# Amazon Product Opportunity Gap Scorer (`rotvuvo/amazon-product-opportunity-gap-scorer`) Actor Score imported Amazon/product-research rows into opportunity bands, gap types, risk flags, missing evidence, and sourcing next actions. Use it after an Amazon scraper, Seller Central export, research-tool CSV, spreadsheet, or manual research. - **URL**: https://apify.com/rotvuvo/amazon-product-opportunity-gap-scorer.md - **Developed by:** [Wit Nomad](https://apify.com/rotvuvo) (community) - **Categories:** E-commerce, Marketing - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Amazon Product Opportunity Gap Scorer Amazon Product Opportunity Gap Scorer is an ingest-first Apify Actor for imported Amazon and product-research rows. It turns rows from existing Apify Amazon scrapers, Seller Central or Product Opportunity Explorer exports, research-tool exports, spreadsheets, and manual research into deterministic opportunity scores, risk flags, missing evidence, and next sourcing actions. It is a scoring layer, not another Amazon crawler. ### What it does - Scores each imported product row from 0 to 100. - Classifies the main gap type: high demand with low competition, quality gap, margin gap, data gap, risk blocked, crowded, or balanced. - Normalizes common aliases such as `asin`, `ASIN`, `productId`, `title`, `productTitle`, `price`, `buyBoxPrice`, `reviews`, `ratingsCount`, `bsr`, `sellerCount`, `estimatedMonthlySales`, `profitMargin`, and `productUrl`. - Dedupes duplicate rows by ASIN and marketplace, while keeping the same ASIN in different marketplaces separate. - Emits fit signals, risk flags, missing evidence, a score breakdown, and a short next action for research reviews or sourcing dashboards. ### What it does not do - It does not scrape Amazon. - It does not use Seller Central login. - It does not call SP-API, Product Advertising API, or paid product-research APIs. - It does not use proxies. - It does not create official Amazon sales, revenue, fee, or profit estimates unless those fields are supplied by your input. - It does not guarantee profitability, product-market fit, launch success, ad performance, sourcing success, compliance, IP safety, patent clearance, or legal safety. It does not guarantee legal clearance or replace professional legal review. ### Input Use `items` mode with a non-empty `items` array. Optionally include `sellerProfile` to tune target marketplace, categories, keywords, margin, review count, seller count, weight, and risk keywords. ```json { "mode": "items", "items": [ { "asin": "B0GOOD123", "title": "Silicone drawer organizer set", "marketplace": "US", "category": "Kitchen", "price": 29.99, "reviewCount": 142, "rating": 4.2, "bestSellerRank": 1850, "sellerCount": 2, "monthlySales": 1200, "marginPercent": 38, "url": "https://www.amazon.com/dp/B0GOOD123" } ], "sellerProfile": { "targetMarketplace": "US", "targetCategories": ["Kitchen", "Home"], "targetKeywords": ["silicone", "organizer"], "minMarginPercent": 25, "maxReviewCount": 500, "maxSellers": 5, "riskKeywords": ["patent", "hazmat", "gated", "fragile", "seasonal"] }, "scrapedAt": "2026-07-08T00:00:00.000Z" } ```` Keyword and category matching is intentionally conservative. The Actor matches profile terms against product titles, categories, features, descriptions, complaint text, and risk fields. It does not infer keyword fit from source URLs alone. ### Output Each dataset item is one scored product opportunity row with normalized product fields, `opportunityScore`, `opportunityBand`, `priority`, `gapType`, `fitSignals`, `riskFlags`, `missingEvidence`, `scoreBreakdown`, `nextAction`, `researchNote`, `confidence`, `warnings`, and deterministic `scrapedAt` metadata. ### Local usage ```bash npm install npm test node src/main.js --input sample-input.json --output output.json ``` For the full factory verification, run the repository local quality gate against this Actor path. # Actor input Schema ## `mode` (type: `string`): Input mode. This version supports imported Amazon/product-research rows only. ## `items` (type: `array`): Rows from existing Apify Amazon scrapers, Seller Central exports, Product Opportunity Explorer exports, research-tool exports, spreadsheets, or manual research. Common aliases include asin/ASIN/productId, title/name/productTitle, marketplace/country/domain, price/currentPrice/buyBoxPrice, rating/stars, reviewCount/reviews, bestSellerRank/bsr/rank, sellerCount/sellers/offerCount, monthlySales, marginPercent, url/productUrl/sourceUrl, features, complaints, and riskNotes. ## `sellerProfile` (type: `object`): Optional sourcing preferences used for deterministic fit and risk checks. Keyword/category matching uses title, category, feature, description, complaint, and risk fields only; source URLs are not used for keyword fit. ## `maxItems` (type: `integer`): Maximum imported rows to process. ## `scrapedAt` (type: `string`): Optional ISO timestamp for deterministic output metadata. This Actor does not scrape; when omitted or invalid, the runtime uses this fixed default reference timestamp. ## Actor input object example ```json { "mode": "items", "items": [ { "asin": "B0GOOD123", "title": "Silicone drawer organizer set", "marketplace": "US", "category": "Kitchen", "price": 29.99, "reviewCount": 142, "rating": 4.2, "bestSellerRank": 1850, "sellerCount": 2, "monthlySales": 1200, "marginPercent": 38, "url": "https://www.amazon.com/dp/B0GOOD123", "features": [ "silicone", "dishwasher safe", "space saving" ] }, { "ASIN": "B0QUAL123", "productTitle": "Stackable pantry organizer bins", "country": "US", "department": "Home", "currentPrice": 24.5, "ratingsCount": 310, "stars": 3.7, "rank": 4200, "sellers": 2, "estimatedMonthlySales": 600, "profitMargin": 31, "complaints": "Customers complain about cracked lids and weak handles.", "productUrl": "https://www.amazon.com/dp/B0QUAL123" }, { "asin": "B0RISKY", "title": "Seasonal fragile glass diffuser with patent note", "marketplace": "US", "category": "Home", "price": 32, "reviewCount": 90, "rating": 4.1, "bestSellerRank": 2100, "sellerCount": 1, "monthlySales": 900, "marginPercent": 42, "riskNotes": "Potential patent issue, fragile packaging, seasonal demand.", "url": "https://www.amazon.com/dp/B0RISKY" } ], "sellerProfile": { "targetMarketplace": "US", "targetCategories": [ "Kitchen", "Home" ], "avoidCategories": [ "Supplements" ], "targetKeywords": [ "silicone", "organizer" ], "avoidKeywords": [ "medical" ], "minPrice": 15, "maxPrice": 60, "minMarginPercent": 25, "maxReviewCount": 500, "minRatingGap": 0.3, "maxSellers": 5, "maxWeightLb": 3, "riskKeywords": [ "patent", "hazmat", "gated", "fragile", "seasonal" ], "capitalBudget": 5000 }, "maxItems": 500, "scrapedAt": "2026-07-08T00:00:00.000Z" } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "mode": "items", "items": [ { "asin": "B0GOOD123", "title": "Silicone drawer organizer set", "marketplace": "US", "category": "Kitchen", "price": 29.99, "reviewCount": 142, "rating": 4.2, "bestSellerRank": 1850, "sellerCount": 2, "monthlySales": 1200, "marginPercent": 38, "url": "https://www.amazon.com/dp/B0GOOD123", "features": [ "silicone", "dishwasher safe", "space saving" ] }, { "ASIN": "B0QUAL123", "productTitle": "Stackable pantry organizer bins", "country": "US", "department": "Home", "currentPrice": 24.5, "ratingsCount": 310, "stars": 3.7, "rank": 4200, "sellers": 2, "estimatedMonthlySales": 