# Job Posting Age Anomaly Detector (`trovevault/job-posting-age-anomaly-detector`) Actor Find job postings that have stayed open unusually long in a city and function area. Returns posting age, anomaly flag, hard-to-fill signal, salary when available, and job URL. - **URL**: https://apify.com/trovevault/job-posting-age-anomaly-detector.md - **Developed by:** [Trove Vault](https://apify.com/trovevault) (community) - **Categories:** Jobs, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $1.70 / 1,000 job openings This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Job Posting Age Anomaly Detector Find roles that have stayed open unusually long in a local hiring market. Give the actor a city, a function area, and optional keywords. It returns a compact job table with posting age, a hard-to-fill signal, and the evidence needed to review the role quickly. This actor is built for labor-market research, recruiting intelligence, sales prospecting, and weekly monitoring of persistent hiring demand. It does not require any third-party API key. ### What it does Job Posting Age Anomaly Detector scans job postings for a selected market and returns: - Job title, company, location, and URL - Posting date and posting age in days - Whether the posting exceeds your age threshold - A simple hard-to-fill signal - A plain-English reason for the signal - Salary range when available - A short description snippet for quick review The main signal is intentionally transparent: when a posting remains visible beyond the configured threshold, usually 60 days, it is flagged as an age anomaly. ### Input Typical input: ```json { "city": "Prague", "functionArea": "Software Engineering", "keywords": ["backend engineer"], "ageThresholdDays": 60, "maxResults": 50 } ```` Required fields: - `city`: target city, for example Prague, Munich, Lisbon, London, or Toronto - `functionArea`: job family, for example Software Engineering, Sales, Marketing, Customer Success, Finance, or Operations Optional fields: - `keywords`: job titles, skills, or phrases to narrow the scan. Keywords are matched against both job titles and descriptions. - `ageThresholdDays`: number of days after which a posting is treated as unusually old. Default is 60. - `maxResults`: maximum rows returned after filtering and deduplication. Default is 50. - `datasetId`: existing Apify dataset to append results to. - `runId`: external run identifier for workflow chaining. The actor infers country context for common city names, so users do not need to provide a country for normal runs. ### Output Each dataset item is one job posting. Operational diagnostics are written to the run summary and logs, not mixed into the result table. Example output: ```json { "title": "Senior Backend Engineer", "company": "Example GmbH", "location": "Berlin, Germany", "city": "Berlin", "functionArea": "Software Engineering", "postedAt": "2026-01-18T00:00:00.000Z", "postingAgeDays": 102, "ageThresholdDays": 60, "exceedsAgeThreshold": true, "hardToFillSignal": "high", "signalReason": "Open for 102 days, above the 60-day threshold.", "salaryRange": "EUR 90000-120000", "jobUrl": "https://example.com/job", "descriptionSnippet": "Short excerpt from the posting...", "scrapedAt": "2026-05-03T10:00:00.000Z" } ``` ### How to read the signal `exceedsAgeThreshold` is the clearest field. It tells you whether the posting has been open longer than your threshold. `hardToFillSignal` gives a simple label: - `high`: old posting with strong age evidence - `medium`: old posting with moderate age evidence - `low`: recent posting or weak age evidence - `unknown`: posting age was unavailable `signalReason` explains the label in plain English so the row can be used directly in reviews, spreadsheets, and downstream workflows. ### Data quality Job posting dates are useful but imperfect. Some companies refresh postings, keep evergreen roles live, or reuse job URLs. Treat the output as a shortlist for review, not as proof that every old posting is actively hard to fill. Salary availability depends on the job posting. When compensation is not published, `salaryRange` is empty. ### Use cases - Find hard-to-fill roles in a target city - Monitor senior hiring pressure by function area - Build recruiter or vendor lead lists from persistent hiring signals - Compare local hiring demand across markets - Identify stale or evergreen job listings - Feed clean job-age signals into a wider TroveVault workflow ### API use Run the actor manually, schedule it, or call it through the Apify API. ```bash curl -X POST "https://api.apify.com/v2/acts/TroveVault~job-posting-age-anomaly-detector/runs" \ -H "Authorization: Bearer YOUR_APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{"city":"Prague","functionArea":"Software Engineering","keywords":["backend engineer"],"maxResults":50}' ``` The actor writes a `RUN_SUMMARY` key-value-store record with row counts, anomaly count, matching mode, and run status. # Actor input Schema ## `city` (type: `string`): City to scan for job postings. The actor returns postings that match this local market. Example: Munich, Berlin, London, Lisbon. Use the city name a job seeker would type into a job board. ## `functionArea` (type: `string`): Job family to scan. The actor