# WEAR (ZOZO) Fashion Coordinate & Outfit Scraper (`jungle_synthesizer/wear-zozo-fashion-coordinate-outfit-scraper`) Actor Scrape outfit coordinates from WEAR (wear.jp), Japan's largest ZOZO-backed fashion community. Extracts tagged apparel items, brands, style tags, Wearista influencer status, engagement metrics, and body context. For JP fashion-trend and influencer-marketing datasets. - **URL**: https://apify.com/jungle\_synthesizer/wear-zozo-fashion-coordinate-outfit-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** E-commerce, For creators, Marketing - **Stats:** 2 total users, 1 monthly users, 0.0% 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## WEAR (ZOZO) Fashion Coordinate & Outfit Scraper Scrape outfit coordinates from [WEAR (wear.jp)](https://wear.jp/coordinate/) — Japan's largest ZOZO-backed fashion-coordinate community. Extract rich outfit data including tagged apparel items with ZOZO product purchase links, brand associations, style and season tags, Wearista influencer status, engagement metrics (likes, views), and body-context metadata (height, gender). ### What this actor extracts Each outfit coordinate record includes: | Field | Description | |-------|-------------| | `coordinate_id` | WEAR's unique coordinate ID | | `detail_url` | Canonical URL of the coordinate page | | `image_url` | Main outfit photo URL | | `user_name` | WEAR username | | `nick_name` | Poster's display name | | `is_wearista` | Whether the poster is a verified Wearista influencer | | `gender` | Outfit gender category (WOMEN / MEN / KIDS) | | `height_cm` | Poster's height in centimeters | | `like_count` | Number of likes | | `view_count` | Number of views | | `title` | Coordinate title or description | | `published_at` | Publish timestamp (ISO-8601) | | `tagged_items` | Apparel items: name, brand, size, price, ZOZO link | | `brands` | Brand names featured in the coordinate | | `tags` | Style and season tags (e.g. "きれいめカジュアル", "休日スタイル") | | `scrapedAt` | ISO-8601 scrape timestamp | ### Input parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `maxItems` | integer | 10 | Maximum number of coordinates to scrape | | `gender` | string | `"all"` | Filter by gender: `"all"`, `"women"`, `"men"`, or `"kids"` | | `sortBy` | string | `"popular"` | Sort order: `"popular"` (most liked) or `"new"` (latest) | ### Use cases - **JP fashion-trend intelligence**: Identify which brands and styles are popular by gender, height, season, and engagement score. - **Influencer marketing research**: Surface top Wearistas (verified influencers) with their outfit-product associations and engagement data. - **Shoppable coordinate-tag graph**: Map real JP street-style outfits to ZOZO-stocked apparel with direct purchase links. - **Competitive fashion analysis**: Track brand co-occurrence patterns across thousands of real-world outfits. ### Sample output ```json { "coordinate_id": 26703460, "detail_url": "https://wear.jp/ochimhan/26703460/", "image_url": "https://images.wear2.jp/coordinate/QWio67p0/ClLA7Qem/1779760881.jpg", "user_name": "ochimhan", "nick_name": "OKIRI", "is_wearista": true, "gender": "WOMEN", "height_cm": 161, "like_count": 66, "view_count": 6768, "title": "NOGLEのスラックスを使ったコーディネート", "published_at": "2026-06-01T16:28:20+09:00", "tagged_items": [ "【NOGLE/ノグル】ウエストタックワイドカーブスラックス | NOGLE / パンツ | FREE | ¥6,600 | https://zozo.jp/sp/brand/nogle/...", "ROUND NECK KNIT | PAES / トップス | FREE | ¥4,950 | https://zozo.jp/sp/brand/paes/..." ], "brands": ["NOGLE", "PAES", "CONVERSE", "MARC JACOBS"], "tags": ["きれいめカジュアル", "プチプラ", "休日スタイル", "バルーンパンツ"], "scrapedAt": "2026-06-10T15:40:00.000Z" } ```` ### Notes - WEAR caps the browsable feed at 10,000 coordinates. For larger datasets, consider multiple gender/sort filter combinations. - The actor respects WEAR's `crawl-delay: 10` from robots.txt by running at low concurrency with request delays. - All data is extracted from Next.js SSR-rendered HTML — no authentication required. # 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 outfit coordinates to scrape. ## `gender` (type: `string`): Filter by gender: 'all', 'women', 'men', or 'kids'. Default: 'all'. ## `sortBy` (type: `string`): Sort order: 'popular' (most liked) or 'new' (latest). ## 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, "gender": "all", "sortBy": "popular" } ``` # 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, "gender": "all", "sortBy": "popular" }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/wear-zozo-fashion-coordinate-outfit-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, "gender": "all", "sortBy": "popular", } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/wear-zozo-fashion-coordinate-outfit-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, "gender": "all", "sortBy": "popular" }' | apify call jungle_synthesizer/wear-zozo-fashion-coordinate-outfit-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/wear-zozo-fashion-coordinate-outfit-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "WEAR (ZOZO) Fashion Coordinate & Outfit Scraper", "description": "Scrape outfit coordinates from WEAR (wear.jp), Japan's largest ZOZO-backed fashion community. Extracts tagged apparel items, brands, style tags, Wearista influencer status, engagement metrics, and body context. For JP fashion-trend and influencer-marketing datasets.", "version": "0.1", "x-build-id": "nEZiNJ1RFHK4lvx8c" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~wear-zozo-fashion-coordinate-outfit-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-wear-zozo-fashion-coordinate-outfit-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~wear-zozo-fashion-coordinate-outfit-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-wear-zozo-fashion-coordinate-outfit-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~wear-zozo-fashion-coordinate-outfit-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-wear-zozo-fashion-coordinate-outfit-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" ], "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 outfit coordinates to scrape.", "default": 10 }, "gender": { "title": "Gender Filter", "enum": [ "all", "women", "men", "kids" ], "type": "string", "description": "Filter by gender: 'all', 'women', 'men', or 'kids'. Default: 'all'.", "default": "all" }, "sortBy": { "title": "Sort By", "enum": [ "popular", "new" ], "type": "string", "description": "Sort order: 'popular' (most liked) or 'new' (latest).", "default": "popular" } } }, "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 } } } } } } } } } } ```