# Whbm Discovery Spider (`getdataforme/whbm-discovery-spider`) Actor Discover and extract comprehensive product data from White House Black Market with the Whbm Discovery Spider. Retrieve titles, images, prices, ratings, and color variations via flexible searches. Perfect for e-commerce research, competitive intelligence, and automated workflows.... - **URL**: https://apify.com/getdataforme/whbm-discovery-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 1,000 results 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.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 ### Description Discover and extract comprehensive product data from White House Black Market with the Whbm Discovery Spider. Retrieve titles, images, prices, ratings, and color variations via flexible searches. Perfect for e-commerce research, competitive intelligence, and automated workflows.... --- ## Whbm Discovery Spider The Whbm Discovery Spider is an Apify Actor designed to scrape product data from White House Black Market (WHBM) website efficiently and reliably. It allows users to search for products based on specific terms and retrieve detailed information including images, prices, ratings, and more. This tool is ideal for e-commerce professionals, researchers, and businesses needing automated data extraction from WHBM's catalog. ### Features - **Comprehensive Product Data Extraction**: Retrieves detailed product information such as titles, images, color variations, ratings, reviews, and pricing details. - **Flexible Search Capabilities**: Supports custom search terms to target specific product categories or items. - **High-Performance Scraping**: Built with robust handling to manage large volumes of data while respecting website limits. - **Structured JSON Output**: Delivers clean, structured data in JSON format for easy integration with other tools or databases. - **Configurable Item Limits**: Allows users to set maximum items to process, optimizing resource usage. - **Reliable and Scalable**: Designed for consistent performance, with error handling to ensure data integrity. - **Apify Integration**: Seamlessly runs on the Apify platform, supporting scheduling and monitoring. ### Input Parameters | Parameter | Type | Required | Description | Example | |------------|---------|----------|-----------------------------------------------------------------------------|------------------| | SearchTerm | string | No | The search term used to query products on the WHBM website. Defaults to "dress". | "dress" | | itemLimit | integer | No | Specifies the maximum number of items to process. Defaults to 20. | 20 | ### Example Usage To run the Whbm Discovery Spider, provide input parameters in JSON format. Here's an example: ```json { "SearchTerm": "dress", "itemLimit": 20 } ```` The actor will output a JSON array of product objects. Example output: ```json [ { "type": "product", "id": "196536514989", "parent_id": "196535766877", "title": "Mock Neck Knit Lace Midi Dress", "primary_image": "https://www.whitehouseblackmarket.com/_assets/products/content/pucrwvf4pv/jpeg/WHBM_SL%20Knit%20FNF%20Midi_570404747_BLU007359_OF_MAIN.jpeg?position=c&color=ffffffff&quality=100&u=9o1w1f&versionId=6daa8186", "all_images": [ "https://www.whitehouseblackmarket.com/_assets/products/content/pucrwvf4pv/jpeg/WHBM_SL%20Knit%20FNF%20Midi_570404747_BLU007359_OF_MAIN.jpeg?position=c&color=ffffffff&quality=100&u=9o1w1f&versionId=6daa8186", "https://www.whitehouseblackmarket.com/_assets/products/content/ej0dme8fc2/jpeg/WHBM_SL%20Knit%20FNF%20Midi_570404747_BLU007359_OF_ALT%201.jpeg?position=c&color=ffffffff&quality=100&u=9o1w1f&versionId=dc2ed4a4", "https://www.whitehouseblackmarket.com/_assets/products/content/jeweaneupw/jpeg/WHBM_SL%20Knit%20FNF%20Midi_570404747_BLU007359_OF_ALT%202.jpeg?position=c&color=ffffffff&quality=100&u=9o1w1f&versionId=6fc6180f", "https://www.whitehouseblackmarket.com/_assets/products/content/a1mndhkrwy/jpeg/WHBM_SL%20Knit%20FNF%20Midi_570404747_BLU007359_OF_ALT%203.jpeg?position=c&color=ffffffff&quality=100&u=9o1w1f&versionId=a4895486", "https://www.whitehouseblackmarket.com/_assets/products/content/tcyjdgjqgl/jpeg/WHBM_SL%20Knit%20FNF%20Midi_570404747_BLU007359_PU_ALT%204.jpeg?position=c&color=ffffffff&quality=100&u=9o1w1f&versionId=3cba0d22" ], "color_variations": [ { "label": "Nautical