# Running Shoe Multi-Brand Catalog Scraper (`jungle_synthesizer/running-shoe-multi-brand-catalog-scraper`) Actor Scrape running shoe catalogs from Brooks, Saucony, Altra, On, and Running Warehouse — normalized records with runner-relevant specs (drop, stack, foam, plate, weight). - **URL**: https://apify.com/jungle\_synthesizer/running-shoe-multi-brand-catalog-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** E-commerce, Sports - **Stats:** 2 total users, 1 monthly users, 66.7% runs succeeded, NaN 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.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 ## Running Shoe Multi-Brand Catalog Scraper Scrape running shoe catalogs from Brooks, Saucony, Altra, and On — four premier running-specialist brands — all normalized into a single dataset. Built for affiliate publishers, competitive-intelligence teams, and shoe-recommendation engines that need structured spec data beyond what generic shopping scrapers offer. ### What it does Crawls Running Warehouse's per-brand catalog pages (the cleanest, most open source for all four brands) and extracts runner-relevant technical specs from each product detail page: - **Biomechanical specs**: heel-to-toe drop (mm), heel stack height (mm), forefoot stack height (mm), weight (g) - **Product metadata**: SKU, model family, generation, product URL, MSRP - **Catalog info**: available sizes, in-stock status, star rating - **Context fields**: gender (men/women), terrain (road/trail) ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `maxItems` | integer | 10 | Maximum shoe records to return | | `sources` | array | `["brooks","saucony","altra","on"]` | Which brands to scrape | #### Example — Altra only ```json { "maxItems": 50, "sources": ["altra"] } ```` #### Example — all four brands ```json { "maxItems": 500, "sources": ["brooks", "saucony", "altra", "on"] } ``` ### Output schema ```json { "source_brand": "altra", "product_id": "WME04M2", "product_name": "Altra Escalante 4 Men's Shoes Gray/Lime", "model_family": "Altra Escalante", "generation": "4", "product_url": "https://www.runningwarehouse.com/Altra_Escalante_4/descpage-WME04M2.html", "msrp_usd": 129.95, "sale_price_usd": null, "color_options": null, "sizes_available": "7.0, 7.5, 8.0, 8.5, 9.0", "gender": "men", "terrain": "road", "cushioning": null, "drop_mm": 0, "weight_g": 244, "stack_height_mm": 25, "forefoot_stack_mm": 25, "foam_technology": null, "plate_material": null, "intended_use": null, "upper_material": "Stylish and breathable engineered mesh upper", "star_rating": 4.6, "review_count": 26, "in_stock": true } ``` ### Use cases - **Affiliate content**: keep product specs and pricing current for review sites (Believe in the Run, Doctors of Running, Solereview) - **Competitive intelligence**: track model changes, stack-height trends, and pricing across brands - **Shoe recommenders**: power filters by drop, weight, terrain, and gender - **Price monitoring**: detect sales across Brooks, Saucony, Altra, and On in one run ### Brand coverage | Brand | Men's | Women's | Catalog pages scraped | |-------|-------|---------|----------------------| | Brooks | Road + Stability | Road + Stability | MRABROOKS, MRSBROOKS, WRABROOKS, WRSBROOKS | | Saucony | Road + Stability | Road + Stability | MRSSAUCONY, MSASAUCONY, WRSSAUCONY, WSASAUCONY | | Altra | All | All | ALTRAMS, ALTRAWS | | On | Road | Road | MRSON, WRSON | Estimated active catalog: ~400–600 SKUs across all four brands (varies by season). ### Notes - All data scraped from [Running Warehouse](https://www.runningwarehouse.com), which carries complete catalogs for all four brands with no anti-bot protection - `foam_technology`, `plate_material`, `intended_use`, and `cushioning` are null in V1 — these appear only in product description prose; structured extraction is planned for V2 - Full 4-brand catalog run completes in under 15 minutes at 5 concurrent requests # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of shoe records to scrape across all selected sources ## `sources` (type: `array`): Which brands to include. Options - brooks, saucony, altra, on. Defaults to all four. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "sources": [ "brooks", "saucony", "altra", "on" ] } ``` # 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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "sources": [ "brooks", "saucony", "altra", "on" ] }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/running-shoe-multi-brand-catalog-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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "sources": [ "brooks", "saucony", "altra", "on", ], } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/running-shoe-multi-brand-catalog-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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "sources": [ "brooks", "saucony", "altra", "on" ] }' | apify call jungle_synthesizer/running-shoe-multi-brand-catalog-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/running-shoe-multi-brand-catalog-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Running Shoe Multi-Brand Catalog Scraper", "description": "Scrape running shoe catalogs from Brooks, Saucony, Altra, On, and Running Warehouse — normalized records with runner-relevant specs (drop, stack, foam, plate, weight).", "version": "0.1", "x-build-id": "PQDJRyGqmDmY1XkQg" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~running-shoe-multi-brand-catalog-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-running-shoe-multi-brand-catalog-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/jungle_synthesizer~running-shoe-multi-brand-catalog-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-running-shoe-multi-brand-catalog-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/jungle_synthesizer~running-shoe-multi-brand-catalog-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-running-shoe-multi-brand-catalog-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": [ "maxItems", "sources" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of shoe records to scrape across all selected sources" }, "sources": { "title": "Sources (brands)", "type": "array", "description": "Which brands to include. Options - brooks, saucony, altra, on. Defaults to all four.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```