# WorkIndia Candidates URLs Scraper (`getdataforme/workindia-candidates-urls-jb-scraper`) Actor The WorkIndia Candidates URLs Scraper extracts candidate profiles from WorkIndia.com using customizable filters for cities, industries, and job titles.... - **URL**: https://apify.com/getdataforme/workindia-candidates-urls-jb-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** Lead generation, Jobs, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## WorkIndia Candidates URLs Scraper ### Introduction The WorkIndia Candidates URLs Scraper is designed to efficiently extract candidate information from WorkIndia.com based on specified filters such as cities, industries, and job titles. This tool provides valuable insights into potential candidates by scraping detailed profiles, making it an essential asset for recruitment agencies, HR departments, and market researchers. ### Features - **Customizable Filters**: Tailor searches with city, industry, and job title filters. - **Sorting Options**: Sort candidates by newest, location relevance, or activity level. - **Scalable Data Extraction**: Extract up to 10 candidate profiles per run. - **High-Quality Data**: Reliable and structured data output for easy analysis. - **User-Friendly Interface**: Simple setup with intuitive input parameters. ### Input Parameters | Parameter | Type | Required | Description | Example | |---------------|---------|----------|-----------------------------------------------------------------------------|------------------------------| | cities | array | Yes | City names to filter candidates by. | `["delhi"]` | | industries | array | Yes | Industry names to filter candidates by. | `["accounts"]` | | jobTitles | array | Yes | Job titles to filter candidates by. | `["Accountant"]` | | sortNew | boolean | No | Sort candidates by newest first. | `true` | | sortLocation | boolean | No | Sort candidates by location relevance. | `false` | | sortActive | boolean | No | Sort candidates by most active. | `false` | | maxItems | integer | No | Maximum number of candidates to scrape. | `10` | ### Example Usage #### Input JSON ```json { "cities": ["delhi"], "industries": ["accounts"], "jobTitles": ["Accountant"], "sortNew": true, "maxItems": 5 } ```` #### Output JSON ```json [ { "name": "Deepak", "gender": "male", "age": 21, "matchPercentage": 100, "location": "Sector 62 A - Noida", "city": "", "joined": "2026-07-12T15:52:07+05:30", "interestedIn": [ "accounts", "back_office", "business_development", "telecalling" ], "english": "Good English", "education": { "qualification": "graduate", "course": "BCA - Computers", "detailed": "Bca in Computers from Iimt Engineering College" }, "experience": { "level": "Fresher", "years": null, "previousJob": "", "companyName": "", "duration": "fresher", "sector": "accounts", "details": [] }, "skills": [ "Basic Excel", "Computer", "Email", "Good Communication Skills", "MS Power Point" ], "isNewCandidate": false, "isActiveCandidate": true, "id": "1956015977c512ba301ef40a962edeea4f25edd90d4511e88fd8aeb97862399a", "mobileNo": "", "salary": { "current": "-" }, "languages": [ "English" ], "documents": [], "hasResume": false, "profileImage": "", "status": { "isWorking": false, "lookingUrgently": false, "leadType": "matched" }, "assessmentSkills": [ "english", "good_english" ], "lastSeen": "2026-07-12T10:30:21Z", "scrapedAt": "2026-07-12T11:05:14.864Z" } ] ``` ### Use Cases - **Market Research and Analysis**: Gain insights into candidate demographics and skills. - **Competitive Intelligence**: Understand competitor recruitment strategies. - **Price Monitoring**: Analyze salary trends in various industries. - **Content Aggregation**: Compile data for reports or presentations. - **Academic Research**: Support studies on employment trends. - **Business Automation**: Streamline HR processes with automated candidate sourcing. ### Installation and Usage 1. Search for "WorkIndia Candidates URLs Scraper" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters as needed. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). ### Output Format The output is a JSON array of candidate objects, each containing fields such as `name`, `gender`, `age`, `matchPercentage`, `location`, and more. Key fields include: - **name**: Candidate's name. - **gender**: Gender of the candidate. - **age**: Age of the candidate. - **matchPercentage**: Matching percentage based on filters. - **location**: Current location of the candidate. - **education**: Educational background details. - **experience**: Work experience details. - **skills**: List of skills possessed by the candidate. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: [Contact Us](https://getdataforme.com/contact/) We're here to help you get the most out of this Actor! # Actor input Schema ## `cities` (type: `array`): City names to filter candidates by ## `industries` (type: `array`): Industry names to filter candidates by ## `jobTitles` (type: `array`): Job titles to filter candidates by ## `sortNew` (type: `boolean`): Sort candidates by newest first ## `sortLocation` (type: `boolean`): Sort candidates by location relevance ## `sortActive` (type: `boolean`): Sort candidates by most active ## `maxItems` (type: `integer`): Maximum number of candidates to scrape ## Actor input object example ```json { "cities": [ "delhi" ], "industries": [ "accounts" ], "jobTitles": [ "Accountant" ], "sortNew": true, "sortLocation": false, "sortActive": false, "maxItems": 10 } ``` # 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 = { "cities": [ "delhi" ], "industries": [ "accounts" ], "jobTitles": [ "Accountant" ] }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/workindia-candidates-urls-jb-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 = { "cities": ["delhi"], "industries": ["accounts"], "jobTitles": ["Accountant"], } # Run the Actor and wait for it to finish run = client.actor("getdataforme/workindia-candidates-urls-jb-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 '{ "cities": [ "delhi" ], "industries": [ "accounts" ], "jobTitles": [ "Accountant" ] }' | apify call getdataforme/workindia-candidates-urls-jb-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/workindia-candidates-urls-jb-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "WorkIndia Candidates URLs Scraper", "description": "The WorkIndia Candidates URLs Scraper extracts candidate profiles from WorkIndia.com using customizable filters for cities, industries, and job titles....", "version": "0.0", "x-build-id": "6oumSTiR7bCdZ7uLf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~workindia-candidates-urls-jb-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-workindia-candidates-urls-jb-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~workindia-candidates-urls-jb-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-workindia-candidates-urls-jb-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~workindia-candidates-urls-jb-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-workindia-candidates-urls-jb-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "cities", "industries", "jobTitles" ], "properties": { "cities": { "title": "Cities", "type": "array", "description": "City names to filter candidates by", "default": [ "delhi" ], "items": { "type": "string" } }, "industries": { "title": "Industries", "type": "array", "description": "Industry names to filter candidates by", "default": [ "accounts" ], "items": { "type": "string" } }, "jobTitles": { "title": "Job Titles", "type": "array", "description": "Job titles to filter candidates by", "default": [ "Accountant" ], "items": { "type": "string" } }, "sortNew": { "title": "Sort by Newest", "type": "boolean", "description": "Sort candidates by newest first", "default": true }, "sortLocation": { "title": "Sort by Location", "type": "boolean", "description": "Sort candidates by location relevance", "default": false }, "sortActive": { "title": "Sort by Active", "type": "boolean", "description": "Sort candidates by most active", "default": false }, "maxItems": { "title": "Max Items", "minimum": 1, "type": "integer", "description": "Maximum number of candidates to scrape", "default": 10 } } }, "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 } } } } } } } } } } ```