expands this into practical search terms and labels every output row with the selected area. Example: Customer Success, Software Engineering, Sales, Marketing. Pick the closest business function. ## `keywords` (type: `array`): Optional job titles, skills, or phrases to make the scan more precise. Keywords are matched against job titles and descriptions, then combined with the selected function area. Example: backend engineer, client success, growth manager. Leave empty for a broader function-area scan. ## `ageThresholdDays` (type: `integer`): Number of days after which a job posting is flagged as unusually old. Example: 60 means postings open for 60 days or more are marked as age anomalies. Lower values catch more roles but add noise. ## `maxResults` (type: `integer`): Maximum rows to return after filtering and deduplication. Example: 50 for a quick scan, 200 for a broader market check. Higher values take longer. ## `datasetId` (type: `string`): ID of an existing Apify dataset to append results to, in addition to the default run dataset. Leave blank to use only the default run dataset. ## `runId` (type: `string`): ID of an existing Apify actor run to associate results with. Used for pipeline chaining and multi-step workflows. Leave blank for standalone runs. ## Actor input object example ```json { "city": "Munich", "functionArea": "Software Engineering", "ageThresholdDays": 60, "maxResults": 50 } ``` # Actor output Schema ## `dataset` (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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("trovevault/job-posting-age-anomaly-detector").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("trovevault/job-posting-age-anomaly-detector").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 trovevault/job-posting-age-anomaly-detector --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=trovevault/job-posting-age-anomaly-detector", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Job Posting Age Anomaly Detector", "description": "Find job postings that have stayed open unusually long in a city and function area. Returns posting age, anomaly flag, hard-to-fill signal, salary when available, and job URL.", "version": "0.1", "x-build-id": "N4S6Wmb3MoKWan0zg" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/trovevault~job-posting-age-anomaly-detector/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-trovevault-job-posting-age-anomaly-detector", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/trovevault~job-posting-age-anomaly-detector/runs": { "post": { "operationId": "runs-sync-trovevault-job-posting-age-anomaly-detector", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/trovevault~job-posting-age-anomaly-detector/run-sync": { "post": { "operationId": "run-sync-trovevault-job-posting-age-anomaly-detector", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "city", "functionArea" ], "properties": { "city": { "title": "City", "type": "string", "description": "City to scan for job postings. The actor returns postings that match this local market. Example: Munich, Berlin, London, Lisbon. Use the city name a job seeker would type into a job board.", "default": "Munich" }, "functionArea": { "title": "Function Area", "enum": [ "Software Engineering", "Data & Analytics", "Product Management", "Design", "Sales", "Marketing", "Customer Success", "Operations", "Finance", "Human Resources", "Legal", "Healthcare", "Education", "Logistics", "Manufacturing", "Other" ], "type": "string", "description": "Job family to scan. The actor expands this into practical search terms and labels every output row with the selected area. Example: Customer Success, Software Engineering, Sales, Marketing. Pick the closest business function.", "default": "Software Engineering" }, "keywords": { "title": "Keywords (optional)", "type": "array", "description": "Optional job titles, skills, or phrases to make the scan more precise. Keywords are matched against job titles and descriptions, then combined with the selected function area. Example: backend engineer, client success, growth manager. Leave empty for a broader function-area scan.", "items": { "type": "string" } }, "ageThresholdDays": { "title": "Age Threshold Days", "minimum": 1, "maximum": 365, "type": "integer", "description": "Number of days after which a job posting is flagged as unusually old. Example: 60 means postings open for 60 days or more are marked as age anomalies. Lower values catch more roles but add noise.", "default": 60 }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum rows to return after filtering and deduplication. Example: 50 for a quick scan, 200 for a broader market check. Higher values take longer.", "default": 50 }, "datasetId": { "title": "Dataset ID (optional)", "type": "string", "description": "ID of an existing Apify dataset to append results to, in addition to the default run dataset. Leave blank to use only the default run dataset." }, "runId": { "title": "Run ID (optional)", "type": "string", "description": "ID of an existing Apify actor run to associate results with. Used for pipeline chaining and multi-step workflows. Leave blank for standalone runs." } } }, "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 } } } } } } } } } } ```