# LinkedIn Repost & Ghost Job Checker (`sharplinelabs/linkedin-repost-checker`) Actor

Paste LinkedIn job links and see which 'just posted' jobs are really old reposts. We date every LinkedIn job ID from an hourly index, so you see when the job was first created, how many days it has been open, and whether LinkedIn re-listed it as new. Free, no login.

- **URL**: https://apify.com/sharplinelabs/linkedin-repost-checker.md
- **Developed by:** [SharpLine Labs](https://apify.com/sharplinelabs) (community)
- **Categories:** Jobs, AI
- **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/actors/running/actors-in-store.md#pay-per-usage

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

### What is the LinkedIn Repost & Ghost Job Checker?

**Paste LinkedIn job links and find out which "just posted" jobs are really old listings that were re-posted.** LinkedIn shows a re-listed job as "posted 2 hours ago" even when the same opening has been up for weeks or months. This actor tells you when each job was **first created**, how many days it has been open, and whether LinkedIn is showing it as new.

Free to use (you only pay Apify's small platform usage, about $0.001 per run). No LinkedIn account, no cookies.

**Try it:** paste a few job links from your LinkedIn search into **LinkedIn job links** and press **Start**.

### Why check for reposts?

- **About half of the US jobs LinkedIn shows as new are reposts.** We measured this on our hourly index in late October 2026: of the jobs LinkedIn listed as posted in the last day, roughly 50-60% had a job ID that already existed days, weeks or months earlier.
- **A job that has been open for months** may be hard to fill, an evergreen "pipeline" posting, or a ghost job that nobody is actively hiring for. It's worth knowing before you spend an hour on a cover letter.
- **Applicant counts make more sense.** "Over 200 applicants" on a job "posted 3 hours ago" is a repost.

### How it works

LinkedIn gives every job a numeric ID, and new jobs get higher IDs. A re-posted job keeps its old ID while LinkedIn shows the new posting time. We crawl LinkedIn's public job search every hour and record the newest job ID we see, so we can date any job ID:

- **Since 2026-10-24:** to the hour, from our index.
- **March to October 2026:** to within a few weeks, from older reference points.
- **Before that:** reported as "before 2025-12-14".

A job is a **repost** when LinkedIn lists it as posted within the last 2 days but its ID is more than 2 days old.

### Output

```json
{
  "job_id": 4475887918,
  "url": "https://www.linkedin.com/jobs/view/4475887918",
  "title": "Senior Data Engineer",
  "company": "Example Corp",
  "linkedin_posted": "2 hours ago",
  "id_created_estimate": "2026-10-05",
  "id_age_days": 22.4,
  "date_basis": "anchors",
  "is_repost": true,
  "applicants": 200,
  "closed": false,
  "summary": "Repost: LinkedIn shows '2 hours ago', but this job was first created around 2026-10-05 (22 days ago)."
}
```

`date_basis` says how exact the date is: `index` (to the hour), `anchors` (a few weeks), `extrapolated` or `older` (rough).

### Want every new job, without the reposts?

**[LinkedIn Jobs Search Pro](https://apify.com/sharplinelabs/linkedin-jobs-search)** searches our hourly index with title, location, remote/hybrid and experience filters, **drops reposts and (optionally) recruitment agencies**, and gives every job an apply link, the employer's own where we find it. $1.25 per 1,000 jobs.

### FAQ

#### Is a repost always a ghost job?

No. Many reposts are real openings that are hard to fill, or roles companies keep open all year. A repost tells you the job is older than it looks; whether to apply is your call.

#### Why can't you date some jobs exactly?

Our hourly index started on 2026-10-24. Older IDs are dated from a few reference points, so those dates are estimates (shown in `date_basis`).

#### Do you need my LinkedIn login?

No. We only read LinkedIn's public, logged-out job pages.

#### Something looks wrong?

Open an issue on the **Issues** tab with the job link.

# Actor input Schema

## `jobUrls` (type: `array`):

One LinkedIn job link or job ID per line (…/jobs/view/4484229020, or a search URL with currentJobId=…). Up to 200 per run.

## `fetchDetails` (type: `boolean`):

Read each job page (logged out) for title, company, what LinkedIn shows as the posting time, applicants and whether it is closed. Needed for the repost verdict. Off = only the job ID's age.

## Actor input object example

```json
{
  "jobUrls": [
    "https://www.linkedin.com/jobs/view/4484229020"
  ],
  "fetchDetails": true
}
```

# Actor output Schema

## `results` (type: `string`):

Per job: verdict, is_repost, first-created date estimate, days open, what LinkedIn shows, title, company, applicants.

## `runSummary` (type: `string`):

Jobs checked, reposts found, inputs that were not LinkedIn job links.

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "jobUrls": [
        "https://www.linkedin.com/jobs/view/4484229020"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("sharplinelabs/linkedin-repost-checker").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "jobUrls": ["https://www.linkedin.com/jobs/view/4484229020"] }

# Run the Actor and wait for it to finish
run = client.actor("sharplinelabs/linkedin-repost-checker").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "jobUrls": [
    "https://www.linkedin.com/jobs/view/4484229020"
  ]
}' |
apify call sharplinelabs/linkedin-repost-checker --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,sharplinelabs/linkedin-repost-checker"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/skuNWUm1eErLGrbyb/builds/Rb4gLZ1Ls5ZYK7E7v/openapi.json