# Fresh Jobs Feed (`prince_gabriel/fresh-jobs-feed`) Actor Freshness-ranked job listings powered by FreshContext DAR scoring. - **URL**: https://apify.com/prince\_gabriel/fresh-jobs-feed.md - **Developed by:** [Immanuel Gabriel](https://apify.com/prince_gabriel) (community) - **Categories:** Jobs, E-commerce, Agents - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.25 / 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 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 ## Fresh Jobs Feed Apify Actor Fresh Jobs Feed is a minimal Apify Actor wrapper around the deployed Fresh Jobs Feed Cloudflare Worker API: ```text https://fresh-jobs-feed.gimmanuel73.workers.dev ```` It does not scrape job boards and does not duplicate the Worker. It calls the live API, writes each returned job signal to the default Apify Dataset, and stores the full API response in the default Key-Value Store as `OUTPUT`. Fresh Jobs Feed uses FreshContext DAR scoring: ```text rt_score = base_score * exp(-0.005 * age_hours) ``` AI/ML jobs are the default category and first marketable view. ### Input | Field | Type | Default | Description | | --- | --- | --- | --- | | `mode` | `"feed"`, `"fresh"`, or `"remote"` | `"feed"` | Selects the live API endpoint. | | `category` | `"ai"`, `"engineering"`, `"data"`, `"remote"`, or `"startup"` | `"ai"` | Category filter. | | `limit` | number | `10` | Number of job signals to return. Clamped to `1..100`. | | `min_score` | number | `0` | Minimum decay-adjusted `rt_score`. | | `source` | `"all"`, `"remotive"`, or `"remoteok"` | `"all"` | Optional source filter. | | `remote` | boolean | `false` | If true, requests remote jobs only. | | `q` | string | empty | Optional keyword filter such as `llm`, `python`, `rag`, or `mlops`. | | `includeExplain` | boolean | `false` | If true, attaches `/v1/jobs/explain/:id` scoring details to each dataset item. | ### Example Input ```json { "mode": "feed", "category": "ai", "limit": 10, "min_score": 20, "source": "all", "remote": false, "q": "llm", "includeExplain": true } ``` ### Example Dataset Item ```json { "id": "remoteok:1131520", "source": "remoteok", "title": "Engineering Manager AI Fleet Management & Honk", "company": "Spotify", "location": "Remote", "remote": true, "url": "https://remoteOK.com/remote-jobs/example", "salary": null, "tags": ["manager", "ai", "engineering"], "categories": ["ai", "engineering", "remote"], "base_score": 80, "rt_score": 55.4, "age_hours": 73.1, "published_at": "2026-05-08T16:00:00.000Z", "fetched_at": "2026-05-11T17:22:48.811Z", "mode": "feed", "actor_fetched_at": "2026-05-11T18:00:00.000Z", "source_api": "https://fresh-jobs-feed.gimmanuel73.workers.dev/v1/jobs/feed?category=ai&limit=10&min_score=20", "explanation": { "base_score": 80, "lambda": 0.005, "rt_score": 55.4, "formula": "rt_score = base_score * exp(-0.005 * age_hours)", "interpretation": "active" } } ``` The Key-Value Store `OUTPUT` record contains the normalized input, the full live API response, dataset item count, and generation timestamp. ### Use Cases - AI job agents that need freshness-ranked openings. - Job search automation that wants fewer stale recommendations. - Career tools and recruiting workflows. - Startup talent monitoring. - Market intelligence for current hiring demand. ### Local Run Install dependencies: ```bash npm install ``` Run locally: ```bash apify run ``` ### Push to Apify ```bash apify push ``` # Actor input Schema ## `mode` (type: `string`): Which Fresh Jobs Feed endpoint to call. ## `category` (type: `string`): Job category filter. AI/ML is the default marketable view. ## `limit` (type: `integer`): Maximum number of job signals to return. Values are clamped to 1-100. ## `min_score` (type: `number`): Minimum FreshContext decay-adjusted score to include. ## `source` (type: `string`): Optional upstream job source filter. ## `remote` (type: `boolean`): When true, request only jobs marked remote by the live API. ## `q` (type: `string`): Optional keyword search, for example llm, python, rag, or mlops. ## `includeExplain` (type: `boolean`): Fetch /v1/jobs/explain/:id for each returned job and attach scoring details. ## Actor input object example ```json { "mode": "feed", "category": "ai", "limit": 10, "min_score": 0, "source": "all", "remote": false, "includeExplain": false } ``` # Actor output Schema ## `jobs` (type: `string`): Dataset items containing freshness-ranked job listings. ## `summary` (type: `string`): Key-value store OUTPUT record containing normalized input, full API response, item count, and generation timestamp. # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("prince_gabriel/fresh-jobs-feed").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 = {} # Run the Actor and wait for it to finish run = client.actor("prince_gabriel/fresh-jobs-feed").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 '{}' | apify call prince_gabriel/fresh-jobs-feed --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=prince_gabriel/fresh-jobs-feed", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Fresh Jobs Feed", "description": "Freshness-ranked job listings powered by FreshContext DAR scoring.", "version": "0.1", "x-build-id": "jzo6JePORfZxFlIUb" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/prince_gabriel~fresh-jobs-feed/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-prince_gabriel-fresh-jobs-feed", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/prince_gabriel~fresh-jobs-feed/runs": { "post": { "operationId": "runs-sync-prince_gabriel-fresh-jobs-feed", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/prince_gabriel~fresh-jobs-feed/run-sync": { "post": { "operationId": "run-sync-prince_gabriel-fresh-jobs-feed", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "mode": { "title": "Feed mode", "enum": [ "feed", "fresh", "remote" ], "type": "string", "description": "Which Fresh Jobs Feed endpoint to call.", "default": "feed" }, "category": { "title": "Category", "enum": [ "ai", "engineering", "data", "remote", "startup" ], "type": "string", "description": "Job category filter. AI/ML is the default marketable view.", "default": "ai" }, "limit": { "title": "Limit", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of job signals to return. Values are clamped to 1-100.", "default": 10 }, "min_score": { "title": "Minimum rt_score", "minimum": 0, "type": "number", "description": "Minimum FreshContext decay-adjusted score to include.", "default": 0 }, "source": { "title": "Source", "enum": [ "all", "remotive", "remoteok" ], "type": "string", "description": "Optional upstream job source filter.", "default": "all" }, "remote": { "title": "Remote only", "type": "boolean", "description": "When true, request only jobs marked remote by the live API.", "default": false }, "q": { "title": "Keyword", "type": "string", "description": "Optional keyword search, for example llm, python, rag, or mlops." }, "includeExplain": { "title": "Include explanations", "type": "boolean", "description": "Fetch /v1/jobs/explain/:id for each returned job and attach scoring details.", "default": false } } }, "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 } } } } } } } } } } ```