# LinkedIn Jobs Scraper — Salary, Skills, Seniority & Easy Apply (`khadinakbar/linkedin-jobs-scraper`) Actor Extract LinkedIn jobs by keyword + location. Returns job title, company, parsed salary (min/max), skills, seniority, employment type, work type, easy-apply flag, applicant count & full description. No login required. Supports filters and bulk queries. - **URL**: https://apify.com/khadinakbar/linkedin-jobs-scraper.md - **Developed by:** [Khadin Akbar](https://apify.com/khadinakbar) (community) - **Categories:** Lead generation, Automation - **Stats:** 7 total users, 3 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $1.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## 🔍 LinkedIn Jobs Scraper — Skills, Salary & B2B Hiring Signals Extract LinkedIn job listings by keyword + location — no login, no cookies, no manual URL building required. Returns fully structured data including parsed salary (min/max numbers), required skills array, easy-apply flag, applicant count, seniority level, and full job description. The only LinkedIn jobs actor with a documented output schema — making it the best choice for AI agents (Claude, ChatGPT) and automated pipelines. Export scraped data, run via API, schedule recurring runs, or integrate with other tools. --- ### Why This Actor? Most LinkedIn scrapers either require you to manually build search URLs, return raw unstructured text, or cost 5-10x more per result. This actor is different: - **Simple input** — just type your job title and location. No URL building in incognito windows. - **Multi-query + multi-location** — run `["Software Engineer", "ML Engineer"]` × `["New York", "London"]` in one run - **Parsed salary** — returns `salary_min: 180000` and `salary_max: 240000`, not just a raw string - **Skills as array** — `required_skills: ["Python", "PyTorch"]` for easy filtering - **B2B hiring signals** — find companies actively hiring to power sales prospecting and competitive intel - **Built-in deduplication** — cross-query runs never return the same job twice - **Full output schema** — every field documented for AI agent compatibility --- ### Use Cases **For recruiters and talent teams:** Aggregate open roles across companies and locations. Monitor competitors' hiring activity. Build sourcing pipelines for specific skill sets. **For B2B sales teams:** Identify companies hiring for roles that signal budget and buying intent. Find hiring managers and decision-makers. Use job postings as warm outreach triggers. **For job seekers and market researchers:** Track salary trends across roles and geographies. Analyze which skills are most in-demand. Compare remote vs on-site availability by location. **For developers and data teams:** Feed structured job data into dashboards, CRMs, ATS systems, or AI pipelines. Schedule daily monitoring runs for specific searches. --- ### Input Parameters #### Simple keyword search (recommended) ```json { "searchQueries": ["Software Engineer", "Product Manager"], "locations": ["San Francisco, CA", "Remote"], "maxResults": 100, "datePosted": "r604800" } ```` This runs 4 searches: each query × each location. Results are automatically deduplicated. #### Direct URL mode (advanced) Open `linkedin.com/jobs` in an incognito window, apply your filters, copy the URL, and paste it here: ```json { "startUrls": [ { "url": "https://www.linkedin.com/jobs/search/?keywords=data+scientist&location=New+York&f_E=4&f_WT=2" } ], "maxResults": 200 } ``` #### With filters ```json { "searchQueries": ["DevOps Engineer"], "locations": ["United States"], "maxResults": 50, "experienceLevel": ["4", "5"], "workType": ["2", "3"], "jobType": ["F"], "salaryBase": "100000" } ``` *** ### Output Fields Each result is a JSON object with the following fields: | Field | Type | Description | |---|---|---| | `job_id` | string | LinkedIn's unique job ID | | `job_title` | string | null | Full job title | | `company_name` | string | null | Hiring company name | | `company_url` | string | null | LinkedIn company profile URL | | `company_size` | string | null | Employee count range | | `company_industry` | string | null | Industry category | | `location` | string | null | City, state, work arrangement | | `work_type` | string | null | Remote / Hybrid / On-site | | `employment_type` | string | null | Full-time / Part-time / Contract | | `seniority_level` | string | null | Entry / Mid-Senior / Director etc. | | `salary_range` | string | null | Raw salary string from LinkedIn | | `salary_min` | number | null | Parsed minimum salary (USD) | | `salary_max` | number | null | Parsed maximum salary (USD) | | `required_skills` | string\[] | Extracted skills list | | `is_easy_apply` | boolean | True if LinkedIn Easy Apply is enabled | | `applicant_count` | string | null | e.g. "142 applicants" | | `posted_at` | string | null | Relative posting time | | `job_description` | string | null | Full plain-text description | | `job_url` | string | Direct link to the job posting | | `search_query` | string | null | Query that found this job | | `scraped_at` | string | ISO timestamp of extraction | #### Example output record ```json { "job_id": "3812749302", "job_title": "Senior Machine Learning Engineer", "company_name": "Anthropic", "company_url": "https://www.linkedin.com/company/anthropic", "company_size": "201-500 employees", "company_industry": "Artificial Intelligence", "location": "San Francisco, CA (Hybrid)", "work_type": "Hybrid", "employment_type": "Full-time", "seniority_level": "Mid-Senior level", "salary_range": "$200,000–$280,000/yr", "salary_min": 200000, "salary_max": 280000, "required_skills": ["Python", "PyTorch", "RLHF", "Distributed Systems"], "is_easy_apply": false, "applicant_count": "142 applicants", "posted_at": "2 days ago", "job_description": "We are looking for a Senior ML Engineer...", "job_url": "https://www.linkedin.com/jobs/view/3812749302/", "search_query": "Machine Learning Engineer", "source_url": "https://www.linkedin.com/jobs/view/3812749302/", "scraped_at": "2026-03-31T14:22:00.000Z" } ``` *** ### Cost & Performance **Pricing:** $0.0005 per job result (PAY\_PER\_EVENT). No actor start fee. | Scenario | Jobs | Estimated Cost | |---|---|---| | Quick search | 50 | ~$0.025 | | Standard batch | 200 | ~$0.10 | | Large scrape | 1,000 | ~$0.50 | | Daily monitoring (50/day) | 1,500/month | ~$0.75/month | Compare: the dominant competitor charges $0.001/result (2x more). Premium competitors charge up to $0.005/result (10x more). **Performance:** ~25–50 jobs/minute depending on LinkedIn's response times and proxy routing. 50 jobs typically takes 2–4 minutes. *** ### Filters Reference | Filter | Values | |---|---| | `datePosted` | `r86400` (24h), `r604800` (week), `r2592000` (month) | | `jobType` | `F` (Full-time), `P` (Part-time), `C` (Contract), `T` (Temporary), `I` (Internship) | | `experienceLevel` | `1` (Intern), `2` (Entry), `3` (Associate), `4` (Mid-Senior), `5` (Director), `6` (Executive) | | `workType` | `1` (On-site), `2` (Remote), `3` (Hybrid) | | `salaryBase` | `40000`, `60000`, `80000`, `100000`, `120000` | *** ### Limitations - LinkedIn limits each search URL to ~1,000 results. Use multiple search queries to get more. - Salary data is only available on job postings where the employer has added it (typically 30–40% of postings in the US). - Skills extraction requires LinkedIn to expose the skills section on the public job page. For some postings, skills will be empty. - LinkedIn may show an auth wall on some requests. The actor retries with a fresh session when this happens. *** ### No Login Required This actor operates entirely on LinkedIn's public job search pages, which are accessible without an account. No cookies, no session tokens, no LinkedIn credentials needed. *** ### Support & Feedback Found a bug or want a new feature? Open an issue on this actor's page. We respond within 48 hours and typically ship fixes within a week. Works great with our [Google Jobs Scraper](https://apify.com/khadinakbar/google-jobs-scraper) for cross-platform job market research. *** ### Changelog **v0.1** — Initial release. Keyword + location search, multi-query cross-product, full detail page extraction, salary parsing, skills extraction, PPE pricing. # Actor input Schema ## `searchQueries` (type: `array`): Use this field when the user provides job titles, roles, or keywords to search. E.g. \['Software Engineer', 'Product Manager']. Use startUrls instead when the user provides direct LinkedIn search URLs. Supports multiple queries — each is run against every location. ## `locations` (type: `array`): List of locations to search in. Each query is cross-searched against each location. Use city names, states, or countries. E.g. \['New York', 'Remote', 'London']. Use 'United States' for nationwide US results. ## `startUrls` (type: `array`): Use this field instead of searchQueries when the user provides direct LinkedIn job search URLs (e.g. copied from linkedin.com/jobs/search). Open linkedin.com/jobs in an incognito window, apply your filters, and paste the URL here. Overrides searchQueries + locations when provided. ## `maxResults` (type: `integer`): Maximum number of job listings to return across all queries and locations combined. LinkedIn limits each search to ~1000 results. Use multiple queries to get more. ## `datePosted` (type: `string`): Filter jobs by how recently they were posted. 