# Fashion Intelligence Bundle (`runtime/fashion-intelligence-bundle`) Actor Find official fashion brand websites, social profiles, event mentions, retail footprint signals, and hiring signals in one scored AI-ready dataset. - **URL**: https://apify.com/runtime/fashion-intelligence-bundle.md - **Developed by:** [scraping automation](https://apify.com/runtime) (community) - **Categories:** Lead generation, Business, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Fashion Intelligence Bundle Turn fashion brand, event, retail, and hiring signals into normalized AI-ready records for market monitoring, brand research, lead generation, and competitive watchlists. This bundle is designed as a pay-per-event product. It should charge per saved fashion intelligence record using Apify's synthetic `apify-default-dataset-item` event. ### Commercial model The bundle runs as one commercial Actor. Customers should pay for this bundle, not for a stack of hidden child Actors. To avoid double billing, this Actor does not call paid public Store Actors by default. Brand resolution and web discovery are integrated directly in the bundle. Future calendar, jobs, store, or product sources should be integrated as shared code or explicitly documented internal/free helpers. The public Actor is published at `https://apify.com/runtime/fashion-intelligence-bundle`. Build `0.1.2` is tagged as `latest`. PPE pricing is scheduled in Apify for `2026-07-09T00:00:00.000Z`, because Apify requires pricing changes to be effective at least two weeks in the future. The primary event is `apify-default-dataset-item` as `Fashion intelligence record`, priced at `$0.10` for Free users, `$0.09` for Bronze, `$0.08` for Silver, and `$0.07` for Gold and above. Actor start is `$0.00005`. ### Current sources - Direct official-brand-site resolution inside this Actor. - Direct DuckDuckGo web discovery inside this Actor. - Optional Brave Search API when provided through `BRAVE_SEARCH_API_KEY` or `braveApiKey`. - Mock mode for smoke tests and integration checks. ### Validation status - Public Actor exists on Apify as `runtime/fashion-intelligence-bundle`. - Production build `0.1.2` passed real cloud smoke with 1 accepted Dior `brand_site` record and `officialUrl`: run `JvSRaWBzqpzoW2O2f`, dataset `vYpupgWbTRqGyYY5g`, usage `$0.00027`, max memory `58.7 MB`. - 2026-06-25 production recheck passed with 1 accepted Dior `brand_site` record: run `lnkXDuDkEQuFxE7Pf`, dataset `WasbidHU5DhzZ5ddW`, usage `$0.00017`. - Dev build `0.1.1` passed the same real cloud smoke: run `aeW4ax7eMa7tFjwnF`, dataset `fabRYFDUcHSn6jK6g`, usage `$0.00032`, max memory `84.8 MB`. ### Output Each dataset item includes: - `signalType`, `workflow`, `brand`, `company` - `title`, `url`, `candidateUrl`, `officialUrl`, `domain`, `description` - `socialLinks`, `confidence`, `confidenceReasons`, `negativeSignals`, `reviewStatus` - `location`, `city`, `country`, `season`, `eventName`, `date` - `source`, `sourceUrl`, `sourceQuery`, `sourcePosition`, `sourceData`, `scrapedAt` ### Dataset views The Actor defines dataset views for Store examples and published tasks: - `Brands` for official brand URLs, domains, confidence, review status, social links, description, source, and scrape timestamp. - `Signals` for event, retail, hiring, and web monitoring records with brand, URL, location context, confidence, and review status. - `Evidence` for source URLs, queries, candidate URLs, scoring reasons, negative signals, source data, and scrape timestamp. ### Example input ```json { "workflow": "brand-official-site-finder", "brandName": "Dior", "city": "Paris", "season": "Spring/Summer 2027", "eventName": "Paris Fashion Week", "includeBrandResolution": true, "includeWebSignals": true, "maxCandidatesPerBrand": 3, "maxWebSignals": 5, "crawlHomepage": true } ```` ### Smoke input ```json { "mockMode": true, "maxResults": 2 } ``` ### Planned source order 1. Paris Fashion Week calendar source. 2. Curated brand-list source. 3. Luxury hiring source after provider-level smoke tests. 4. Retail footprint source after store locator cost/risk measurement. 