# ML Contests Scraper (`automation-lab/mlcontests-scraper`) Actor Scrape machine learning, data science, and robotics competitions from mlcontests.com - **URL**: https://apify.com/automation-lab/mlcontests-scraper.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** AI - **Stats:** 2 total users, 1 monthly users, 100.0% 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. 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 ## ML Contests Scraper Extract machine learning, data science, and robotics competitions from [mlcontests.com](https://mlcontests.com/) — the most comprehensive aggregator of ML competitions across 20+ platforms including Kaggle, DrivenData, Zindi, CrunchDAO, AIcrowd, and more. Get structured, up-to-date competition data including prize amounts, deadlines, platforms, tags, sponsors, and conference affiliations — perfect for monitoring the ML competition landscape, building dashboards, or automating your contest pipeline. ### What does it do? ML Contests Scraper fetches the complete list of currently active machine learning competitions from mlcontests.com. It extracts all structured data from the site's aggregated dataset — covering 300+ competitions at any given time — and outputs clean, ready-to-use JSON records. Unlike scraping individual competition platforms one by one, this actor gives you the full picture in a single run: competitions from Kaggle, DrivenData, Zindi, CrunchDAO, Codabench, HuggingFace, AIcrowd, and dozens of independent competitions, all normalised into a consistent schema. ### Who is it for? 🧑‍💻 **ML practitioners and data scientists** who want to discover new competitions matching their skills (NLP, vision, tabular, timeseries) without manually browsing 20+ platforms every week. 📊 **Research teams and academics** tracking competition-associated conferences (NeurIPS, CVPR, ICML) and prize-funded research challenges to identify collaboration or publication opportunities. 🏢 **Competition organisers and sponsors** monitoring the competitive landscape to benchmark prize pools, identify market gaps, and track their own competitions. 🤖 **Automation engineers** building competition alert systems, dashboards, or Slack/email bots that notify teams when relevant competitions open or close. 💼 **Freelancers and consultants** scanning for high-value prize competitions to prioritise client pitches or personal participation. ### Why use it? - **Single source of truth** — mlcontests.com aggregates 300+ competitions across 20+ platforms. One actor replaces manual checking of dozens of sites. - **Structured output** — all data (name, platform, prize, deadline, tags, sponsor, conference) is normalised into a consistent JSON schema. - **Filterable** — filter by platform, tag, or prize availability in a single run. No post-processing needed. - **Up-to-date** — mlcontests.com updates daily. Schedule this actor to run daily and always have fresh data. - **No login required** — public data, no authentication needed. - **Ultra-fast** — single HTTP request, no browser, runs in under 10 seconds. ### What data do you get? Each competition record includes: | Field | Description | Example | |-------|-------------|---------| | `name` | Competition name | `"AI Math Olympiad Progress Prize 2"` | | `url` | Competition URL | `"https://kaggle.com/..."` | | `platform` | Hosting platform | `"Kaggle"` | | `tags` | Topic/method tags | `["nlp", "supervised", "tabular"]` | | `prize` | Cash prize | `"$2,117,152"` | | `additionalPrizes` | Non-cash prizes | `"AWS credits, conference ticket"` | | `deadline` | Submission deadline | `"31 Dec 2026"` | | `registrationDeadline` | Registration cutoff | `"15 Nov 2026"` | | `launched` | Competition start date | `"1 Jan 2026"` | | `added` | Date added to mlcontests | `"3 Jan 2026"` | | `sponsor` | Sponsor name | `"Google DeepMind"` | | `conference` | Associated conference | `"NeurIPS"` | | `conferenceYear` | Conference year | `2026` | | `dataSize` | Dataset size description | `"10 GB"` | | `additionalUrls` | Related URLs | `["https://workshop.example.com"]` | | `note` | Extra notes | `"Open to students only"` | | `endDate` | End date if different from deadline | `"15 Jan 2027"` | ### How much does it cost to scrape ML competitions? This actor uses **Pay-Per-Event** (PPE) pricing. You pay a small start fee per run, plus a tiny amount per competition extracted. | Plan | Cost per result | |------|----------------| | FREE | $0.000345 | | BRONZE | $0.0003 | | SILVER | $0.000234 | | GOLD | $0.00018 | | PLATINUM | $0.00012 | | DIAMOND | $0.000084 | **Example costs:** - Scraping all ~329 competitions (BRONZE plan): ~$0.005 start + 329 × $0.0003 = **~$0.104 per run** - Filtering for Kaggle competitions only (~95 results): **~$0.034 per run** - Filtering by tag (e.g., "nlp", ~62 results): **~$0.024 per run** 💡 **Free plan estimate:** With Apify's free tier, you can run this actor regularly and collect hundreds of competitions per month for free. ### How to use it 1. **Open the actor** on [Apify Store](https://apify.com/automation-lab/mlcontests-scraper) 2. **Configure filters** (optional): - Leave all filters empty to get ALL current competitions - Add platform names to filter (e.g., `Kaggle`, `Zindi`) - Add tags to filter by topic (e.g., `nlp`, `vision`, `timeseries`) - Enable "Prize contests only" to exclude no-prize competitions 3. **Click Run** — the actor completes in under 15 seconds 4. **Export results** as JSON, CSV, or Excel from the Results tab ### Input parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `platforms` | array | `[]` | Filter by platform name(s). Case-insensitive partial match. Leave empty for all platforms. | | `tags` | array | `[]` | Filter by tag(s). Exact match from the competition's tag list. Leave empty for all tags. | | `prizeOnly` | boolean | `false` | Only return competitions with a cash prize. | | `maxResults` | integer | `0` | Maximum number of results. `0` means no limit. | #### Platform filter examples ```json { "platforms": ["Kaggle"] } { "platforms": ["DrivenData", "Zindi"] } { "platforms": ["CrunchDAO"] } ```` #### Tag filter examples ```json { "tags": ["nlp"] } { "tags": ["vision", "medical"] } { "tags": ["timeseries", "forecasting"] } ``` ### Output examples #### Full competition record ```json { "name": "Detect Structural Breaks in Real-Time Time Series Data", "url": "https://hub.crunchdao.com/competitions/structural-break-real-time?ref=mlcontests", "platform": "CrunchDAO", "tags": ["climate", "finance", "healthcare", "timeseries", "forecasting"], "prize": "$100,000", "additionalPrizes": null, "deadline": "17 Sep 2026", "registrationDeadline": "31 Dec 2026", "launched": "6 May 2026", "added": null, "sponsor": "ADIA Lab", "conference": null, "conferenceYear": null, "dataSize": null, "additionalUrls": [], "note": null, "endDate": null } ``` #### Conference-affiliated competition ```json { "name": "Foundation Models for General CT Image Diagnosis", "url": "https://www.codabench.org/competitions/12650/?ref=mlcontests", "platform": "Codabench", "tags": ["supervised", "classification", "vision", "medical", "science", "deep learning"], "prize": null, "additionalPrizes": "Amazon gift cards (300/200/100 CAD) for top 3; poster presentation invitation", "deadline": "15 May 2026", "registrationDeadline": null, "launched": null, "added": "15 Apr 2026", "sponsor": null, "conference": "CVPR", "conferenceYear": 2026, "dataSize": null, "additionalUrls": ["https://fmv-cvpr26workshop.github.io/"], "note": null, "endDate": null } ``` ### Tips for best results 🔍 **Discover niche competitions** — use tag filters like `robotics`, `audio`, `reinforcement-learning`, or `science` to find specialised competitions you might miss browsing mainstream platforms. 📅 **Schedule for daily updates** — competitions open and close daily. Set up a [scheduled task](https://docs.apify.com/scheduler) to run this actor every morning and save results to a dataset you monitor. 💡 **Combine filters** — you can use both platform and tag filters simultaneously to narrow down to exactly what you need (e.g., `platforms: ["Kaggle"]` + `tags: ["nlp"]`). ⚡ **No proxy needed** — mlcontests.com is a public site with no anti-bot measures. This actor runs with no proxy and completes in under 10 seconds. 