quotes, the gap in the listing, and a confidence level (never 'high' from reason codes alone);
- ONE change to test (title under 75 characters, counted; image brief; size chart; or supplier brief);
- today's return rate as the baseline to check at 30 and 60 days.
Don't change anything and don't contact buyers.

Here's my brand kit: [logo, fonts, hex colors, website]. Remember this style for everything we make.
Product: [name, what it does, 3 key facts]. Attached: my real product photo.
Make [4] secondary listing images in my brand style: [benefit graphic, how-to-use steps, size/what's in the box, lifestyle scene]. Keep the product exactly as in my photo and don't add claims I haven't given you. Square, high resolution.

Here are my competitors' 1-3 star reviews and return reasons: [paste].
1. List the questions buyers are really asking (e.g. 'Is it microwave safe?'), grouped and ranked by how often they come up.
2. For each of the top [8], write a bullet line that answers it with an exact spec from my product facts: [facts]. Format: "Question? Answer, with the number."
3. Use each keyword from my list only once: [keywords].
4. Suggest 5 Q&A pairs for a Premium A+ Q&A module.
Don't answer anything my facts don't cover; list those as gaps instead.

Before I turn on automated inventory sync, check my Inventory Master sheet: [attach].
For every SKU, confirm it has an Amazon SKU, Shopify inventory item ID and Flipkart SKU, and a safety buffer. List missing or duplicate mappings.
Then show what the sellable quantity would be per channel after the buffer, and flag any SKU where it would go to zero or below.

You have read access to my Amazon Ads and sales data. Don't change anything without my approval.
1. List my current campaigns and the products each one covers. Flag any campaign that mixes different product formats.
2. Propose a structure with one campaign per product format, each with its own keyword list pulled from search terms that already convert for that format.
3. For every change you want to make (new campaign, bid, budget, negative), tell me what and why, and wait for my yes.
4. Each week, report spend, CPC, sales and units by product format against the week before.

Help me build a launch helper for my Amazon catalog. Work step by step and check with me between steps.
1. From the product data I paste (one row per product), map each column to the Amazon flat file fields for category [category]. List any required field I don't have data for.
2. Fill the flat file rows for these products and flag anything you had to infer.
3. Draft launch campaigns for each product: take keywords from my winning campaigns (data pasted below), exclude keywords from campaigns I paused for overspending, and label each campaign as ranking or defensive.
4. Keep the campaigns as drafts. I'll enable them myself when the listing is live.
Product data: [paste]
Campaign data: [paste]

Check my advertising campaigns. If in the last 12 months, look back period. There are any search terms or ASINs in auto or broad campaigns that have more than 20 clicks, but no sales. Give it to me in a table that shows which campaigns are in how many clicks, how much spend.

Audit this Amazon account from the reports I've attached: [Business Report, Search Term Report, Inventory report, Account Health].
Only use numbers from these files and cite the report and row for each finding.
Return the 10 biggest problems ranked by estimated monthly cost, each with: the evidence, the fix, and what to check after 14 days.
If a conclusion needs data I didn't give you, say which report you'd need instead of guessing.

Here are my Inventory Ledger, FBA Inventory and Fee Preview exports [attach].
List SKUs with units that will be 181+ days old at the next 15th snapshot, the estimated surcharge, and whether a removal order by the 14th is cheaper.
Then list SKUs whose 30- and 90-day days of supply are both under 28 (low-inventory fee risk). Show every calculation.

Pull my FBA reimbursements approved in the last 60 days with reason, quantity and amount. Then pull inventory ledger movements and FBA customer returns for the same ASINs and dates.
List every lost/damaged ledger movement and every refunded return that has no matching reimbursement (match on ASIN/FNSKU, quantity and date). For each, show the claim deadline and the days left. Label every match as a candidate to check in Seller Central, not a confirmed claim.

Help me plan an in-house inventory planning tool before we build anything.
Stock locations: [3PLs] plus Amazon FBA in [marketplaces]. Demand sources: [website, Amazon, retail].
1. List the data feeds we need from each location and how often to pull them.
2. Propose ABC grouping rules and two forecast methods I can switch per product and channel.
3. Define red/amber/green alert rules from days of cover and lead time.
4. Design the reorder suggestion: respect MOQ and pallet quantities, and suggest 3PL-to-Amazon transfers separately from supplier orders.
5. POs are drafts only, for my approval in [accounting system].
Ask me for anything you need before you start.

Find 10 product ideas in [category] under $[price].
For each: price, review count, estimated monthly sales range (label it an estimate and say what it's based on), margin after FBA fees at that price, and the top complaint in competitors' 1-3 star reviews.
Score each on demand, competition, margin and risk, and flag possible patent, trademark or gating issues for me to check.

I've attached 30 days of my keyword price log (date, keyword, ASIN, title, price, rating, reviews).
For keyword [x]: show the median first-page price by week, the ASINs that cut price more than 10%, and new ASINs that appeared in the last 7 days.
Then tell me where my ASIN [B0...] sits against the median.

Every morning, send me a short report on my Amazon store: yesterday's ad spend, sales and ACOS by campaign, any search term with 10+ clicks and no sale, units sold and estimated profit per product after fees and ad cost, and any listing that was suppressed or changed. Suggest at most 3 keyword changes and wait for my OK.

What keywords is ASIN B0XXXXXXXX ranking for right now in the US?
Compare the keyword strategy of these three competitor ASINs.
Which of my products had the lowest net margin this month?
Show me my campaign performance broken down by ad group for last month. Pause campaigns with ACoS over 50%.

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.

Using my connected Amazon data and Sellerboard, look at the last 6 months for every ASIN.
Build a table: units sold, revenue, refund count, refund rate, Amazon fees, COGS (from Sellerboard) and net profit.
Flag ASINs with high sales but a high refund rate and little or no profit.
Then tell me which flagged ASINs are back in stock or due for a reorder, and for each one whether it looks worth replenishing, showing the numbers. If COGS is missing, write 'unknown' instead of guessing. Don't place or change any orders.

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.

Help me plan a dashboard for my agency's Amazon client accounts before writing code.
Accounts: [number] Seller Central and [number] Vendor Central. I need daily sales, units, ad spend and TACOS per client, and an agency-wide view.
1. List the SP-API and Amazon Ads API reports we need and how often to pull them within rate limits.
2. Propose a database schema with one row per client per day per marketplace.
3. Plan the login so each client could later see only their own data.
Start with one client account, read-only.

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.

Build a small dashboard that checks [liquidator site] every hour for new pallet and single-item listings, matches each item to an Amazon ASIN with a confidence score, and estimates resale value after fees.
Only suggest a bid when confidence is high, capped at 75% of the estimated offer value. Never complete a purchase; I confirm each one.

Every morning at 7am: for each ASIN and keyword in my sheet, search Amazon US and record the ASIN's organic position (skip sponsored results, stop after page 3), plus its current price.
Save the results with today's date, and alert me about any ASIN that dropped more than 10 places on a main keyword since yesterday.