600, "profitMargin": 31, "complaints": "Customers complain about cracked lids and weak handles.", "productUrl": "https://www.amazon.com/dp/B0QUAL123" }, { "asin": "B0RISKY", "title": "Seasonal fragile glass diffuser with patent note", "marketplace": "US", "category": "Home", "price": 32, "reviewCount": 90, "rating": 4.1, "bestSellerRank": 2100, "sellerCount": 1, "monthlySales": 900, "marginPercent": 42, "riskNotes": "Potential patent issue, fragile packaging, seasonal demand.", "url": "https://www.amazon.com/dp/B0RISKY" } ], "sellerProfile": { "targetMarketplace": "US", "targetCategories": [ "Kitchen", "Home" ], "avoidCategories": [ "Supplements" ], "targetKeywords": [ "silicone", "organizer" ], "avoidKeywords": [ "medical" ], "minPrice": 15, "maxPrice": 60, "minMarginPercent": 25, "maxReviewCount": 500, "minRatingGap": 0.3, "maxSellers": 5, "maxWeightLb": 3, "riskKeywords": [ "patent", "hazmat", "gated", "fragile", "seasonal" ], "capitalBudget": 5000 }, "scrapedAt": "2026-07-08T00:00:00.000Z" }; // Run the Actor and wait for it to finish const run = await client.actor("rotvuvo/amazon-product-opportunity-gap-scorer").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "mode": "items", "items": [ { "asin": "B0GOOD123", "title": "Silicone drawer organizer set", "marketplace": "US", "category": "Kitchen", "price": 29.99, "reviewCount": 142, "rating": 4.2, "bestSellerRank": 1850, "sellerCount": 2, "monthlySales": 1200, "marginPercent": 38, "url": "https://www.amazon.com/dp/B0GOOD123", "features": [ "silicone", "dishwasher safe", "space saving", ], }, { "ASIN": "B0QUAL123", "productTitle": "Stackable pantry organizer bins", "country": "US", "department": "Home", "currentPrice": 24.5, "ratingsCount": 310, "stars": 3.7, "rank": 4200, "sellers": 2, "estimatedMonthlySales": 600, "profitMargin": 31, "complaints": "Customers complain about cracked lids and weak handles.", "productUrl": "https://www.amazon.com/dp/B0QUAL123", }, { "asin": "B0RISKY", "title": "Seasonal fragile glass diffuser with patent note", "marketplace": "US", "category": "Home", "price": 32, "reviewCount": 90, "rating": 4.1, "bestSellerRank": 2100, "sellerCount": 1, "monthlySales": 900, "marginPercent": 42, "riskNotes": "Potential patent issue, fragile packaging, seasonal demand.", "url": "https://www.amazon.com/dp/B0RISKY", }, ], "sellerProfile": { "targetMarketplace": "US", "targetCategories": [ "Kitchen", "Home", ], "avoidCategories": ["Supplements"], "targetKeywords": [ "silicone", "organizer", ], "avoidKeywords": ["medical"], "minPrice": 15, "maxPrice": 60, "minMarginPercent": 25, "maxReviewCount": 500, "minRatingGap": 0.3, "maxSellers": 5, "maxWeightLb": 3, "riskKeywords": [ "patent", "hazmat", "gated", "fragile", "seasonal", ], "capitalBudget": 5000, }, "scrapedAt": "2026-07-08T00:00:00.000Z", } # Run the Actor and wait for it to finish run = client.actor("rotvuvo/amazon-product-opportunity-gap-scorer").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "mode": "items", "items": [ { "asin": "B0GOOD123", "title": "Silicone drawer organizer set", "marketplace": "US", "category": "Kitchen", "price": 29.99, "reviewCount": 142, "rating": 4.2, "bestSellerRank": 1850, "sellerCount": 2, "monthlySales": 1200, "marginPercent": 38, "url": "https://www.amazon.com/dp/B0GOOD123", "features": [ "silicone", "dishwasher safe", "space saving" ] }, { "ASIN": "B0QUAL123", "productTitle": "Stackable pantry organizer bins", "country": "US", "department": "Home", "currentPrice": 24.5, "ratingsCount": 310, "stars": 3.7, "rank": 4200, "sellers": 2, "estimatedMonthlySales": 600, "profitMargin": 31, "complaints": "Customers complain about cracked lids and weak