# Arcteryx Category Scraper (`getdataforme/arcteryx-category-scraper`) Actor The Arcteryx Category Scraper efficiently extracts detailed product data from the Arc'teryx website, ideal for market research and competitive analysis.... - **URL**: https://apify.com/getdataforme/arcteryx-category-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 1 total users, 0 monthly users, 0.0% 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 ## Arcteryx Category Scraper The Arcteryx Category Scraper is a powerful tool designed to extract detailed product information from the Arc'teryx website. Whether you're conducting market research, monitoring prices, or gathering data for competitive analysis, this actor provides reliable and comprehensive data to meet your needs. ### Features - **Comprehensive Data Extraction**: Scrapes detailed product information including names, descriptions, prices, reviews, and images. - **Customizable Search**: Input specific product categories or search terms to tailor the scraping process. - **High Performance**: Efficiently handles large volumes of data with a maximum limit of 1000 products per run. - **Accurate and Reliable**: Ensures high data quality with up-to-date information directly from the source. - **User-Friendly Interface**: Simple setup and configuration through the Apify platform. - **Flexible Output Formats**: Export data in JSON, CSV, or Excel formats for easy integration with other tools. - **Automated Data Collection**: Schedule regular scraping tasks to keep your data fresh and relevant. ### Input Parameters | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------|-------------------| | searchQuery | String | Yes | Product category or search term to scrape (e.g., 'trail run women'). | "trail run women" | | maxItems | Integer | Yes | Maximum number of products to scrape. The crawler stops once this limit is reached. | 100 | ### Example Usage #### Example Input ```json { "searchQuery": "trail run women", "maxItems": 100 } ```` #### Example Output ```json [ { "id": "X000010153", "name": "Sylan 2 Shoe Women's", "description": "Propulsive trail running shoe for confident speed", "slug": "womens/sylan-2-shoe-0153", "isNew": true, "isPro": false, "isRevised": false, "collection": null, "price": { "currency": "CAD", "regular": 280, "minDiscount": 280, "maxDiscount": 280, "isOnSale": false }, "review": { "count": 8, "rating": 4.875 }, "images": { "hover": "https://images.arcteryx.com/details/1350x1710/S26-X000010153-Sylan-2-Shoe-Graphite-Graphite-Women-s-Hover.jpg" }, "colours": [ { "label": "Shincha/Shincha", "primaryColour": "Multi", "selected": true, "imageUrl": "https://images.arcteryx.com/details/1350x1710/S26-X000010153-Sylan-2-Shoe-Shincha-Shincha-Women-s-Profile.jpg", "thumbnailUrl": "https://images.arcteryx.com/S26/1350x1710/Sylan-2-Shoe-W-Shincha-Shincha.jpg", "hoverImageUrl": "https://images.arcteryx.com/details/1350x1710/S26-X000010153-Sylan-2-Shoe-Shincha-Shincha-Women-s-Hover.jpg", "badges": [ { "code": "bestseller", "label": "Best seller" } ] } ], "url": "https://arcteryx.com/ca/en/shop/womens/sylan-2-shoe-0153" } ] ``` ### Use Cases - **Market Research and Analysis**: Gather comprehensive product data for market trends and consumer preferences. - **Competitive Intelligence**: Monitor competitor offerings and pricing strategies. - **Price Monitoring**: Track price changes and discounts over time. - **Content Aggregation**: Compile product information for content creation or e-commerce platforms. - **Academic Research**: Analyze product data for academic studies and reports. - **Business Automation**: Automate data collection for business intelligence systems. ### Installation and Usage 1. Search for "Arcteryx Category Scraper" 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 where each object contains detailed product information such as `id`, `name`, `description`, `price`, `review`, `images`, `colours`, and `url`. This structured data allows for easy integration and analysis. ### 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 ## `searchQuery` (type: `string`): Product category or search term to scrape (e.g. 'trail run women', 'mens jackets'). This will be converted to a URL slug automatically. ## `maxItems` (type: `integer`): Maximum number of products to scrape. The crawler will stop once this limit is reached. ## Actor input object example ```json { "searchQuery": "trail run women", "maxItems": 100 } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "searchQuery": "trail run women" }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/arcteryx-category-scraper").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 = { "searchQuery": "trail run women" } # Run the Actor and wait for it to finish run = client.actor("getdataforme/arcteryx-category-scraper").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 '{ "searchQuery": "trail run women" }' | apify call getdataforme/arcteryx-category-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/arcteryx-category-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Arcteryx Category Scraper", "description": "The Arcteryx Category Scraper efficiently extracts detailed product data from the Arc'teryx website, ideal for market research and competitive analysis....", "version": "0.0", "x-build-id": "G0sTSaasfqnUqJnfH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~arcteryx-category-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-arcteryx-category-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~arcteryx-category-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-arcteryx-category-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~arcteryx-category-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-arcteryx-category-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "searchQuery", "maxItems" ], "properties": { "searchQuery": { "title": "Search Query", "type": "string", "description": "Product category or search term to scrape (e.g. 'trail run women', 'mens jackets'). This will be converted to a URL slug automatically." }, "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of products to scrape. The crawler will stop once this limit is reached.", "default": 100 } } }, "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 } } } } } } } } } } ```