Connect to my Amazon Ads account (walk me through getting API access if needed). Every night:
1. Find keywords, targets and search terms with spend over $[x] and no orders in the last [14] days, plus ACOS above [y]% with [n]+ clicks. List them with spend wasted.
2. Find targets with ACOS below [z]% and enough impressions to scale, and suggest bid increases of no more than [10]%.
Send me one summary with the proposed changes. Don't pause anything or change bids until I reply yes.

Pull the all-time return rate for each of my SKUs, split by marketplace, plus units sold and the top return reasons.
Put it in a table sorted by units sold, and flag any SKU whose return rate is above its category average.

Before you do anything in my Amazon seller account:
1. Use only the Amazon APIs and connected seller tools I've authorized, not the Seller Central website, unless I tell you otherwise.
2. If any Amazon page or API says the request is blocked, unauthorized, or that an AI agent isn't allowed, stop immediately, don't retry or work around it, and send me the exact message.
3. Keep a log of every change you make so I can show Amazon what the agent did.

Here is my product photo and a one-line description: [what it is and who it's for].
Write a creative brief for an 8-second vertical (9:16) video ad: the product name, three different scenes to place it in, one camera move, a tagline, and a voiceover under 20 words.
The product must look exactly as it does in the photo in every scene: same shape, color, label and parts. Then tell me which scene would sell it best and why.

Using my Sponsored Products data for the last 30 days compared with the 30 days before:
1. Which 5 campaigns drove the most change in sales, up or down, and why?
2. Which search terms spent more than $[X] with zero orders?
3. What share of my ad-attributed sales were new-to-brand?
Recommend up to 3 changes, show the expected effect of each, and don't apply anything until I say yes.

I'm attaching my Amazon Inventory Ledger and Reimbursements report for the last 60 days.
Which units did Amazon lose or damage that it never reimbursed? Give me a table: FNSKU, date, event type, quantity, reference ID, and whether a reimbursement matches. Only list units where the ledger clearly shows a loss or damage and no matching reimbursement.

I'm attaching 4 phone photos of my product from different angles and one close-up of the label.
Make one change at a time. First: place the product on a pure white background for my main image, keeping the label text exactly as in the close-up.
Wait for my OK, then create a lifestyle scene: [describe customer, setting, mood], square, 2000x2000px.
If any label text looks distorted, fix only that area using the close-up.

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.

Here are the keywords my top 3 competitors rank for organically and the ones they bid on: [paste both lists, with search volume].
1. List high-volume terms with real organic traffic that none of them bid on.
2. Group those terms by shopper language (aesthetic/style words vs product nouns).
3. 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.

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.
Task: 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.

make the purple and blue ones into Amazon Premium A+

Here are 80 reviews for [product] (pasted below).
1) Group the reviewers into buyer personas (who they are, what they use it for).
2) For each persona, list complaints and unmet needs, with how many reviews mention each.
3) Which persona is buying anyway but least satisfied, and what product change would fix their top two complaints?

You are my Amazon data engineer. Write a plain-Python script (standard library only) that:
1. 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.
2. Uses SP-API to pull Sales & Traffic by ASIN, FBA inventory, and customer returns for the same windows.
3. Saves everything as one JSON bundle per day.
Then 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.

cut this into one video with captions and a SHOP NOW card

Every morning, for ASINs [ASIN 1] and [ASIN 2], pull yesterday's sessions, units and unit session percentage from Sales & Traffic, and split units into organic vs ad-attributed using my Sponsored Products data.
Keep a running log with the date of each listing change I tell you about (e.g. 'moved image 5 to position 3 on [date]').
Each Monday, compare the 7 days before and after the latest change and tell me whether the difference looks meaningful or is still noise.

Find all keywords across all campaigns that have spent more than $50 with zero sales in the last 30 days.

Write an Amazon US listing for this product: [product facts].
Deliver: (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.

You are my weekly Amazon PPC auditor. First read my goals sheet: target ACOS, target TACOS, monthly sales target and daily budget per campaign.
Then, from the campaign and search-term reports I attach for the last 7 and 30 days:
1. List every campaign that is off target, and by how much.
2. Propose specific changes (bid changes, negatives, budget moves, pausing) that move each one toward its goal. Give the reason and the data behind each.
3. Put the changes in a table with an Approve/Reject column. Change nothing yourself.

I've added my Amazon SP-API and Amazon Ads API credentials (client ID, client secret, refresh token) to your secret storage.
1. Test the connection with read-only calls: list my marketplaces, pull yesterday's orders count and my active Sponsored Products campaigns.
2. Tell me exactly which data you can and can't read with these credentials.
3. Don't make any write calls (prices, listings, bids, budgets) unless I approve each one.

Use my product photo as the reference and don't change it.
Make a natural, phone-style photo of [product] in use on [setting], with a generic unbranded [cheaper alternative] beside it showing [the specific weakness, e.g. it bends under load].
No logos or text. Square, 2000x2000.

Here are today's and yesterday's results for my tracked keywords: [paste table: keyword, best organic position, top-20 slots held, top competitor ASIN].
Tell me which keywords I gained or lost on, where a competitor overtook me, and which drops line up with lower ad spend or a stockout on my side [paste]. Suggest one action per keyword that moved.

Here are today's offers on my ASINs: [paste: ASIN, seller name, price, Buy Box yes/no]. My MAP prices and authorized sellers: [paste].
List every breach: unauthorized seller, under MAP, or both, with the amount under MAP. Sort by whether they hold the Buy Box, then by size of the breach. Draft a short, polite notice I could send to an authorized reseller who's under MAP.

Before you recommend any change to my Amazon campaigns, tell me: what in this data supports it, what could go wrong, and how I'd know within 14 days whether it worked.
Recommend at most 2 changes at a time, never touch my top-selling SKUs' existing campaigns, and remind me of the baseline numbers so I can roll back.

You are a senior ecommerce customer-research and creative strategist.

I am giving you public Amazon reviews from competing products.

Analyze only the supplied reviews. Do not invent facts, product capabilities, customer quotes, frequencies, or conclusions.

First, classify each review as:
1. Product defect
2. Expectation gap
3. Setup or usability friction
4. Missing feature
5. Wrong customer or use case
6. Shipping, seller, or delivery noise
7. Isolated complaint with insufficient evidence

Then create a table showing:
• Complaint pattern
• Number of reviews mentioning it
• Situation in which it happens
• Consequence for the customer
• Outcome the customer wanted
• Objection our advertising must answer
• Proof required before making the claim
• Review IDs supporting the pattern

Do not treat one-star reviewers as representative of the entire market. Identify repeated patterns separately from isolated complaints.

Next, 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.

Finally, create 12 original ad experiments for my product.

For every experiment, provide:
• Hook
• Body copy
• Visual concept
• Customer problem
• Promise
• Proof needed
• Awareness stage
• Hypothesis being tested
• Result we should measure

Do 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.

What features do customers wish this product had?
Compare review sentiment for our product against the top 3 competitors — what do they praise that we get criticized for?
Has a specific complaint theme increased in frequency since our last listing update, suggesting the change backfired?
Which negative review themes correlate most strongly with declining conversion rate, versus ones that are just noise?

Review my Sponsored Products targets for the last 30 days. For each target with ACOS above [X]% or more than [N] clicks and no sales, recommend lower bid, pause or keep, with the reason.
Show the list first; only make the changes I approve.

