# Jobdocket Category Scraper (`getdataforme/jobdocket-category-scraper`) Actor Extract job listings from Jobdocket categories with this reliable Apify Actor. Powered by PlaywrightCrawler, it scrapes titles, companies, locations, salaries, and descriptions into structured JSON. Perfect for market research, competitive intelligence, and automation.... - **URL**: https://apify.com/getdataforme/jobdocket-category-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** Automation, Jobs, Lead generation - **Stats:** 2 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 Category Scraper ### Introduction The Jobdocket Category Scraper is a powerful Apify Actor designed to extract job listings from Jobdocket's category pages. It leverages PlaywrightCrawler for reliable web scraping, delivering structured data on job titles, companies, locations, salaries, and more. This tool empowers businesses and researchers to gather insights from job markets efficiently, enabling data-driven decisions without manual effort. ### Features - **Comprehensive Job Data Extraction**: Scrapes detailed job information including titles, companies, locations, types, salaries, and descriptions from Jobdocket categories. - **High Reliability with Playwright**: Utilizes PlaywrightCrawler for robust handling of dynamic web content, ensuring accurate data collection even from complex sites. - **Customizable Start URLs**: Allows users to specify multiple starting URLs for targeted scraping across different job categories. - **Structured JSON Output**: Produces clean, machine-readable JSON data for easy integration with databases, analytics tools, or downstream applications. - **Scalable Performance**: Optimized for handling large volumes of job listings with efficient crawling and minimal resource usage. - **Error-Resilient Scraping**: Includes built-in mechanisms to handle common web scraping challenges like timeouts and anti-bot measures. - **Real-Time Crawling Timestamps**: Each scraped item includes a "crawledAt" field for tracking data freshness. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|------|----------|-------------|---------| | startUrls | array | Yes | An array of URLs to start the scraping process. Each URL should point to a Jobdocket category page. | `[{"url": "https://jobdocket.com/category/software-engineering"}]` | ### Example Usage To run the Jobdocket Category Scraper, provide input parameters in JSON format. Here's an example: ```json { "startUrls": [ { "url": "https://jobdocket.com/category/software-engineering" }, { "url": "https://jobdocket.com/category/data-science" } ] } ```` This will scrape job listings from the specified category pages. The output will be a JSON array of job objects, such as: ```json [ { "title": "Apify platform", "company": "Not specified", "location": "Not specified", "type": "Not specified", "salary": "Not Specified", "description": "Apify platform", "jobUrl": null, "sourceUrl": "https://apify.com", "crawledAt": "2026-02-10T11:11:48.232Z" } ] ``` ### Use Cases - **Market Research and Analysis**: Analyze job market trends by scraping listings from various categories to identify in-demand skills and salary ranges. - **Competitive Intelligence**: Monitor competitors' hiring patterns by extracting job data from industry-specific categories. - **Price Monitoring**: Track salary information for roles in different sectors to inform pricing strategies for freelance or consulting services. - **Content Aggregation**: Build job portals or newsletters by aggregating listings from multiple Jobdocket categories. - **Academic Research**: Collect data for studies on employment trends, regional job availability, or skill gaps in specific fields. - **Business Automation**: Automate lead generation for recruitment agencies by scraping and filtering job postings based on criteria. ### Installation and Usage 1. Search for "Jobdocket Category Scraper" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters (e.g., add start URLs). 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 Actor outputs a JSON array of objects, each representing a scraped job listing. Key fields include: - `title`: The job title. - `company`: The hiring company (may be "Not specified"). - `location`: Job location (may be "Not specified"). - `type`: Job type (e.g., full-time; may be "Not specified"). - `salary`: Salary information (may be "Not Specified"). - `description`: A brief job description. - `jobUrl`: Direct link to the job posting (null if unavailable). - `sourceUrl`: The URL from which the job was scraped. - `crawledAt`: Timestamp of when the data was crawled (ISO 8601 format). This structured format ensures easy parsing and integration. ### 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. ## Actor input object example ```json { "startUrls": [ { "url": "https://apify.com" } ] } ``` # 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://apify.com" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/jobdocket-category-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://apify.com" }] } # Run the Actor and wait for it to finish run = client.actor("getdataforme/jobdocket-category-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://apify.com" } ] }' | apify call getdataforme/jobdocket-category-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/jobdocket-category-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Jobdocket Category Scraper", "description": "Extract job listings from Jobdocket categories with this reliable Apify Actor. Powered by PlaywrightCrawler, it scrapes titles, companies, locations, salaries, and descriptions into structured JSON. Perfect for market research, competitive intelligence, and automation....", "version": "0.0", "x-build-id": "le5HlwWcR3OkA5ba4" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~jobdocket-category-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-jobdocket-category-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~jobdocket-category-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-jobdocket-category-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~jobdocket-category-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-jobdocket-category-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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" } } } } } }, "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 } } } } } } } } } } ```