# Job Posting Deduper & Scorer (`knotted_tussock/job-posting-deduper-scorer`) Actor Clean, deduplicate, normalize, and score job posting data from multiple sources for job seekers, recruiters, career coaches, and AI job-search agents. - **URL**: https://apify.com/knotted\_tussock/job-posting-deduper-scorer.md - **Developed by:** [Ralph T](https://apify.com/knotted_tussock) (community) - **Categories:** Jobs, AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Job Posting Deduper & Scorer Clean, deduplicate, normalize, and score job posting records from multiple sources. ### Use cases - Combine jobs from LinkedIn, Indeed, Google Jobs, company career pages, and custom sources. - Remove duplicate jobs syndicated across multiple boards. - Rank jobs for a specific job seeker or search profile. - Prepare clean data for AI job-search agents, alerts, spreadsheets, and workflows. ### Input options Provide records directly: ```json { "jobRecords": [ { "title": "Remote Data Engineer", "company": "ExampleCo", "location": "Remote", "url": "https://example.com/job/1" } ], "idealKeywords": ["remote", "data", "engineer"], "locations": ["remote"], "remoteOnly": false } ```` Or pass an Apify `datasetId` from another jobs Actor. ### Output Each output row is one canonical job cluster with: - normalized title/company/location - duplicate count and source URLs - best apply URL - remote and salary signals - score, matched keywords, and flags This Actor does not scrape job boards by itself; it improves job records produced by other Actors or APIs. # Actor input Schema ## `jobRecords` (type: `array`): Job records from other scrapers/datasets. Common fields like title/jobTitle, company, location, url/applyUrl, salary, postedAt are supported. ## `datasetId` (type: `string`): Optional Apify dataset ID to read records from when jobRecords is empty. ## `idealKeywords` (type: `array`): Keywords that increase a job's score when present in title, company, location, or description fields. ## `excludeKeywords` (type: `array`): Keywords that reduce a job's score or mark it as low quality. ## `locations` (type: `array`): Preferred location terms to boost, such as remote, United States, London, or New York. ## `remoteOnly` (type: `boolean`): When enabled, only output jobs with remote/work-from-home signals. ## `salaryMinimum` (type: `integer`): Optional minimum annual salary; records below this are penalized when salary can be parsed. ## `maxRecords` (type: `integer`): Maximum number of input records to read and process. ## Actor input object example ```json { "jobRecords": [], "idealKeywords": [ "remote", "engineer", "developer", "data", "ai" ], "excludeKeywords": [ "commission only", "unpaid", "recruiter" ], "locations": [ "remote" ], "remoteOnly": false, "salaryMinimum": 0, "maxRecords": 1000 } ``` # Actor output Schema ## `results` (type: `string`): Deduplicated and scored job clusters in the default dataset. ## `summary` (type: `string`): Summary JSON with input counts, unique jobs, duplicates removed, and scoring stats. # 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 = { "idealKeywords": [ "remote", "engineer", "developer", "data", "ai" ], "excludeKeywords": [ "commission only", "unpaid", "recruiter" ], "locations": [ "remote" ] }; // Run the Actor and wait for it to finish const run = await client.actor("knotted_tussock/job-posting-deduper-scorer").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 = { "idealKeywords": [ "remote", "engineer", "developer", "data", "ai", ], "excludeKeywords": [ "commission only", "unpaid", "recruiter", ], "locations": ["remote"], } # Run the Actor and wait for it to finish run = client.actor("knotted_tussock/job-posting-deduper-scorer").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 '{ "idealKeywords": [ "remote", "engineer", "developer", "data", "ai" ], "excludeKeywords": [ "commission only", "unpaid", "recruiter" ], "locations": [ "remote" ] }' | apify call knotted_tussock/job-posting-deduper-scorer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=knotted_tussock/job-posting-deduper-scorer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Job Posting Deduper & Scorer", "description": "Clean, deduplicate, normalize, and score job posting data from multiple sources for job seekers, recruiters, career coaches, and AI job-search agents.", "version": "0.1", "x-build-id": "nye3harfZGzcS0zMH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/knotted_tussock~job-posting-deduper-scorer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-knotted_tussock-job-posting-deduper-scorer", "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/knotted_tussock~job-posting-deduper-scorer/runs": { "post": { "operationId": "runs-sync-knotted_tussock-job-posting-deduper-scorer", "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/knotted_tussock~job-posting-deduper-scorer/run-sync": { "post": { "operationId": "run-sync-knotted_tussock-job-posting-deduper-scorer", "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": { "jobRecords": { "title": "Job records", "type": "array", "description": "Job records from other scrapers/datasets. Common fields like title/jobTitle, company, location, url/applyUrl, salary, postedAt are supported.", "default": [] }, "datasetId": { "title": "Source dataset ID", "type": "string", "description": "Optional Apify dataset ID to read records from when jobRecords is empty." }, "idealKeywords": { "title": "Ideal keywords", "type": "array", "description": "Keywords that increase a job's score when present in title, company, location, or description fields.", "items": { "type": "string" } }, "excludeKeywords": { "title": "Exclude keywords", "type": "array", "description": "Keywords that reduce a job's score or mark it as low quality.", "items": { "type": "string" } }, "locations": { "title": "Preferred locations", "type": "array", "description": "Preferred location terms to boost, such as remote, United States, London, or New York.", "items": { "type": "string" } }, "remoteOnly": { "title": "Remote only", "type": "boolean", "description": "When enabled, only output jobs with remote/work-from-home signals.", "default": false }, "salaryMinimum": { "title": "Minimum salary", "type": "integer", "description": "Optional minimum annual salary; records below this are penalized when salary can be parsed.", "default": 0 }, "maxRecords": { "title": "Max records", "minimum": 1, "maximum": 50000, "type": "integer", "description": "Maximum number of input records to read and process.", "default": 1000 } } }, "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 } } } } } } } } } } ```