I'm attaching four Sponsored Ads reports for the last 30 days: search term, targeting, placement and search term impression share.
Target ACoS: [x]%. TACoS goal: [y]%.
1. Sanity check: flag missing data or anything that looks wrong before you analyse.
2. Cluster search terms and targets by theme; for each cluster show spend, sales, ACoS, ROAS and top converting terms.
3. Scale-ups: terms under target ACoS with low impression share; suggest a bid increase and say why.
4. Fix or kill: targets well over target ACoS; say which to fix (bid/match type) and which to negate.
5. Placement and budget changes, then a 7-day action plan.

Here are reviews for my product and for [3] competitors: [paste or attach].
1. For each product, list complaint themes, praise themes and feature requests, with a count and one example quote each.
2. Find gaps: things buyers complain about in my product that they praise in a competitor's.
3. Write a 5-line brief: the top 3 gaps and what to change in the product or the listing for each.
Only use what's in the reviews.

For each flagged product below, write a Slack brief under 200 words using only these numbers:
ASIN, yesterday vs today price, stock status change, Buy Box seller yesterday vs today, cheapest Google Shopping offer (seller, price).
Lead with any product where the Buy Box moved to another seller. Don't guess causes.

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.

You are the strategy agent in my Amazon PPC system. You do NOT execute changes. Each day:
1. Read the bidding agent's proposed bid changes and the inventory agent's stock alerts (pasted below).
2. Resolve conflicts with this priority: inventory risk > profitability > growth. Example: never approve a bid increase on an ASIN with < 14 days of stock.
3. Ignore any recommendation driven by a single day of data unless it's confirmed by the 7-day trend.
4. Output an approval queue for me: change, agent, reason, conflict resolved (Y/N).
Bidding agent proposals: [paste]
Inventory agent alerts: [paste]

Look at [ASIN] over the last two years. How much have sales and profit gone down, and when did the drop start?
Then check sessions and conversion rate in the business reports, keyword rank history and advertising for the same period, and give me the most likely causes in order, with the evidence for each.

Before changing anything in my ad account, tell me: the goal you're optimizing for, the daily budget cap and max CPC you will stay under, and the conditions (spend with no orders, CPC above $[X], ACOS above [Y]%) that make you stop and ask me.
Then propose this week's changes as a list for my approval. Don't apply them.

Act as my Amazon store operator. Each week:
1. Audit: pull sales, sessions, conversion and ad data per ASIN for the last 7 days vs the 4 weeks before. List the 5 biggest problems with numbers.
2. Fix: for each problem, propose one specific change (listing text, bid, budget, negative keyword). Record today's baseline for the metric it should move.
3. Measure: check last week's changes against their baselines and say which helped, which didn't, and what to roll back.
Wait for my approval before making any change.

The objective is to create a set of new, high-converting listing images in the style of the backpack images. You don't have to copy the existing listing images exactly in terms of text or photos, though you may if it makes sense to do so. They should retain the same general message as the original images even if the text changes. You can use the existing photos or select new ones from Google Drive.

Here are my Sponsored Products search term and targeting reports for the last 7 days [attach]. Target ACoS [x]%.
List wasted spend (clicks over [15], no orders) and winners (ACoS under target with 2+ orders).
Draft a bulk file with only the changes: negatives, bid cuts of [20]%, bid raises of [10]%. Wait for my approval before anything is uploaded.

Here are the main and secondary images from my Amazon listing [attach]. Keep the product exactly as shown: shape, colour, logo and proportions.
Generate: 1) a hero lifestyle still of the product in use by [customer], 2) three UGC-style phone-camera scenes, 3) a clean static ad with space for one headline. Don't add features or accessories the product doesn't have.

Here are my Amazon DSP exports for the last 4 weeks: campaign report, reach and frequency, and device breakdown.
1. Show average frequency per line item by market and flag any above 7.
2. Show spend share and ROAS by device type and suggest bid-modifier changes.
3. List the 5 changes you'd make before the next flight, each with the number that supports it. Don't assume data that isn't in the files.

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.

- Never invent dimensions, materials, compatibility, certifications, ingredients,
  compliance attributes, medical claims, safety claims, age ranges, country of
  origin, warranty, or included components.
- Do not infer regulated attributes from marketing language.
- If a value is likely but not proven, mark it `Needs source` and DO NOT put it
  in the CSV draft value field.

Here are the products from the Amazon search '[keyword]': [paste title, brand, price, rating, review count].
For each: category, key feature, a one-line buyer verdict, and a 1-10 value score with the reason. Then tell me which price band has high ratings but few reviews, which could be an opening.

Check these listing images before I upload them: [attach].
For each, tell me: does it contain photorealistic AI-generated people (faces, bodies, hands)? If yes, remind me to tick 'AI-generated people' in A+ Content.
For the main image: is the background pure white, does the product fill at least 85% of the frame, and is the longest side at least 1600px? Flag anything that doesn't accurately show the real product.

Here are this week's units sold by SKU, Amazon FBA stock, 3PL stock and my reorder sheet (lead time, reorder point, case pack, vendor) [paste].
For each SKU below its reorder point: suggested order quantity rounded to case packs and the vendor.
Group by vendor and draft one short purchase-request email per vendor. Don't send anything; I'll approve first.

Here is 30 days of daily BSR for my ASINs and 3 competitors: [paste].
For each ASIN, tell me: trend direction, the days with the biggest moves, and whether they line up with my price changes, stockouts or ad spend changes [paste]. Suggest a sensible alert threshold per ASIN.

Here's the raw JSON from the FBA /fba/inventory/v1/summaries endpoint (pasted below). Write an ingest script that upserts sellerSku, asin, and the fulfillable/inbound quantities into a fba_inventory table, paginating on nextToken, capped at 20 pages. Never overwrite the cost column on the product table — COGS is entered manually and must survive a refresh.

Before reviewing any numbers, read the attached client notes and emails and write a one-line goal for each campaign (ranking, defence, profit, launch) with its target ACOS or TACoS.
Then review last 30 days' performance against those goals, not against a single ACOS target. List campaigns where the goal is unclear instead of guessing.

Here is my search term report [attach] and my per-ASIN break-even ACoS (from price, COGS and fees): [paste].
Judge every term against its ASIN's break-even ACoS, not a single target. Only consider terms with at least [15] clicks.
For each term you'd harvest into exact match, also list the negative exact to add in the source campaign.
Show the numbers behind every recommendation and don't apply anything.

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].

Here is this week's data for my Amazon business: [paste per-SKU sales, units, profit, refund rate, FBA stock, days of cover, inbound].
Fill in my weekly review template: 1) top 5 and bottom 5 SKUs by profit change vs last week; 2) SKUs with refund rate above [x]%, with the change; 3) inventory risks: under [30] days of cover or over [120]; 4) three questions the team should answer in this week's meeting.
Only use the numbers I gave you.

Here are my keyword performance export and targets: target ACOS [X]%, harvest after [N] orders, negate after [M] clicks with 0 orders.
List search terms to promote to exact match and terms to add as negatives, with the campaign and ad group for each, as a Sponsored Products bulk file I can review before upload.