Blue", "value": "7359", "swatch_url": "https://www.whitehouseblackmarket.com/_assets/products/content/roeihbgwuu/gif/WHBM_SL%20Knit%20FNF%20Midi_570404747_BLU007359_SWATCH_MAIN.gif?position=c&color=ffffffff&quality=100&u=9o1w1f&versionId=cb49cbe7" }, { "label": "Canary", "value": "1393", "swatch_url": "https://www.whitehouseblackmarket.com/_assets/products/content/qvmmf6jybe/gif/WHBM_SL%20Knit%20FNF%20Midi_570404748_YLW001393_SWATCH_MAIN.gif?position=c&color=ffffffff&quality=100&u=9o1w1f&versionId=b0bd3d86" } ], "rating": 5, "reviews_count": 5, "min_sale_price": 152.99, "max_sale_price": 240, "min_actual_price": 220, "max_actual_price": 240, "currency": "USD", "promo_message": "30% Off. Price as Marked.", "skus": [ { "style": "570404747", "title": "Mock Neck Knit Lace Midi Dress", "sale_price": 240, "actual_price": null, "currency": "USD", "discount_pct": null, "promo_message": "", "product_url": "https://www.whitehouseblackmarket.com/store/product/Mock-Neck-Knit-Lace-Midi-Dress/570404747?color=7359", "available": true }, { "style": "570404748", "title": "Mock Neck Knit Lace Midi Dress", "sale_price": 168, "actual_price": 240, "currency": "USD", "discount_pct": 30, "promo_message": "30% Off. Price as Marked.", "product_url": "https://www.whitehouseblackmarket.com/store/product/Mock-Neck-Knit-Lace-Midi-Dress/570404748?color=1393", "available": true } ], "actor_id": null, "run_id": null } ] ``` ### Use Cases - **Market Research and Analysis**: Gather data on product trends, pricing, and customer ratings to inform business strategies. - **Competitive Intelligence**: Monitor competitors' offerings on WHBM to compare products and prices. - **Price Monitoring**: Track sale prices and discounts for inventory management or dynamic pricing. - **Content Aggregation**: Collect product images and descriptions for e-commerce platforms or blogs. - **Academic Research**: Study fashion trends and consumer preferences based on WHBM's catalog. - **Business Automation**: Integrate with workflows to automate data collection for reports or dashboards. ### Installation and Usage 1. Search for "Whbm Discovery Spider" in the Apify Store 2. Click "Try for free" or "Run" 3. Configure input parameters 4. Click "Start" to begin extraction 5. Monitor progress in the log 6. Export results in your preferred format (JSON, CSV, Excel) ### Output Format The output is a JSON array of product objects. Each object includes fields like `type`, `id`, `title`, `primary_image`, `all_images`, `color_variations`, `rating`, `reviews_count`, `min_sale_price`, `max_sale_price`, `currency`, `promo_message`, and `skus` (an array of SKU details with prices, URLs, and availability). This structure ensures easy parsing and integration. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! *** # Actor input Schema ## `SearchTerm` (type: `string`): The search term for the spider. ## `itemLimit` (type: `integer`): Specifies the maximum number of items to process. ## Actor input object example ```json { "SearchTerm": "dress", "itemLimit": 20 } ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # 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 = { "itemLimit": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/whbm-discovery-spider").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 = { "itemLimit": 20 } # Run the Actor and wait for it to finish run = client.actor("getdataforme/whbm-discovery-spider").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 '{ "itemLimit": 20 }' | apify call getdataforme/whbm-discovery-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/whbm-discovery-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Whbm Discovery Spider", "description": "Discover and extract comprehensive product data from White House Black Market with the Whbm Discovery Spider. Retrieve titles, images, prices, ratings, and color variations via flexible searches. Perfect for e-commerce research, competitive intelligence, and automated workflows....", "version": "0.0", "x-build-id": "bKnRkubKilh9bSJAI" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~whbm-discovery-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-whbm-discovery-spider", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~whbm-discovery-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-whbm-discovery-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~whbm-discovery-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-whbm-discovery-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "SearchTerm": { "title": "Search Term", "type": "string", "description": "The search term for the spider.", "default": "dress" }, "itemLimit": { "title": "Item Limit", "type": "integer", "description": "Specifies the maximum number of items to process.", "default": 20 } } }, "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 } } } } } } } } } } ```