'Any time' returns all available jobs. Use 'Past 24 hours' for fresh postings. ## `jobType` (type: `array`): Filter by employment contract type. Leave empty to return all types. F=Full-time, P=Part-time, C=Contract, T=Temporary, I=Internship. ## `experienceLevel` (type: `array`): Filter by required experience level. Leave empty to return all levels. 1=Internship, 2=Entry level, 3=Associate, 4=Mid-Senior, 5=Director, 6=Executive. ## `workType` (type: `array`): Filter by on-site, remote, or hybrid work arrangement. Leave empty to return all. 1=On-site, 2=Remote, 3=Hybrid. ## `salaryBase` (type: `string`): Filter to only show jobs above a minimum annual salary. Note: LinkedIn only shows salary on a subset of postings. ## Actor input object example ```json { "searchQueries": [ "Software Engineer" ], "locations": [ "United States" ], "startUrls": [], "maxResults": 50, "datePosted": "any" } ``` # Actor output Schema ## `job_id` (type: `string`): LinkedIn's unique numeric job posting identifier. Use this to deduplicate across runs. ## `job_title` (type: `string`): Full job title as listed by the employer on LinkedIn. ## `company_name` (type: `string`): Name of the hiring company. ## `company_url` (type: `string`): URL to the company's LinkedIn profile. ## `company_size` (type: `string`): Employee count range for the hiring company, e.g. '1,001-5,000 employees'. ## `company_industry` (type: `string`): Industry category of the hiring company. ## `location` (type: `string`): City, state/country of the job as listed on LinkedIn. ## `work_type` (type: `string`): Work arrangement: Remote, Hybrid, or On-site. ## `employment_type` (type: `string`): Contract type: Full-time, Part-time, Contract, Temporary, Internship, or Volunteer. ## `seniority_level` (type: `string`): Required experience level: Internship, Entry level, Associate, Mid-Senior level, Director, Executive. ## `salary_range` (type: `string`): Raw salary string exactly as shown on LinkedIn, e.g. '$120,000-$160,000/yr'. Null if not listed. ## `salary_min` (type: `string`): Parsed minimum annual salary as a plain number in USD. Null if salary is not listed. ## `salary_max` (type: `string`): Parsed maximum annual salary as a plain number in USD. Null if salary is not listed. ## `required_skills` (type: `string`): Skills extracted from the LinkedIn skills section or parsed from the job description requirements section. ## `is_easy_apply` (type: `string`): True if the job supports LinkedIn one-click Easy Apply. False means applicants are redirected to an external site. ## `applicant_count` (type: `string`): Number of applicants as shown by LinkedIn, e.g. '142 applicants' or 'Over 200 applicants'. ## `posted_at` (type: `string`): When the job was posted, in LinkedIn's relative format (e.g. '2 days ago') or absolute date. ## `job_description` (type: `string`): Full plain-text job description including responsibilities, requirements, and benefits. ## `job_url` (type: `string`): Direct URL to the LinkedIn job posting. ## `search_query` (type: `string`): The keyword query used to find this job. ## `source_url` (type: `string`): The URL that was scraped to extract this record. ## `scraped_at` (type: `string`): ISO 8601 timestamp of when this record was extracted. # 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 = { "searchQueries": [ "Software Engineer" ], "locations": [ "United States" ], "startUrls": [], "maxResults": 50 }; // Run the Actor and wait for it to finish const run = await client.actor("khadinakbar/linkedin-jobs-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 = { "searchQueries": ["Software Engineer"], "locations": ["United States"], "startUrls": [], "maxResults": 50, } # Run the Actor