I've pulled my [report type, e.g. GET_FBA_MYI_UNSUPPRESSED_INVENTORY_DATA] report as rows: [attach].
Summarize it: row count, date range, and the 10 SKUs that need attention most, with the columns that show why. Tell me how many rows you actually read before giving totals.

Apply my listing framework skill to every SKU in the attached catalog export.
Title: [formula]. Bullets: [template]. Index each search term once across title, bullets and backend. Only use claims on the approved-claims list for that SKU; if a claim isn't on it, flag it instead of writing it.
Output a flat-file-ready sheet plus a list of every SKU you flagged and why.

Here are my 23 product criteria with pass/fail thresholds: [list].
For each ASIN in the attached list, pull the data for each criterion, mark pass or fail with the number, and give an overall verdict. Put anything that fails only one criterion in a separate 'review' list instead of rejecting it.

1. Campaign Audit
"Flag every campaign with ACoS above 35% and spend above $200 in the last 30 days. Rank them by wasted spend."

2. Search Term Mining
"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."

3. Keyword Cleanup
"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."

add a badge highlighting serving count in the style of this reference image, maintaining consistent branding

From the FBA Inventory Ledger (summary and detailed), FC transfers and inventory reports I attach, build one table per SKU across all my marketplaces: starting balance, receipts, customer shipments, returns, transfers in/out, lost/damaged/found adjustments, ending balance and reserved/inbound quantities.
Then flag any SKU where units marked lost or damaged exceed units later found, or where the ending balance doesn't add up from the movements. Show the rows behind each flag.

Read only the file ads_report_[date].csv in /reports. Don't fetch or calculate anything outside it.
Summarize spend, sales, ACOS and orders by campaign versus last week's file, list the five biggest changes, and quote the exact row behind every number you mention.

Read [library repo] and the Amazon [Ads / SP-API] reference for [endpoints I need].
Build a CLI with read-only commands first: [list campaigns, pull search term report, get FBA inventory].
Then write a skill that explains when to call each command. Don't add any command that changes bids, budgets or listings until I ask.

I'm going to give the same A+ Content brief to two AI tools and compare them. Write me the brief: product [name], audience [who], 3 key benefits [list], proof points [list], brand look [colors/fonts/tone]. Ask for 5 modules with a layout description, headline, short copy and image direction for each.
Then give me a 5-point scorecard to judge the two outputs: hierarchy, brand feel, claim accuracy, mobile readability and how easy it is to build in Seller Central.

Here are photos of a returned unit, its LPN sticker and the return slip [attach].
Extract: Return Shipment ID (the part between the first and second dash after 'RA #'), LPN, Removal Order ID, FNSKU (in the column labelled ASIN).
Then write one line for Condition from what the photo shows, e.g. 'Returned missing all of the hardware needed to install the product.'
Flag any character you're not sure of (l vs I, 0 vs O).

You are planning Amazon A+ Content for [product]. Brand claims I can substantiate: [paste]. Packaging and brand colors: [describe or attach].
Plan 5 connected modules. Give each one a single job (introduce, benefits, ingredients/specs, desire, purchase confidence), a headline, 1-2 lines of copy, and an image brief that keeps the real packaging exactly as it is. Make the visual elements carry from one module to the next so it reads like one landing page. Don't add claims I didn't give you.

You are an Amazon listing specialist. Write 5 bullet points for an Amazon product listing.

Product: [description]

Top keywords to integrate: [list 8-10 keywords]

Key product strengths: [list 4-5 genuine strengths]

Top competitor complaints to address (from 1-star reviews):

1. [complaint 1]
2. [complaint 2]
3. [complaint 3]
4. [complaint 4]
5. [complaint 5]

Requirements:

- Start each bullet with a capitalized benefit phrase in bold
- First bullet must contain the primary keyword: [keyword]
- Max 250 characters per bullet
- Address at least 3 of the competitor complaints as implied advantages
- No all-caps except for the bold opener
- No exclamation points
- Write in second person ("you") not third person ("this product")

Here are my bank and card transactions since [date] [attach], and Amazon's settlement payouts and ad invoices for each brand [attach].
Match each Amazon payout and ad charge to its brand by amount and date. List everything else you can't attribute, with your best guess and why.
Output a table per brand (date, description, amount) I can paste into its ledger. Don't include anything already on the ledger [paste last 10 rows].

Here is my product truth card: [what the product is, materials, size, power/switch type, style, what's included, what it is NOT].
For each keyword in the attached Cerebro export, mark RELEVANT, PARTIAL or IRRELEVANT against the truth card, with a one-line reason. Flag terms that imply a different product type, a replacement part or a feature we don't have.

You are drafting Amazon A+ Content copy. Use only the inputs below.
Strategy brief: [keyword tiers, target customer, competitor A+ notes]
Brand voice profile: [sentence length, vocabulary level, words we use, words we never use]
Approved claims list: [paste]
For each module, write the header, body copy, image alt text and (if there's a comparison chart) row labels. Use Tier 1 keywords naturally, once each at most. Use no claim that isn't on the approved list. If the brief doesn't give enough detail for a module, write FLAG and say what's missing instead of guessing.

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.

Ask 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.

Classify 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.

Output 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.

For brand [X]: starting cash $[ ] plus Amazon's pending balance $[ ].
Using my projected monthly unit sales and reorder dates [paste], unit cost, freight per unit and payment terms ([30]% deposit at order, rest on completion), estimate monthly cash in (after referral, fulfillment, storage and returns) and cash out for the next 12 months.
Tell me the most I can distribute today while keeping cash above $[buffer] every month. Show the table and your fee assumptions.

Attached is the column layout of my distributor price list (UPC, item, dealer cost, weight, status).
Help me build a tool that: drops inactive items, items over 2 lb, items with no UPC and anything on my restricted list; looks up each remaining UPC through SP-API (catalog, competitive pricing, fees estimate); and outputs Buy Box price, fees, profit, ROI, sales rank, number of FBA offers and whether Amazon is on the listing, with BUY if ROI >= [30]% and profit >= $[4], REVIEW if close, SKIP otherwise.
Explain each step for someone who has never used an API.

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.

Here are this week's messages from my suppliers and forwarder [paste], and my open orders list (order, units, product, status, dates) [paste].
Update each order's status and the date it changed. List anything waiting on me, any supplier who hasn't replied in [3] days, and any Amazon shipment ID the forwarder still needs.

Pull last month's FBA customer returns report from Seller Central through my SP-API credentials and save it as a CSV in my reports folder.
Use Seller Central's definition of "last month" for my marketplace's time zone, and log the timestamp, report type, date range and output file name.

I am uploading a raw dataset from my Amazon Search Term Report. Act as a Data Scientist specializing in e-commerce attribution.

Task 1: Segmentation. Analyze the 'Customer Search Term' column. Identify any term that matches the string '[Your Brand Name]' or phonetic misspellings. Tag these rows as 'BRANDED'. Tag all other rows as 'NON-BRANDED'.

Task 2: Quantitative Analysis. Create a summary table comparing these two segments across the following metrics:
- Total Spend
- Total Sales
- Weighted Average CPC (Cost Per Click)
- Conversion Rate (CVR)
- ROAS

Task 3: Agent Efficiency Score. Focus specifically on campaigns where the 'Campaign Name' indicates it is an Agent-managed campaign. Calculate the percentage of the Agent's total budget allocated to 'BRANDED' terms.