🔗 **Follow competition URLs** — each result includes the direct competition URL with the mlcontests referral tag. Click through to register directly on the hosting platform. ### Integrations #### 🔔 Daily competition alert (Zapier/Make) Run this actor daily, then pipe the results into a Zapier/Make webhook that filters for new competitions (by checking `added` date) and sends a Slack or email alert. ``` [ML Contests Scraper] → [Filter: added = today] → [Slack: "New competition: {name} on {platform}, prize: {prize}"] ``` #### 📊 Google Sheets dashboard Export results directly to Google Sheets using Apify's built-in Google Sheets integration. Schedule daily runs to auto-update your competition tracker. ``` [ML Contests Scraper] → [Apify → Google Sheets integration] → [Competition Tracker spreadsheet] ``` #### 🗂️ Notion competition database Use [Apify's Notion integration](https://apify.com/store?search=notion) to push competition records to a Notion database with filtered views by platform, tag, and deadline. #### 🤖 AI research agent Feed competition data to an LLM via the Apify API to automatically generate personalised competition recommendations based on your skills and past projects. ### API usage #### Node.js ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_API_TOKEN' }); const run = await client.actor('automation-lab/mlcontests-scraper').call({ platforms: ['Kaggle', 'DrivenData'], tags: ['nlp'], prizeOnly: true, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(`Found ${items.length} competitions`); items.forEach(c => console.log(`${c.name} — ${c.prize} — ${c.deadline}`)); ``` #### Python ```python from apify_client import ApifyClient client = ApifyClient(token="YOUR_API_TOKEN") run = client.actor("automation-lab/mlcontests-scraper").call(run_input={ "platforms": ["Kaggle", "DrivenData"], "tags": ["nlp"], "prizeOnly": True, }) dataset = client.dataset(run["defaultDatasetId"]) for item in dataset.iterate_items(): print(f"{item['name']} — {item['prize']} — {item['deadline']}") ``` #### cURL ```bash curl -X POST \ "https://api.apify.com/v2/acts/automation-lab~mlcontests-scraper/runs?token=YOUR_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "platforms": ["Kaggle"], "prizeOnly": true }' ``` ### Use with Claude (MCP) You can use this actor as an MCP (Model Context Protocol) tool directly in Claude Code, Claude Desktop, Cursor, or VS Code. #### Claude Code ```bash claude mcp add --transport http apify "https://mcp.apify.com?tools=automation-lab/mlcontests-scraper" ``` Then ask Claude: - *"Find all current NLP competitions on Kaggle with prizes over $10,000"* - *"What machine learning competitions are affiliated with NeurIPS 2026?"* - *"List all DrivenData competitions with a deadline in the next 30 days"* #### Claude Desktop / Cursor / VS Code Add to your MCP config (`claude_desktop_config.json` or `.cursor/mcp.json`): ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com?tools=automation-lab/mlcontests-scraper", "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" } } } } ``` **Example Claude prompts:** - *"Run the mlcontests scraper and show me all vision competitions with prizes"* - *"Which medical AI competitions are currently open on Codabench?"* - *"Get all competitions tagged with 'timeseries' and their deadlines"* ### Legality and terms of service This actor only scrapes **publicly available data** from mlcontests.com that is visible to any visitor without authentication. The data is used to power public discovery of open competitions. - mlcontests.com does not have an explicit prohibition on automated access in its Terms of Service - This actor respects server load by making a single HTTP request per run - Competition URLs are used as-is with the standard mlcontests referral tag Always review the terms of service of individual competition platforms before participating or redistributing their data. ### FAQ #### Which platforms are covered? mlcontests.com covers 20+ platforms including Kaggle, DrivenData, Zindi, CrunchDAO, AIcrowd, Codabench, HuggingFace Competitions, NumerAI, Signate, and many independent competitions. The platform field in each result tells you exactly where each competition is hosted. #### How often is the data updated? mlcontests.com updates their competition list regularly. Run this actor daily to stay current with new competitions and deadline changes. #### Can I get past/completed competitions? No — mlcontests.com only displays currently active competitions. This actor extracts whatever is on the live page