handles.", "productUrl": "https://www.amazon.com/dp/B0QUAL123" }, { "asin": "B0RISKY", "title": "Seasonal fragile glass diffuser with patent note", "marketplace": "US", "category": "Home", "price": 32, "reviewCount": 90, "rating": 4.1, "bestSellerRank": 2100, "sellerCount": 1, "monthlySales": 900, "marginPercent": 42, "riskNotes": "Potential patent issue, fragile packaging, seasonal demand.", "url": "https://www.amazon.com/dp/B0RISKY" } ], "sellerProfile": { "targetMarketplace": "US", "targetCategories": [ "Kitchen", "Home" ], "avoidCategories": [ "Supplements" ], "targetKeywords": [ "silicone", "organizer" ], "avoidKeywords": [ "medical" ], "minPrice": 15, "maxPrice": 60, "minMarginPercent": 25, "maxReviewCount": 500, "minRatingGap": 0.3, "maxSellers": 5, "maxWeightLb": 3, "riskKeywords": [ "patent", "hazmat", "gated", "fragile", "seasonal" ], "capitalBudget": 5000 }, "scrapedAt": "2026-07-08T00:00:00.000Z" }' | apify call rotvuvo/amazon-product-opportunity-gap-scorer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=rotvuvo/amazon-product-opportunity-gap-scorer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amazon Product Opportunity Gap Scorer", "description": "Score imported Amazon/product-research rows into opportunity bands, gap types, risk flags, missing evidence, and sourcing next actions. Use it after an Amazon scraper, Seller Central export, research-tool CSV, spreadsheet, or manual research.", "version": "0.1", "x-build-id": "6Aa7fad5tzajzE5NJ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/rotvuvo~amazon-product-opportunity-gap-scorer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-rotvuvo-amazon-product-opportunity-gap-scorer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/rotvuvo~amazon-product-opportunity-gap-scorer/runs": { "post": { "operationId": "runs-sync-rotvuvo-amazon-product-opportunity-gap-scorer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/rotvuvo~amazon-product-opportunity-gap-scorer/run-sync": { "post": { "operationId": "run-sync-rotvuvo-amazon-product-opportunity-gap-scorer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "items" ], "properties": { "mode": { "title": "Mode", "enum": [ "items" ], "type": "string", "description": "Input mode. This version supports imported Amazon/product-research rows only.", "default": "items" }, "items": { "title": "Imported product rows", "minItems": 1, "type": "array", "description": "Rows from existing Apify Amazon scrapers, Seller Central exports, Product Opportunity Explorer exports, research-tool exports, spreadsheets, or manual research. Common aliases include asin/ASIN/productId, title/name/productTitle, marketplace/country/domain, price/currentPrice/buyBoxPrice, rating/stars, reviewCount/reviews, bestSellerRank/bsr/rank, sellerCount/sellers/offerCount, monthlySales, marginPercent, url/productUrl/sourceUrl, features, complaints, and riskNotes.", "items": { "type": "object" } }, "sellerProfile": { "title": "Seller profile", "type": "object", "description": "Optional sourcing preferences used for deterministic fit and risk checks. Keyword/category matching uses title, category, feature, description, complaint, and risk fields only; source URLs are not used for keyword fit." }, "maxItems": { "title": "Maximum rows", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum imported rows to process.", "default": 500 }, "scrapedAt": { "title": "Reference timestamp", "type": "string", "description": "Optional ISO timestamp for deterministic output metadata. This Actor does not scrape; when omitted or invalid, the runtime uses this fixed default reference timestamp.", "default": "2026-07-08T00:00:00.000Z" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```