# Jobdocket Profile Scraper (`getdataforme/jobdocket-profile-scraper`) Actor Extract comprehensive job data effortlessly with Jobdocket Profile Scraper, an Apify Actor that automates scraping of job listings from Jobdocket and similar sites.... - **URL**: https://apify.com/getdataforme/jobdocket-profile-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** Automation, Lead generation, Jobs - **Stats:** 3 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.05 / actor start 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 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 ## Jobdocket Profile Scraper The Jobdocket Profile Scraper is a powerful Apify Actor designed to extract detailed job profile data from Jobdocket and similar platforms. It automates the collection of comprehensive job listings, including company details, responsibilities, and application information, enabling efficient data gathering for recruitment, research, and market analysis. This tool ensures high-quality, structured data output to streamline your workflows and decision-making processes. ### Features - **Comprehensive Data Extraction**: Scrapes key job details such as title, company, location, employment type, categories, and full descriptions. - **Reliable Crawling**: Utilizes CheerioCrawler for fast, efficient web scraping with built-in error handling and retries. - **Customizable Inputs**: Supports multiple start URLs and configurable request limits for targeted or broad data collection. - **Structured Output**: Delivers clean JSON data with fields like responsibilities, requirements, and application links. - **Performance Optimized**: Handles up to 100 requests per crawl by default, scalable for larger datasets. - **No-Code Friendly**: Easy to configure via the Apify platform, ideal for non-technical users. - **Data Integrity**: Includes metadata like crawl timestamps and raw descriptions for verification. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|------|----------|-------------|---------| | startUrls | array | Yes | URLs to start the crawl with. Each URL should point to a job listing or profile page. | [{"url": "https://jobdocket.com/job/example"}] | | maxRequestsPerCrawl | integer | No | Maximum number of requests the crawler can make. Helps control costs and avoid overloading sites. | 100 | ### Example Usage #### Input JSON ```json { "startUrls": [ { "url": "https://jobdocket.com/job/software-engineer" } ], "maxRequestsPerCrawl": 50 } ```` #### Output JSON ```json [ { "url": "https://apify.com", "crawledAt": "2026-02-10T11:12:32.927Z", "title": "", "company": "N/A", "location": "N/A", "employmentType": "N/A", "categories": [], "aboutCompany": "", "description": "", "responsibilities": [], "requirements": [], "requirementsHtml": "", "apply": { "email": null, "careersPage": null }, "rawMetaDescription": "Cloud platform for web scraping, browser automation, AI agents, and data for AI. Use 10,000+ ready-made tools, code templates, or order a custom solution.", "pageTitle": "Apify: Full-stack web scraping and data extraction platform" } ] ``` ### Use Cases - **Market Research and Analysis**: Gather job market trends, salary data, and industry demands from multiple listings. - **Competitive Intelligence**: Monitor competitors' job postings to understand hiring strategies and skill requirements. - **Price Monitoring**: Track job-related data for pricing models in recruitment services. - **Content Aggregation**: Compile job data for blogs, newsletters, or databases. - **Academic Research**: Collect data for studies on employment patterns and labor markets. - **Business Automation**: Automate lead generation by identifying companies and their open positions. ### Installation and Usage 1. Search for "Jobdocket Profile Scraper" in the Apify Store 2. Click "Try for free" or "Run" 3. Configure input parameters 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 an array of JSON objects, each representing a scraped job profile. Key fields include: - `url`: The source URL of the job listing. - `crawledAt`: Timestamp of when the data was scraped. - `title`: Job title (may be empty if not found). - `company`: Company name or "N/A". - `location`: Job location or "N/A". - `employmentType`: Type of employment (e.g., full-time) or "N/A". - `categories`: Array of job categories. - `aboutCompany`: Description of the company. - `description`: Full job description. - `responsibilities`: Array of job responsibilities. - `requirements`: Array of job requirements. - `requirementsHtml`: HTML version of requirements. - `apply`: Object with email and careers page links (may be null). - `rawMetaDescription`: Raw meta description from the page. - `pageTitle`: Title of the webpage. This structured format ensures easy parsing and integration into databases or analysis tools. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! *** # Actor input Schema ## `startUrls` (type: `array`): URLs to start with. ## `maxRequestsPerCrawl` (type: `integer`): Maximum number of requests that can be made by this crawler. ## Actor input object example ```json { "startUrls": [ { "url": "https://crawlee.dev" } ], "maxRequestsPerCrawl": 100 } ``` # 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 = { "startUrls": [ { "url": "https://crawlee.dev" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/jobdocket-profile-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 = { "startUrls": [{ "url": "https://crawlee.dev" }] } # Run the Actor and wait for it to finish run = client.actor("getdataforme/jobdocket-profile-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 '{ "startUrls": [ { "url": "https://crawlee.dev" } ] }' | apify call getdataforme/jobdocket-profile-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/jobdocket-profile-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Jobdocket Profile Scraper", "description": "Extract comprehensive job data effortlessly with Jobdocket Profile Scraper, an Apify Actor that automates scraping of job listings from Jobdocket and similar sites....", "version": "0.0", "x-build-id": "cPUHAky30gTyvm1IN" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~jobdocket-profile-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-jobdocket-profile-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~jobdocket-profile-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-jobdocket-profile-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~jobdocket-profile-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-jobdocket-profile-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "startUrls": { "title": "Start URLs", "type": "array", "description": "URLs to start with.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxRequestsPerCrawl": { "title": "Max Requests per Crawl", "type": "integer", "description": "Maximum number of requests that can be made by this crawler.", "default": 100 } } }, "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 } } } } } } } } } } ```