and wait for it to finish run = client.actor("khadinakbar/linkedin-jobs-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 '{ "searchQueries": [ "Software Engineer" ], "locations": [ "United States" ], "startUrls": [], "maxResults": 50 }' | apify call khadinakbar/linkedin-jobs-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=khadinakbar/linkedin-jobs-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Jobs Scraper — Salary, Skills, Seniority & Easy Apply", "description": "Extract LinkedIn jobs by keyword + location. Returns job title, company, parsed salary (min/max), skills, seniority, employment type, work type, easy-apply flag, applicant count & full description. No login required. Supports filters and bulk queries.", "version": "0.1", "x-build-id": "d3kiDg8qbc4ePhH60" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/khadinakbar~linkedin-jobs-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-khadinakbar-linkedin-jobs-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/khadinakbar~linkedin-jobs-scraper/runs": { "post": { "operationId": "runs-sync-khadinakbar-linkedin-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/khadinakbar~linkedin-jobs-scraper/run-sync": { "post": { "operationId": "run-sync-khadinakbar-linkedin-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchQueries": { "title": "Job Search Queries", "type": "array", "description": "Use this field when the user provides job titles, roles, or keywords to search. E.g. ['Software Engineer', 'Product Manager']. Use startUrls instead when the user provides direct LinkedIn search URLs. Supports multiple queries — each is run against every location.", "items": { "type": "string" } }, "locations": { "title": "Locations", "type": "array", "description": "List of locations to search in. Each query is cross-searched against each location. Use city names, states, or countries. E.g. ['New York', 'Remote', 'London']. Use 'United States' for nationwide US results.", "items": { "type": "string" } }, "startUrls": { "title": "Start URLs", "type": "array", "description": "Use this field instead of searchQueries when the user provides direct LinkedIn job search URLs (e.g. copied from linkedin.com/jobs/search). Open linkedin.com/jobs in an incognito window, apply your filters, and paste the URL here. Overrides searchQueries + locations when provided.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxResults": { "title": "Maximum Results", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of job listings to return across all queries and locations combined. LinkedIn limits each search to ~1000 results. Use multiple queries to get more.", "default": 50 }, "datePosted": { "title": "Date Posted", "enum": [ "any", "r86400", "r604800", "r2592000" ], "type": "string", "description": "Filter jobs by how recently they were posted. 'Any time' returns all available jobs. Use 'Past 24 hours' for fresh postings.", "default": "any" }, "jobType": { "title": "Job Type", "type": "array", "description": "Filter by employment contract type. Leave empty to return all types. F=Full-time, P=Part-time, C=Contract, T=Temporary, I=Internship.", "items": { "type": "string", "enum": [ "F", "P", "C", "T", "I", "O" ], "enumTitles": [ "Full-time", "Part-time", "Contract", "Temporary", "Internship", "Other" ] } }, "experienceLevel": { "title": "Experience Level", "type": "array", "description": "Filter by required experience level. Leave empty to return all levels. 1=Internship, 2=Entry level, 3=Associate, 4=Mid-Senior, 5=Director, 6=Executive.", "items": { "type": "string", "enum": [ "1", "2", "3", "4", "5", "6" ], "enumTitles": [ "Internship", "Entry level", "Associate", "Mid-Senior level", "Director", "Executive" ] } }, "workType": { "title": "Work Type", "type": "array", "description": "Filter by on-site, remote, or hybrid work arrangement. Leave empty to return all. 1=On-site, 2=Remote, 3=Hybrid.", "items": { "type": "string", "enum": [ "1", "2", "3" ], "enumTitles": [ "On-site", "Remote", "Hybrid" ] } }, "salaryBase": { "title": "Minimum Salary (USD/yr)", "enum": [ "40000", "60000", "80000", "100000", "120000" ], "type": "string", "description": "Filter to only show jobs above a minimum annual salary. Note: LinkedIn only shows salary on a subset of postings." } } }, "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 } } } } } } } } } } ```