5. Ecommerce/product sources only after anti-bot and proxy cost risk is known. # Actor input Schema ## `workflow` (type: `string`): Business workflow label stored on each output item. ## `brandName` (type: `string`): Single fashion brand to analyze. Used when Brand names is empty. ## `brandNames` (type: `array`): Batch brands, one per line. When non-empty, this overrides Brand name. ## `city` (type: `string`): Optional city context, usually from a fashion event. ## `season` (type: `string`): Optional season or collection context. ## `eventName` (type: `string`): Optional event context. ## `country` (type: `string`): Optional country context. ## `date` (type: `string`): Optional event, monitoring, or report date in YYYY-MM-DD format. ## `includeBrandResolution` (type: `boolean`): Find and score likely official brand websites and social profiles inside this bundle Actor. ## `includeWebSignals` (type: `boolean`): Collect supporting web signals for the selected workflow using direct web search. ## `searchQueries` (type: `array`): Optional custom queries, one per line. If empty, the Actor generates workflow-specific queries from the brands and context. ## `customQueries` (type: `array`): Extra queries for official-site resolution, one per line. ## `manualCandidates` (type: `array`): Optional candidate URLs to score. Add brandName per candidate when running multiple brands. ## `searchProviders` (type: `array`): Search providers to use. Brave requires BRAVE\_SEARCH\_API\_KEY or braveApiKey. ## `braveApiKey` (type: `string`): Optional Brave Search API key. Prefer using the BRAVE\_SEARCH\_API\_KEY environment variable. ## `maxBrands` (type: `integer`): Maximum brands to process. ## `maxResults` (type: `integer`): Maximum normalized records saved by the bundle. ## `maxCandidatesPerBrand` (type: `integer`): Maximum official-site candidates scored per brand. ## `maxSearchResults` (type: `integer`): Maximum raw search/manual candidates before domain deduplication. ## `maxWebSignals` (type: `integer`): Maximum supporting web signals to save. ## `maxWebQueries` (type: `integer`): Maximum workflow search queries to execute. ## `acceptThreshold` (type: `integer`): Confidence required to mark a brand site as accepted. ## `reviewThreshold` (type: `integer`): Minimum confidence for needs\_review. Lower scores are rejected. ## `includeRejected` (type: `boolean`): Save rejected official-site candidates. Disabled by default to keep paid datasets focused. ## `crawlHomepage` (type: `boolean`): Fetch candidate homepages to extract metadata, JSON-LD organization names, and social links. ## `blockedDomains` (type: `array`): Additional domains to reject before crawl. ## `allowedDomains` (type: `array`): Domains that bypass the built-in false-positive domain filter. ## `mockMode` (type: `boolean`): Generate sample fashion signals without network access. ## `failOnNoResults` (type: `boolean`): Fail the run if no normalized records are produced. ## `requestTimeoutSecs` (type: `integer`): Timeout for search and homepage crawl requests. ## `useApifyProxy` (type: `boolean`): Enable Apify Proxy for direct web discovery. ## `proxyConfiguration` (type: `object`): Advanced Apify Proxy or custom proxy settings. ## Actor input object example ```json { "workflow": "brand-official-site-finder", "brandName": "Dior", "brandNames": [], "city": "Paris", "season": "Spring/Summer 2027", "eventName": "Paris Fashion Week", "country": "France", "includeBrandResolution": true, "includeWebSignals": true, "searchQueries": [], "customQueries": [], "manualCandidates": [], "searchProviders": [ "duckduckgo" ], "maxBrands": 10, "maxResults": 25, "maxCandidatesPerBrand": 5, "maxSearchResults": 20, "maxWebSignals": 10, "maxWebQueries": 10, "acceptThreshold": 80, "reviewThreshold": 40, "includeRejected": false, "crawlHomepage": true, "blockedDomains": [], "allowedDomains": [], "mockMode": false, "failOnNoResults": true, "requestTimeoutSecs": 20, "useApifyProxy": false, "proxyConfiguration": {} } ``` # Actor output Schema ## `signalType` (type: `string`): Normalized fashion signal type ## `workflow` (type: `string`): Workflow that produced the record ## `brand` (type: `string`): Fashion brand associated with the record ## `company` (type: `string`): Company or brand entity ## `title` (type: `string`): Record title ## `url` (type: `string`): Primary URL ## `candidateUrl` (type: `string`): Candidate URL scored during brand resolution ## `officialUrl` (type: `string`): Accepted official brand URL when confidence is high enough ## `domain` (type: `string`): Primary domain ## `description` (type: `string`): Snippet, homepage description, or source summary ## `socialLinks` (type: `string`): Detected social links ## `confidence` (type: `string`): 0-100 confidence score ## `confidenceReasons` (type: `string`): Positive scoring reasons ## `negativeSignals` (type: `string`): Risk or quality warnings ## `reviewStatus` (type: `string`): accepted, needs\_review, or rejected ## `location` (type: `string`): Location context ## `city` (type: `string`): City context ## `country` (type: `string`): Country context ## `season` (type: `string`): Season or collection context ## `eventName` (type: `string`): Fashion event context ## `date` (type: `string`): Event, monitoring, or report date ## `source` (type: `string`): Source module or provider ## `sourceUrl` (type: `string`): Source or listing URL ## `sourceQuery` (type: `string`): Search query that produced the record ## `sourcePosition` (type: `string`): Position in the source listing ## `sourceData` (type: `string`): Source-specific fields preserved after normalization ## `scrapedAt` (type: `string`): ISO timestamp when the record was created # 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 = { "brandName": "Dior" }; // Run the Actor and wait for it to finish const run = await client.actor("runtime/fashion-intelligence-bundle").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 = { "brandName": "Dior" } # Run the Actor and wait for it to finish run = client.actor("runtime/fashion-intelligence-bundle").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 '{ "brandName": "Dior" }' | apify call runtime/fashion-intelligence-bundle --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=runtime/fashion-intelligence-bundle", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Fashion Intelligence Bundle", "description": "Find official fashion brand websites, social profiles, event mentions, retail footprint signals, and hiring signals in one scored AI-ready dataset.", "version": "0.1", "x-build-id": "lHG17VjyyY8Z72wLe" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/runtime~fashion-intelligence-bundle/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-runtime-fashion-intelligence-bundle", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/runtime~fashion-intelligence-bundle/runs": { "post": { "operationId": "runs-sync-runtime-fashion-intelligence-bundle", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/runtime~fashion-intelligence-bundle/run-sync": { "post": { "operationId": "run-sync-runtime-fashion-intelligence-bundle", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "workflow": { "title": "Workflow", "enum": [ "brand-official-site-finder", "fashion-week-monitor", "luxury-hiring-signals", "retail-footprint-watch", "custom" ], "type": "string", "description": "Business workflow label stored on each output item.", "default": "brand-official-site-finder" }, "brandName": { "title": "Brand name", "type": "string", "description": "Single fashion brand to analyze. Used when Brand names is empty.", "default": "Dior" }, "brandNames": { "title": "Brand names", "type": "array", "description": "Batch brands, one per line. When non-empty, this overrides Brand name.", "items": { "type": "string" }, "default": [] }, "city": { "title": "City", "type": "string", "description": "Optional city context, usually from a fashion event.", "default": "Paris" }, "season": { "title": "Season", "type": "string", "description": "Optional season or collection context.", "default": "Spring/Summer 2027" }, "eventName": { "title": "Event name", "type": "string", "description": "Optional event context.", "default": "Paris Fashion Week" }, "country": { "title": "Country", "type": "string", "description": "Optional country context.", "default": "France" }, "date": { "title": "Date", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "type": "string", "description": "Optional event, monitoring, or report date in YYYY-MM-DD format." }, "includeBrandResolution": { "title": "Resolve official sites", "type": "boolean", "description": "Find and score likely official brand websites and social profiles inside this bundle Actor.", "default": true }, "includeWebSignals": { "title": "Collect web signals", "type": "boolean", "description": "Collect supporting web signals for the selected workflow using direct web search.", "default": true }, "searchQueries": { "title": "Custom web queries", "type": "array", "description": "Optional custom queries, one per line. If empty, the Actor generates workflow-specific queries from the brands and