Task 4: Interpretation. If the Branded Spend Share exceeds 20%, estimate the 'Cannibalization Cost' assuming that 60% of branded sales would have occurred organically without advertising. Provide a corrected 'True ROAS' for the Agent excluding these branded sales.

Every morning, check my FBA inventory and my Sponsored Products campaigns.
- 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.
- 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.
Never act without my explicit approval. Show me a table: ASIN, stock status, campaigns affected, recommended action.

These are some existing images from a listing I have for a swing on Amazon.
I need you to make one in the same with the following info:
Title: Safe, Secure Installation
Text:
One group of text titled "You'll need" with the list items: Stud finder, drill, socket wrench, pencil
Another group titled: "Instructions" with the list items: 1. Find a stud 2. Drill holes 3. Install the swivel mount 4. Hang the swing 5. Have fun swinging! Image: I attached an image of a drill - use that as the main photo for the image

Act as my Amazon launch operator for a new SKU. Step by step, and wait for my approval between steps:
1. From the keyword and competitor data I paste below, pick the 15 highest-intent keywords and explain why.
2. Write a title, 5 bullets and backend search terms using those keywords. No claims I haven't given you.
3. 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.
Product details: [paste]
Keyword + competitor data: [paste]

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.
ASIN | daily ad spend | restock ETA (days)
[paste]

Step 1 (Midjourney): [style guide prefix] [scene description] [shopper context] [lighting direction] [composition] --style raw --v 7 --ar 1:1

Step 2 (Nano Banana): Place the product from image 2 into the scene from image 1 [in the hands of the woman], matching the lighting direction and shadow from the scene.

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.

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.

Before I hire a reimbursement service, help me check its findings. From the Reimbursements report and the Inventory Ledger (detailed) I attach, list:
1. units marked lost or damaged with no reimbursement and no later 'found' event,
2. customer returns refunded more than 60 days ago that never came back to sellable stock and weren't reimbursed,
3. any reimbursement that was later reversed.
Give counts and estimated value at my unit cost: [paste cost per SKU].

Here is my list of FBA inbound shipments with units shipped and units received [paste].
List shipments that are closed with fewer units received than shipped, the missing count per SKU, and how many days since closing.
Draft the reimbursement case text for each, quoting the shipment ID and SKU.

For each of my Amazon markets [list], read the last 14 days of Sponsored Products data [attach].
Give me one page per market: spend vs target, the 5 biggest wasted-spend terms, the 5 best terms not yet in exact match, and budget-capped campaigns.
Rank markets by how urgently they need attention. Don't change anything.

Analyze the attached Search Query Performance export (all 33 columns).
1) Split branded vs unbranded queries using my brand terms: [terms].
2) For each query, show my share of impressions, clicks, add-to-carts and purchases vs the market, and the stage where my share drops most.
3) Sort queries into Defend, Growth, Kill and Monitor, with one line of reasoning each.

Here is my ASIN list with current bids, target ACoS, net price, 30-day conversion rate, FBA units available and average daily units sold [paste].
For each ASIN: days of cover, the bid from target ACoS x net price x conversion rate, and the bid after these rules: under [14] days of cover cut bids by [30]%; under [5] days pause; never go below $[min] or above $[max].
Return a table of current bid, proposed bid and the reason. Don't change anything.

Research silicone spatulas. Pull keywords for the top 3 competitors. Check trademarks on FlexiChef. Find suppliers under 500 MOQ.

Using my SP-API credentials in .env, build a small local dashboard:
1) account health summary;
2) open FBA inbound shipments with units shipped vs received;
3) best sellers by units for the last 7, 30 and 60 days.
Read-only: don't call any endpoint that changes listings, prices or shipments. Show me the endpoints you plan to use before writing code.

I'm uploading my Amazon Sponsored Products search term report (last 30 days). Analyze it and give me three lists:
1. Negate now: search terms with spend above $[x] and zero orders. Include spend wasted.
2. Watch: terms with some orders but ACoS above my target of [x]%.
3. Scale: terms with ACoS below [x]% and at least 2 orders that aren't yet targeted as exact match.
Rank each list by recoverable or growable budget, and total the wasted spend at the top.

Create an Amazon listing with a title and five bullet points for [product] that integrates all of these keywords: [20 keywords].
Keep it readable: if a keyword can't fit naturally, list it separately for backend search terms instead of forcing it in.

Pull my Sponsored Products search term report and the Brand Analytics search terms report for [ASIN] since [date of last bid change; avoid Prime Day/BFCM].
1. Search terms in broad, phrase or ASIN campaigns with at least [2] orders that I don't target as exact.
2. Organic search terms where my product was purchased or added to cart that I don't advertise on (include misspellings).
3. Targets whose ROAS is well above or below [4], ignoring anything with less than $[10] spend.
Check every suggestion against my existing targets. Don't change anything; list proposed campaigns with settings and bids for my approval.

Give me 200 niche ideas within Amazon's Home & Kitchen category, grouped by room or use, with the main search term for each so I can check it in Brand Analytics.

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.

I manage Amazon Ads and I'm not a developer. I want a small local tool that [reads my bulk file / search term report] and [shows wasted spend by campaign and suggests negatives].
Ask me questions first, then build the smallest working version. Explain how to run it, and add one feature at a time after I test.

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.

Here are my products and the top 10 competitor listings in [category]: [paste title, price, variants, pack sizes, rating, review count].
List: 1) variants (size, color, pack count) competitors sell that I don't, with how many competitors carry each; 2) bundle ideas; 3) price points where no strong listing exists; 4) positioning angles nobody owns.
Score each 1-10 for likely demand and effort, and explain the score with the data.

Here are the last 200 reviews of my product: [paste].
Tag every complaint as delivery, packaging, return, sizing, defect, expectation mismatch or other. Count each tag, give 2 example quotes, and say which ones a listing change could fix (and how) versus which need a product or packaging change.

Format the Ryse Amazon main image in the exact same layout as the Neutonic main image.

Update the copy on the Ryse box to highlight:

8G OF PROTEIN + PROBIOTICS

ZERO SUGAR

150MG OF CAFFEINE

Maintain the Ryse branding on the new box. Same layout as the Neutonic reference image.

I sell [product] on Amazon. Attached: my main image and the top 3 competitors' main images as they appear in mobile search.
Tell me why mine looks weaker at thumbnail size (product fill, aspect ratio, contrast, clutter).
Then write one image-generation prompt for Gemini that keeps my product exactly as photographed and fixes those issues, plus 2 alternative concepts I can poll against it.

Using the Amazon Ads MCP tools, draft (do not launch) a Sponsored Products campaign for ASIN [ASIN] in the [marketplace] marketplace:
- Campaign name: [name], daily budget $[x]
- One auto ad group and one manual exact ad group with these keywords: [list]
- Starting bid $[x]
Show me the full structure for review. Only create it after I reply "approve".

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.

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.

Using my real product photo, put the product in this setting: [scene]. Keep the product's shape, color, material and label exactly as in the photo, and don't add any text to the image (I'll add text in Canva).
Make 3 versions with different backgrounds: [options].

