# Amazon Review Monitor — Fake-Review Spike Alerts (Vigia) (`om_kh/vigia-amazon-review-intelligence`) Actor Track Amazon product reviews for your ASINs or competitors' and get alerted only on actionable feedback: 1-2 star reviews, urgent complaints, and sudden review-volume spikes (a fake-review-farm signal). - **URL**: https://apify.com/om\_kh/vigia-amazon-review-intelligence.md - **Developed by:** [omar khandji](https://apify.com/om_kh) (community) - **Categories:** E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 Review Monitor — New Reviews & Fake-Review Spike Alerts (Vigia) Track Amazon reviews for your ASINs or a competitor's and get alerted only on what matters: 1-2 star reviews, urgent complaints ("refund", "scam", "nobody answered"), and a sudden burst of new reviews for one product — a classic fake-review-farm or review-bombing signal raw review extractors don't flag. Vigia never scrapes Amazon itself — it chains onto a licensed Amazon reviews actor and adds the delta/intelligence layer. ### Why choose Vigia - **Only actionable reviews, only charged for those, at $0.020** — plain review extractors return every review, every run, and charge for all of them whether you need to act or not. - **Review-velocity spike detection** — the killer feature: Vigia tracks each ASIN's historical average new-reviews-per-run and flags `velocity_spike_asins` when a product suddenly gets far more reviews than its normal pace (≥3x baseline). Not offered by any competing Amazon review scraper found on the Store — most only extract, they don't watch for anomalies. - **Rating trend, not a snapshot** — `rating_trend` shows whether satisfaction is genuinely improving or sliding, computed across your whole monitored run history. - **Topic + severity triage** — every actionable review is tagged `topic` (shipping/quality/support/pricing) and `severity` (critical/moderate/minor), so you know what to fix first. - **No fragile scraping** — chains onto a maintained Amazon reviews actor instead of a homegrown scraper. - **Fair pricing** — hard `maxTotalChargeUsd` cap; never billed without a result. ### Input - `asins` — Amazon product IDs to monitor. - `monitorEnabled` / `monitorId` — persistent delta mode; first run is a free baseline. - `maxTotalChargeUsd` — hard spend cap for Vigia's own charge. You can also pass `currentReviews` directly if you bring your own review export. ### Output One row per actionable review: `change_type`, `asin`, `rating`, `severity`, `topic`, `actionable`, `signals`, `text`. Plus a `RUN_SUMMARY.json` with the aggregate `review_signal`, including `velocity_spike_asins`. ### Pricing transparency | | Cost | |---|---| | Source (chained Amazon reviews actor) | billed by the scraper actor directly on your Apify invoice, not marked up | | Vigia (delta, topic/severity triage, velocity-spike detection) | $0.020 per actionable review, $0 otherwise | | Example: 5 ASINs, no actionable reviews this run | source cost only, $0 Vigia charge | | Example: 5 ASINs, 3 actionable reviews + 1 velocity spike flagged | source cost + $0.06 Vigia charge | | Cap | `maxTotalChargeUsd` (Vigia's own charge only) | ### FAQ **Does this actor fetch reviews from Amazon itself?** No — it chains onto a licensed Amazon reviews actor and adds the delta, triage, and velocity-spike layer. **Why only actionable reviews, not all of them?** A 5-star review with no complaint doesn't need your attention or a charge — Vigia's job is to cut the noise, not resell every row. **What counts as a velocity spike?** A product's new-review count this run reaching at least 3x its own historical per-run average, with a minimum of 3 new reviews (so a product's first monitored run never false-flags on a normal small baseline). # Actor input Schema ## `asins` (type: `array`): Amazon product IDs (ASINs) to monitor for new reviews. ## `currentReviews` (type: `array`): Skip the chained Amazon fetch and pass review data directly, e.g. from your own export. ## `previousReviews` (type: `array`): Reviews from the prior run; used to identify new feedback when not using persistent monitor mode. ## `maxTotalChargeUsd` (type: `number`): Hard maximum amount that this run may charge. ## `sources` (type: `array`): Declare every upstream source before this run. Each item needs name, authorization, and estimated_cost_usd. ## `monitorEnabled` (type: `boolean`): The first monitored run stores a free baseline. Later runs deliver and charge only new results. ## `monitorId` (type: `string`): Pins the persistent baseline to a name of your choice, so separate targets never share state. Leave empty to derive it automatically. ## Actor input object example ```json { "asins": [], "currentReviews": [], "previousReviews": [], "maxTotalChargeUsd": 1, "sources": [ { "name": "Chained Amazon reviews actor", "authorization": "public_structured_data", "estimated_cost_usd": 0 } ], "monitorEnabled": false, "monitorId": "" } ```` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("om_kh/vigia-amazon-review-intelligence").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 = {} # Run the Actor and wait for it to finish run = client.actor("om_kh/vigia-amazon-review-intelligence").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 '{}' | apify call om_kh/vigia-amazon-review-intelligence --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=om_kh/vigia-amazon-review-intelligence", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amazon Review Monitor — Fake-Review Spike Alerts (Vigia)", "description": "Track Amazon product reviews for your ASINs or competitors' and get alerted only on actionable feedback: 1-2 star reviews, urgent complaints, and sudden review-volume spikes (a fake-review-farm signal).", "version": "0.1", "x-build-id": "EHVQEo3bSPKxrgYaz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/om_kh~vigia-amazon-review-intelligence/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-om_kh-vigia-amazon-review-intelligence", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/om_kh~vigia-amazon-review-intelligence/runs": { "post": { "operationId": "runs-sync-om_kh-vigia-amazon-review-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/om_kh~vigia-amazon-review-intelligence/run-sync": { "post": { "operationId": "run-sync-om_kh-vigia-amazon-review-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "maxTotalChargeUsd" ], "properties": { "asins": { "title": "ASINs to track", "type": "array", "description": "Amazon product IDs (ASINs) to monitor for new reviews.", "default": [], "items": { "type": "string" } }, "currentReviews": { "title": "Bring your own reviews (optional)", "type": "array", "description": "Skip the chained Amazon fetch and pass review data directly, e.g. from your own export.", "default": [] }, "previousReviews": { "title": "Previous reviews (non-monitor mode)", "type": "array", "description": "Reviews from the prior run; used to identify new feedback when not using persistent monitor mode.", "default": [] }, "maxTotalChargeUsd": { "title": "Maximum Vigia charge (USD)", "minimum": 0, "type": "number", "description": "Hard maximum amount that this run may charge.", "default": 1 }, "sources": { "title": "Declared upstream sources and estimated costs", "type": "array", "description": "Declare every upstream source before this run. Each item needs name, authorization, and estimated_cost_usd.", "default": [ { "name": "Chained Amazon reviews actor", "authorization": "public_structured_data", "estimated_cost_usd": 0 } ] }, "monitorEnabled": { "title": "Monitor only new results", "type": "boolean", "description": "The first monitored run stores a free baseline. Later runs deliver and charge only new results.", "default": false }, "monitorId": { "title": "Monitor ID (optional)", "type": "string", "description": "Pins the persistent baseline to a name of your choice, so separate targets never share state. Leave empty to derive it automatically.", "default": "" } } }, "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 } } } } } } } } } } ```