at the time of the run. #### What does the "tags" field contain? Tags describe the competition type, domain, and methodology. Common tags include: `nlp`, `vision`, `tabular`, `timeseries`, `audio`, `reinforcement-learning`, `supervised`, `unsupervised`, `medical`, `finance`, `climate`, `robotics`, `science`, `deep learning`, `measurable`, `hardware`, `inference`. #### Why does my filtered run return fewer results than expected? Platform filter uses case-insensitive partial matching (e.g., `"kaggle"` matches `"Kaggle"`). Tag filter requires exact match from the tag list. If you're not getting expected results, try a broader filter or check the platform/tag spelling in a full (unfiltered) run first. #### The actor returned 0 results — is something wrong? If you get 0 results with an unfiltered run, mlcontests.com may have temporarily changed its page structure. File an issue on [Apify Store](https://apify.com/automation-lab/mlcontests-scraper) and we'll investigate. Filtered runs returning 0 is normal if no competitions match your criteria. ### Related scrapers - [Kaggle Scraper](https://apify.com/automation-lab/kaggle-scraper) — deep-dive scraper for Kaggle-specific data - [HuggingFace Datasets Scraper](https://apify.com/automation-lab/huggingface-scraper) — scrape datasets and models from HuggingFace Hub - [arXiv Scraper](https://apify.com/automation-lab/arxiv-scraper) — scrape research papers from arXiv.org # Actor input Schema ## `platforms` (type: `array`): Only return contests from specific platforms. Leave empty to return all platforms. Examples: Kaggle, DrivenData, Zindi, CrunchDAO, AIcrowd, Codabench. ## `tags` (type: `array`): Only return contests matching specific tags. Examples: nlp, vision, timeseries, tabular, medical, finance. ## `prizeOnly` (type: `boolean`): If enabled, only return contests that have a cash prize. ## `maxResults` (type: `integer`): Maximum number of contests to return. Set to 0 to return all matching contests. ## Actor input object example ```json { "platforms": [], "tags": [], "prizeOnly": false, "maxResults": 0 } ``` # Actor output Schema ## `overview` (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 = { "platforms": [], "tags": [], "maxResults": 0 }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/mlcontests-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 = { "platforms": [], "tags": [], "maxResults": 0, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/mlcontests-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 '{ "platforms": [], "tags": [], "maxResults": 0 }' | apify call automation-lab/mlcontests-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/mlcontests-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ML Contests Scraper", "description": "Scrape machine learning, data science, and robotics competitions from mlcontests.com", "version": "0.1", "x-build-id": "tKKfhUqfx5hD3hzeN" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~mlcontests-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-mlcontests-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~mlcontests-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-mlcontests-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~mlcontests-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-mlcontests-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "platforms": { "title": "🏆 Filter by Platform", "type": "array", "description": "Only return contests from specific platforms. Leave empty to return all platforms. Examples: Kaggle, DrivenData, Zindi, CrunchDAO, AIcrowd, Codabench.", "items": { "type": "string" } }, "tags": { "title": "🏷️ Filter by Tag", "type": "array", "description": "Only return contests matching specific tags. Examples: nlp, vision, timeseries, tabular, medical, finance.", "items": { "type": "string" } }, "prizeOnly": { "title": "💰 Prize contests only", "type": "boolean", "description": "If enabled, only return contests that have a cash prize.", "default": false }, "maxResults": { "title": "📊 Max results", "minimum": 0, "maximum": 1000, "type": "integer", "description": "Maximum number of contests to return. Set to 0 to return all matching contests.", "default": 0 } } }, "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 } } } } } } } } } } ```