Here is one photo of my product: [attach]. Product facts: [ingredients/materials, size, key claims I can legally make].
Create a 7-image Amazon set, one at a time: 1) main image on pure white, product filling 85% of the frame; 2) and 3) benefit infographics; 4) comparison chart vs a generic alternative; 5) 'Why choose [brand]' trust panel; 6) lifestyle shot with [target customer]; 7) pack shot.
Keep the product, label and colors exactly as in my photo. Use only the facts I gave you.

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.

The application should work as follows:

1. User fills out a web form with the following info:
- Text entry: Describe the product.
- Text entry: How would you describe the desired brand look and feel?
- Text entry: What are the main pieces of information you wish to convey about the product?
- 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.

2. 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.

3. 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.

Other context:
1. This is going to run locally on my machine so no concerns about auth, security, etc.
2. All images generated should be in 2k resolution and 1:1 aspect ratio

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.

Using the attached product photo as the exact product (don't change shape, colour or label), make an Amazon infographic, 2000x2000, white background.
Headline: [benefit]. Three callouts with icons: [feature 1], [feature 2], [feature 3]. Show [dimension] with a measurement line.
Text in [Spanish/English], large enough to read on a phone.

Create a clean photo rendering of this product to be used as the primary image on an Amazon listing. Pure white background, product filling most of the frame, clean, modern, aesthetic.

Then, one image at a time, using the same product exactly as shown (don't change the label, cap or bottle shape):
1. A graphic showing [4] ways to use it: [uses].
2. A benefits graphic with these benefits: [benefits].
3. A features graphic: [size, ingredients, what's included].
4. A step-by-step instructional: [steps].
5. A lifestyle shot: [scene].
6. A size comparison: [sizes].
If any label text comes out garbled, tell me so I can fix it rather than guessing.

Here is my approved main image for [product] in [color A]: [attach]. Create the same image for these variations: [list colors/patterns], changing only the [color/pattern] of the product.
Keep the angle, lighting, shadow, background and every other detail identical. Don't change the size or shape.
After each one, tell me anything that differs from the original besides the requested change.

Here is Amazon's return-insight summary for my ASIN and my current listing: [paste both].
1. List each piece of information buyers say is missing or wrong (size, material, compatibility, what's in the box), with how often it comes up.
2. For each, show where it should go in the listing (title, bullet, image, A+ or attribute) and draft the exact text.
3. Flag anything the summary claims that my own return comments and reviews don't support.

Combine these two images: the model photo and the product shot. Show the model using the [product] in a [theme: bright kitchen / outdoor trail / minimalist studio] scene, natural light, brand colours [hex].
Keep the product's label, shape and colours exactly as in the product shot. Square, 2000x2000.

System instructions: You make Amazon listing images. Keep the product exactly as it appears in the photo I upload: same shape, colors, logo and text. Never add features it doesn't have.

Task: Using this phone photo of my [product], create [a white-background image of a person wearing/using it | an infographic with the headline "[headline]" and these 3 points: [points]].
(Set aspect ratio 1:1 and the highest resolution in AI Studio's settings rather than in this prompt. Run the same prompt in 3 chats and keep the best.)

Build an n8n workflow that runs every morning at 7:00:
1) gets an SP-API access token from my LWA refresh token;
2) calls the FBA Inventory API (getInventorySummaries, details=true) for marketplace [ATVPDKIKX0DER], following nextToken;
3) writes one row per SKU to the Google Sheet [name], tab 'Stock': date, SKU, ASIN, fulfillable, inbound, reserved, unfulfillable;
4) highlights any SKU whose fulfillable count is under [30].

Here is my product [name, photo] and the top 5 competitor listings on Amazon.[de] for [keyword] (links or screenshots).
1) List what each competitor's images show and what none of them show.
2) Write a script for 7 images: main image, 3 infographics, 3 lifestyle shots, each with its purpose and the USP or emotional trigger it carries.
3) For each image, write a Nano Banana Pro prompt with lighting, camera angle and setting, keeping my uploaded product photo unchanged.

Here are 1 and 2-star reviews of [product]: [paste].
For each review return JSON: sentiment_score (1-5), category (Quality, Design, Shipping, Instructions, Other), summary (one line), improvement (one concrete change to the product, packaging or listing).
Then group by category and tell me the 3 fixes that would address the most reviews.

Here's a lifestyle photo of my [product]. Write 5 short video prompts (10-15 seconds, portrait) that keep this exact scene and product, each with one simple motion, lighting direction and camera behaviour.
Don't change the product's shape, colour, label or size.

In Amazon Ads, open Sponsored Products campaign [name]. Go to the Targeting tab. For each keyword with more than [N] orders in the last 14 days, raise the bid by 10%, rounding to the cent.
Before saving, show me the list of keywords with old and new bids and wait for my OK.

I'll give you a product photo and my product facts. Generate: one main image on pure white, two lifestyle images with [scene], and one infographic layout with space for [3] callouts I'll add in Photoshop.
Don't add text to the product itself and don't change its shape, color or label. List anything you weren't sure about so I can check it.

(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."

Using the attached product photo, make an Amazon main image: pure white background (RGB 255,255,255), product filling about 85% of the frame, even studio lighting, label sharp and legible.
Don't change the product's shape, colours, label text or proportions. Square, 2000x2000.

From this phone photo of [product], make: 1) an Amazon main image on pure white (RGB 255,255,255), product filling ~85% of the frame; 2) a lifestyle image in [setting] at true scale ([dimensions]); 3) an infographic with 3 short callouts: [benefit 1], [benefit 2], [benefit 3].
Keep the product's shape, colours and label unchanged.

I've uploaded a photo of my product. Don't describe or redesign the product - use the photo as the only reference.
Write 5 image prompts for Amazon secondary images in the 5 use cases most relevant to it: [or name the setting, e.g. office desk]. For each, describe only the environment, mood, lighting and composition, square 1:1, maximum quality.
Real size for scale: [dimensions].

Build an n8n workflow that runs at 6:00 every day:
1) download the distributor feed from [URL] (CSV: SKU, cost, quantity);
2) for each SKU in my Amazon FBM catalog, set quantity = distributor quantity minus a buffer of [2], and price = cost x [1.35] rounded to .99, within my min/max in the sheet [name];
3) send the changes as one SP-API JSON_LISTINGS_FEED and email me the processing report.
Skip any SKU missing from the feed and list it in the email.

(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."

Here is last week's Sponsored Products search term report (CSV). Target ACOS [30]%.
List: 1) search terms with at least [15] clicks and no orders, by spend; 2) converting search terms that aren't keywords in any campaign; 3) terms where broad or phrase is converting but exact isn't running; 4) any campaign whose spend or ACOS moved more than 25% from the prior week.
Return a table for each, numbers only, no advice paragraph.

Research this product idea for Amazon US: [product]. I've attached [a Black Box / Product Opportunity Explorer export] - use it for sales and search figures and say which numbers come from it and which from the web.
Cover: market size and trend, seasonality, the main competitors and their price points, the top 5 buyer complaints in reviews (with quotes), and what a better version would need.
Don't name 'top sellers' unless they're in my file.

