# Ashby & Greenhouse Job Changes | BoardWatch (`njlcazl/boardwatch-job-changes`) Actor

Monitor Ashby and Greenhouse job boards for new jobs, field changes and confirmed listing removals. Persistent baselines, structured change events and success-only board-check pricing.

- **URL**: https://apify.com/njlcazl/boardwatch-job-changes.md
- **Developed by:** [Evan Zeng](https://apify.com/njlcazl) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$2.00 / 1,000 successful board checks

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/actors/running/actors-in-store.md#pay-per-event

## 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

## BoardWatch — Job Board Changes

Checks known public Ashby and Greenhouse job boards and compares them with the previous successful observation. Monitor a watchlist of employers without comparing full job feeds by hand. Designed for niche job boards and recruitment-data workflows.

### Input

```json
{"boards": [{"provider": "ashby", "board": "linear"}, {"provider": "greenhouse", "board": "figma"}]}
```

Use 1–25 board entries. The board name comes from the public careers URL. The Actor uses fixed public API endpoints; arbitrary URLs and private applicant data are not supported.

### Results

The first successful check establishes a baseline without claiming existing jobs are new. Later checks report `added`, `changed`, and `no_longer_listed`. A job must be missing from two successful complete checks before it is reported as no longer listed. This describes listing visibility, not whether a position has been filled.

The default dataset contains `board_check` status rows and `job_event` rows. The default key-value store contains the full `OUTPUT` result. Job descriptions are compared by fingerprints; full descriptions are not republished. Fields include title, location, department, employment type and workplace where available.

### Persistent state

On Apify, the same Actor and same board set in your account reuse a named key-value store and request queue. Reordering the list preserves the baseline; changing its membership starts a different baseline. Stores belong to your account and consume its storage allowance. Deleting a state store resets that baseline. Different tasks with identical board sets intentionally share state.

Overlapping checks of the same watchlist are skipped using a queue lock. An interrupted run can delay the next check for up to ten minutes. Failed boards retain their last successful snapshot. Delivery is at-least-once: consumers should deduplicate job events by `event_id`. Checks have a bounded execution window, so larger or slower watchlists may produce partial results.

Pricing is $2 per 1,000 successful board checks ($0.002 each), including the first baseline and successful checks with no changes. All job change events from that check are included. Failed checks and concurrent-run skips have no service charge. Platform usage is included in the event price for customers. The run stops checking boards when its spending limit cannot cover another check.

Set a run budget that covers the boards you want to check: two boards cost $0.004 if both succeed; 25 boards cost $0.05 if all succeed. No automatic schedule is created. Scheduling and notifications are configured separately in your own workflow. Public API availability and payload changes can interrupt coverage.

### Quick start

1. Add the public board names you want to monitor and run the Actor to establish a baseline.
2. Run it again with the same board list to compare with the previous successful observation.
3. Open **Checks and job changes** for dataset rows, or **Run summary** for the full result. Export the dataset as JSON or CSV for your workflow.

A successful check with no changes still produces a board-check status row. An empty events list does not mean the check failed. Look at each board's status and the run summary for errors or skipped checks.

Independent tool; not affiliated with or endorsed by Ashby or Greenhouse.

### Billing reliability

Results are saved before a successful check is charged. API errors, invalid responses, insufficient budget, and concurrent skips do not trigger a board-check event. A stable per-run, per-board idempotency key prevents duplicate service charges if the same run is restarted. For a new observation, start a new run.

# Actor input Schema

## `boards` (type: `array`):

1–25 entries, each containing provider (ashby or greenhouse) and board (the board name in the public careers URL).

## Actor input object example

```json
{
  "boards": [
    {
      "provider": "ashby",
      "board": "linear"
    },
    {
      "provider": "greenhouse",
      "board": "figma"
    }
  ]
}
```

# Actor output Schema

## `checks_and_changes` (type: `string`):

Board check status rows and job change events. First successful checks establish baselines.

## `summary` (type: `string`):

Full result including skipped checks and spending-limit status.

# 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 = {
    "boards": [
        {
            "provider": "ashby",
            "board": "linear"
        },
        {
            "provider": "greenhouse",
            "board": "figma"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("njlcazl/boardwatch-job-changes").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 = { "boards": [
        {
            "provider": "ashby",
            "board": "linear",
        },
        {
            "provider": "greenhouse",
            "board": "figma",
        },
    ] }

# Run the Actor and wait for it to finish
run = client.actor("njlcazl/boardwatch-job-changes").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 '{
  "boards": [
    {
      "provider": "ashby",
      "board": "linear"
    },
    {
      "provider": "greenhouse",
      "board": "figma"
    }
  ]
}' |
apify call njlcazl/boardwatch-job-changes --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,njlcazl/boardwatch-job-changes"
        }
    }
}
```

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/CAGEIp6sXckx4RKwR/builds/drqxGf56yt6gPFC8j/openapi.json