context.", "items": { "type": "string" }, "default": [] }, "customQueries": { "title": "Custom brand-resolution queries", "type": "array", "description": "Extra queries for official-site resolution, one per line.", "items": { "type": "string" }, "default": [] }, "manualCandidates": { "title": "Manual brand candidates", "type": "array", "description": "Optional candidate URLs to score. Add brandName per candidate when running multiple brands.", "items": { "type": "object", "properties": { "brandName": { "title": "Brand name", "description": "Brand associated with this candidate URL.", "type": "string" }, "url": { "title": "URL", "description": "Candidate URL to score as a possible official brand site.", "type": "string" }, "title": { "title": "Title", "description": "Optional candidate page title.", "type": "string" }, "snippet": { "title": "Snippet", "description": "Optional candidate snippet or context.", "type": "string" } }, "required": [ "url" ] }, "default": [] }, "searchProviders": { "title": "Search providers", "type": "array", "description": "Search providers to use. Brave requires BRAVE_SEARCH_API_KEY or braveApiKey.", "items": { "type": "string", "enum": [ "duckduckgo", "brave" ], "enumTitles": [ "DuckDuckGo", "Brave Search API" ] }, "default": [ "duckduckgo" ] }, "braveApiKey": { "title": "Brave API key", "type": "string", "description": "Optional Brave Search API key. Prefer using the BRAVE_SEARCH_API_KEY environment variable." }, "maxBrands": { "title": "Max brands", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum brands to process.", "default": 10 }, "maxResults": { "title": "Max output records", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum normalized records saved by the bundle.", "default": 25 }, "maxCandidatesPerBrand": { "title": "Max candidates per brand", "minimum": 1, "maximum": 50, "type": "integer", "description": "Maximum official-site candidates scored per brand.", "default": 5 }, "maxSearchResults": { "title": "Max search results", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum raw search/manual candidates before domain deduplication.", "default": 20 }, "maxWebSignals": { "title": "Max web signals", "minimum": 0, "maximum": 500, "type": "integer", "description": "Maximum supporting web signals to save.", "default": 10 }, "maxWebQueries": { "title": "Max web queries", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum workflow search queries to execute.", "default": 10 }, "acceptThreshold": { "title": "Accepted threshold", "minimum": 1, "maximum": 100, "type": "integer", "description": "Confidence required to mark a brand site as accepted.", "default": 80 }, "reviewThreshold": { "title": "Review threshold", "minimum": 0, "maximum": 100, "type": "integer", "description": "Minimum confidence for needs_review. Lower scores are rejected.", "default": 40 }, "includeRejected": { "title": "Include rejected candidates", "type": "boolean", "description": "Save rejected official-site candidates. Disabled by default to keep paid datasets focused.", "default": false }, "crawlHomepage": { "title": "Crawl homepage", "type": "boolean", "description": "Fetch candidate homepages to extract metadata, JSON-LD organization names, and social links.", "default": true }, "blockedDomains": { "title": "Extra blocked domains", "type": "array", "description": "Additional domains to reject before crawl.", "items": { "type": "string" }, "default": [] }, "allowedDomains": { "title": "Allowed domains", "type": "array", "description": "Domains that bypass the built-in false-positive domain filter.", "items": { "type": "string" }, "default": [] }, "mockMode": { "title": "Mock mode", "type": "boolean", "description": "Generate sample fashion signals without network access.", "default": false }, "failOnNoResults": { "title": "Fail on no results", "type": "boolean", "description": "Fail the run if no normalized records are produced.", "default": true }, "requestTimeoutSecs": { "title": "Request timeout seconds", "minimum": 5, "maximum": 120, "type": "integer", "description": "Timeout for search and homepage crawl requests.", "default": 20 }, "useApifyProxy": { "title": "Use Apify Proxy", "type": "boolean", "description": "Enable Apify Proxy for direct web discovery.", "default": false }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Advanced Apify Proxy or custom proxy settings.", "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 } } } } } } } } } } ```