Rewrite these bullets for clarity and keywords: [paste]. Keep every factual claim I already make and add no new ones.
Don't use: antibacterial, antimicrobial, antiviral, kills germs/bacteria/mold/mildew, sanitizes, disinfects, non-toxic, or any health or medical claim.
After the rewrite, list every word or phrase that wasn't in my original so I can check it.

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.
1. Find units that were lost, damaged, or missing at fulfillment centers or inbound and not later found or reimbursed.
2. Find customer returns refunded more than 45 days ago where the unit was never returned or reimbursed.
3. Flag any fee charges that look inconsistent with the product's size tier.
Output a table per finding (FNSKU, date, quantity, reason, evidence row) and draft the Seller Support message for each.

Here is my Sponsored Products search term report for the last 30 days: [attach]. Target ACOS: [x]%. Minimum clicks before judging a term: [15].
1. Terms with spend and zero orders over the click minimum: lower bid by [30]% or propose as negative exact.
2. Terms with ACOS under target and at least [2] orders: raise bid by [15]%.
3. Output an Amazon Ads bulk sheet with only the rows to change (Operation = Update), plus a summary of how many changes and why.
Don't change anything with fewer clicks than the minimum.

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

(Use inside Seller Central.) Before accepting Enhance My Listing suggestions, paste the old and suggested versions into any AI assistant and ask:
"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."

You are analyzing my last 30 days of Amazon Sponsored Products reports (search term, targeting, campaign, placement, budget).
Target ACOS: [x]%.
Return: 1) search terms to add as negatives (spend, clicks, 0 orders); 2) search terms to harvest into exact match; 3) bid changes by target, max ±[20]%, with the reason; 4) placement adjustments; 5) campaigns limited by budget that are under target ACOS.
Recommendations only, with the numbers behind each.

Here is the text of the Amazon Best Sellers page for [category] [paste].
Return a table: rank, title, brand, price, rating, review count and any deal or coupon shown.
Then tell me which price bands and features dominate the top 20, and where a product with [my product's key feature] could fit.

Here is Amazon's violation notice: [paste]. Here is the list of documents I'm attaching, with a one-line summary of each: [list].
Map every concern or request in the notice to the document that answers it. Flag any concern with no document, any document that doesn't match the root cause, and anything that sounds like a promise I can't prove.
Don't write new facts; ask me questions instead.

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:
1. Match each return label to the correct customer order (explain the match).
2. 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.
3. Draft a polite follow-up appeal for if Amazon offers less than the item's value.
Only use facts from my attachments.

Here are related keywords Amazon autocomplete returned for my seed terms: [paste].
Remove duplicates and anything irrelevant to [product]. Group the rest by intent (feature, use case, audience, competitor brand) and mark which I should test in exact match, which in phrase, and which to add as negatives.

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.

Here are my current five bullets and my top 30 relevant keywords with search volume.
Rewrite the bullets so every keyword not already in the title appears once, naturally, without changing any product claim. Keep each bullet under 250 characters and list which keywords went into which bullet.

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.

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.

(In Canva, with my product photos uploaded)
For each photo: remove the background and place the product on pure white (RGB 255,255,255), centered, filling about 85% of a 2000x2000 square. Don't change the product's color or shape. Export as JPG named [SKU]-main.

(Photoshop Generative Fill or a similar AI editor, on my existing product photo)
Keep the product pixels exactly as they are. Replace only the background with: [a cozy living room at Christmas, soft warm light, a decorated tree out of focus]. Match the lighting and add a natural shadow under the product. Make 3 versions.

/imagine [scene: e.g. a woman in her 30s hiking a forest trail, holding an empty space at chest height where a water bottle will go], natural morning light, editorial photo, shallow depth of field --style raw --ar 1:1
(Then composite my real product photo into the scene in Photoshop and match the light and shadow.)

Seller Support denied my request to change these attributes on ASIN [ASIN]: [attribute: current value -> correct value]. Their reply: [paste].
I'm the brand owner (Brand Registry) and here's my evidence: [packaging photo, spec sheet, manufacturer page].
Write a reply under 120 words that restates exactly which attributes to change, cites the evidence, and asks them to apply the brand owner's contribution. No apologies or background story.

I have just uploaded [number] Amazon reviews for [product]. Collect and analyze all product reviews together and answer the following questions:
1. After analyzing the data, what notable insights can you identify?
2. What are the five main pain points customers experience with these products, ranked in order of urgency?
3. What are the seven most valuable benefits customers derive from these products, ranked in order of relevance?
4. For which occasions or events are these products mainly purchased, based on priority?

I've uploaded an Excel file with two sheets: my Sponsored Products Search Term Report (last 60 days) and a map of source campaign -> destination exact-match campaign and ad group.
Harvest every customer search term with at least [2] orders and ACOS under [30]% that isn't already a keyword in its destination ad group.
Return an Amazon Ads Bulk Operations sheet that: creates each as an exact keyword in the destination ad group at a bid of [the term's CPC + 10%], and adds it as a negative exact in the source ad group.
List the rows first so I can check them, then give me the file.

Based on the below list of customer suggestions, what different ways can I improve upon my Yoga Mat product?

What's my profit by SKU this month?

Which of my FBA SKUs are at risk of stocking out in the US? Use Amazon's planning numbers where you can, rank by days of cover, and check whether my inbound shipments land before each at-risk SKU runs out.

Write a JSON spec for each of my [7] listing images, then generate them with Nano Banana Pro.

For each image include:
- "slot": main / secondary
- "product_truth": what must stay exactly true (shape, color, size, label, what's in the box): [facts]
- "amazon_rules": for the main image, pure white background, product only, no text or badges
- "search_intent": the search query or buyer question from my Brand Analytics data this image answers: [queries]
- "do_not_invent": features, claims, certifications or accessories that must not appear
- "composition": layout, angle, props

Generate from my raw photo following each spec exactly, and list anything you couldn't follow.

Act as my Seller Central case desk. Read my last 10 buyer messages and account-health notices [paste or attach].
For each: what it is, the deadline under the 24-hour response rule, policy risk (low/medium/high) and the next step.
Then draft replies for the ones that need one. Don't send anything; I'll review each draft.

Every Monday at 7:00, use DataDoe to build my P&L by marketplace for the week just closed: net profit after Amazon fees, ad spend and landed cost, with margin %.
Every Tuesday at 8:00, list campaigns that spent more than 2x their usual daily spend yesterday, with the search terms behind the spend.
Post results in this chat. Ask before any DataDoe Action that would change something on Amazon.

Act as my Fulfillment Manager.

Use only:
- A return-reason export you upload

When to run: Monthly after the first month of reasons.

Ask me first:
- Top ASINs by return cost.
- Which reasons you already know are photo versus sizing.

Give me:
- Proposed listing clarifications, package inserts, and sizing guidance.

My approval is required for:
- Listing edits.
- Any refund-policy change.

Never:
- Change the live listing.
- Change the refund policy.

Start with: Classify one month of return reasons together, choose an evidence-backed first fix, and retain the approved monthly cadence.

Build me a Python script that connects to Amazon SP-API and pulls: yesterday's ordered revenue and units from the Sales API, any ASINs with fewer than 14 days of inventory from FBA Inventory Planning, and my top 5 campaigns by spend from yesterday using the Amazon Ads API. Output a clean summary to the terminal. Read my credentials from a .env file in the same directory.

Act as my Ads Manager.

Use only:
- Amazon Ads for Sponsored Products campaigns, search-term reports, and ACoS

When to run: A daily recap at 07:00.

Ask me first:
- Target ACoS by SKU family.
- Brand-defense terms you will never negative.
- Which ASINs are in stock this week.

Give me:
- Proposed exact, phrase, and broad groupings, with negative harvests and bid strategies and the math shown.
- A daily 07:00 recap.

My approval is required for:
- Changing a bid.
- Archiving a campaign.
- Raising a budget.

Never:
- Negative a brand-defense term.
- Spend on an ASIN that is out of stock this week.

Start with: Model one ASIN family without publishing, review its structure together, and retain the approved daily recap format.

Act as my Listing Writer.

Use only:
- Brand Registry A+, read only
- Screenshots of the current A+ editor state when its layout is difficult to describe

When to run: Per ASIN family after the first supervised build.

Ask me first:
- ASINs in the family
- Claims you can legally chart
- Which competitor you may name

Give me:
- A module-by-module brief for the A+ editor
- A product comparison whose cells use only verified specifications

My approval is required for:
- Submitting A+ Content
- Changing a price

Never:
- Invent a spec in a comparison chart
- Name a competitor you did not allow

Start with: Plan one ASIN family beside you and retain its approved module order as the working pattern.

Act as my Amazon drafts.

Use only:
- A CSV you drop from your own machine

When to run: On demand after the first schedule.

Ask me first:
- Timezone.
- Target ACoS by hour band.

Give me:
- A schedule you can type into ads.
- A note when the hourly file is too thin to invent a night-and-day split.

Do not:
- Signing in to ads or the seller console
- Uploading a schedule or enabling a new campaign

Start with: One supervised schedule so the hour bands match when you actually convert.

Act as my Amazon drafts.

Use only:
- Documents you already have on your machine

When to run: On demand after the first packet.

Ask me first:
- The gated category.
- Whether you have a test buy.

Give me:
- A packet draft with invoices, photos, and test-buy notes.
- Application answers in a Doc, with blanks where only you know the invoice number.

Do not:
- Signing in to the seller console
- Uploading or filing the application

Start with: Assemble one packet together and verify its invoice timing and product imagery against the category checklist.

Which SKUs have afn-fulfillable-quantity below 30 days of average daily units ordered?

Use the wasted-ad-spend-dashboard skill on [brand] for the last 30 days. Rank by spend, draft a negatives list for my approval, and log what we did under `initiatives/wasted-spend-[date]/`.

Act as my Amazon drafts.

Use only:
- A keyword export you drop from your own machine

When to run: On demand after the first ASIN is built together.

Ask me first:
- Byte budget for the backend block.
- Banned terms.

Give me:
- A paste-ready backend string with a byte count.
- A discards list, including terms already in the title you pasted.

Do not:
- Signing in to the seller console
- Uploading the string or enabling ads

Start with: One supervised ASIN so the priority order is yours.

Act as my Amazon drafts.

Use only:
- A CSV drop folder for reports you download yourself

When to run: Weekly after the first supervised file.

Ask me first:
- Competing brands you care about.
- The category under review.

Give me:
- A memo tagged steal, defend, or ignore.
- The queries attached to each call.

Do not:
- Signing in to pull the report
- Uploading listing or ad changes

Start with: Last week's file together so the steal-or-defend bar is real.

Act as my Amazon drafts.

Use only:
- Mark and invoice files you drop from your own machine

When to run: On demand after the first checklist.

Ask me first:
- Brand name.
- Marketplace.

Give me:
- A documents checklist and trademark-status notes.
- A warning for expired-looking evidence or a trademark class that appears inconsistent with the product.

Do not:
- Signing in to Brand Registry or the seller console
- Uploading or submitting the pack

Start with: One supervised checklist so a required document is not missed.

Act as my Amazon drafts.

Use only:
- A SKU cost Sheet
- A coupon screenshot you paste if you already run one

When to run: On demand after the first two-week calendar.

Ask me first:
- Whether you already run coupons.
- The clip rate you will tolerate.

Give me:
- A two-week coupon calendar with stacking rules.
- A warning when same-day promotion stacking would push a SKU below its sensible landed price.

Do not:
- Signing in to the seller console
- Uploading or enabling a coupon

Start with: One supervised two-week calendar so the stacking rules are real.

Act as my Fulfillment Manager.

Use only:
- A PO and SKU Sheet
- Any existing prep SOP
- Restriction notes you paste from an export

When to run: On each inbound you plan.

Ask me first:
- Partnered carrier versus self-ship.

Give me:
- A pack list and label map for the prep room.

My approval is required for:
- Creating the inbound shipment.

Never:
- Create the inbound from the pack list.

Start with: Prep one inbound on paper with you, then save the pack.

Act as my Fulfillment Manager.

Use only:
- A cost Sheet
- The marketplace list you already sell on

When to run: On demand as a planner after the first two-country playbooks.

Ask me first:
- Origin.
- Brand-registry status.
- Whether you will use a local VAT number.

Give me:
- A country playbook with a landed-cost sketch.
- A note on Remote Fulfillment versus a local FBA inbound.

My approval is required for:
- Opening a marketplace.
- Publishing any listing change.

Never:
- Open a marketplace from the playbook.
- Push a listing from the planner.

Start with: Playbook two countries with you so the regulatory notes are ones you will actually file, then save the planner.

Design a weekly n8n workflow for my Amazon [Vendor Central] account:
- SP-API: request the [GET_VENDOR_SALES_REPORT] for last week, poll until DONE, download and decompress it;
- Amazon Ads API: campaign report for the same week (sales, spend, impressions, clicks, orders, CPC);
- roll child ASINs up to parent using the mapping in the 'Parents' tab;
- update the existing rows in my sheet [name] where the week-start date matches (don't append duplicates);
- email me a summary table every Monday at 6:00.

Draft an Amazon Seller Support case for this FBA inbound shipment discrepancy.
Shipment ID: [FBA...], SKU [x], units shipped [n], units received [n], already reimbursed [n].
State the facts only, attach-ready: shipment ID, SKU, quantities, the date the shipment closed, and a request to investigate and reimburse the [n] missing units. No more than 120 words.

Classify this email from an Amazon seller client into exactly one category: PPC, Listing, Account Health, Reimbursement, FBA Shipment, Inventory, Brand Registry, Billing, Other.
Return JSON: {"category": "...", "client": "...", "asins": [...], "urgency": "high|normal", "summary": "<=25 words"}.
Mark urgency high only for suspensions, listing deactivations or stranded inventory.
Email: [paste]

You're an Amazon listing optimization expert. Here's my current listing, competitor listings, my reviews, competitor reviews, and top search terms. Rewrite my title, bullets, and description. Prioritize keywords that appear in search terms but are missing from my listing. Use language from positive reviews. Address concerns from negative reviews preemptively. Keep bullets under 200 characters each. Make the title under 200 characters.