# Layoff-to-Hiring Reversal Intelligence (`quanmatrix/layoff-hiring-reversal-intelligence`) Actor

Use this Actor to analyze layoff-to-hiring reversal and return decision-ready structured signals. Combine layoff and hiring datasets to detect company workforce reversals, recovery and renewed contraction.

- **URL**: https://apify.com/quanmatrix/layoff-hiring-reversal-intelligence.md
- **Developed by:** [Rafael Barreto Haddad](https://apify.com/quanmatrix) (community)
- **Categories:** Jobs, Business, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $10.50 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## Layoff-to-Hiring Reversal Intelligence

Use this Actor to analyze layoff-to-hiring reversal and return decision-ready structured signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.

Combine layoff and hiring datasets to detect company workforce reversals, recovery and renewed contraction.

### Why use this Actor

Layoff notices and job postings live in separate feeds, obscuring the moment a company moves from contraction back to expansion or relapses. This Actor sits above raw extraction: supply a current dataset, optionally add a previous snapshot, and receive an aggregated report built for recurring monitoring and AI-agent workflows.

### Key features

- Cross-signal layoff-to-hiring reversal detection.

- Company recovery versus renewed contraction scoring.

- Combines WARN-style and job-market snapshots.

- Deterministic workforce-cycle actions.

- Reads inline JSON rows or Apify Dataset IDs with limited READ permission.

- Writes one auditable report to the default Dataset and `INTELLIGENCE_REPORT`.

### Example

Use the prefilled example or replace `currentItems` with rows from an upstream Actor. On recurring runs, provide the prior period in `previousItems` or `previousDatasetId`. The Actor normalizes common aliases, compares snapshots, ranks the strongest entity changes and emits `agentAction`.

### Use cases

- credit and equity research.
- recruiting strategy.
- economic intelligence.

### Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.015 before Apify tier discounts. The 256 MB data-first architecture is designed for strong unit economics.

### Limitations

- Analyzes supplied public or appropriately licensed data and does not bypass restricted sources.
- Scores are decision-support signals, not predictions or guarantees.
- Keep stable identifiers across snapshots for best change detection.
- Zero-direct-competition status is rechecked before publication because the Store changes continuously.

### Workflow

`upstream dataset -> current snapshot -> optional previous snapshot -> normalization -> entity aggregation -> change scoring -> ranked signals -> agentAction`.

### Input

Provide `currentItems` as normalized JSON rows or select a cloud Dataset with `currentDatasetId`. For change analysis, add `previousItems` or `previousDatasetId`. The Actor reads Datasets with limited READ permission. Keep identifiers stable across periods and normalize source fields where possible. The prefilled example is intentionally small so the first run can be validated before scaling.

### Output

The default Dataset receives one decision-ready intelligence report with record counts, snapshot additions and removals, ranked entity signals, secondary-dimension breadth, a bounded signal score, and `agentAction`. The same report is stored as `INTELLIGENCE_REPORT` for downstream automation. Results are designed for Tasks, schedules, webhooks, dashboards, and agent workflows rather than as a replacement for source evidence.

#### Evolution round 2

Expanded source-field normalization after fresh market research so the Actor can consume a broader range of upstream datasets without pretending adjacent products are direct competitors.

# Changelog

This Actor's version history is a separate document: https://apify.com/quanmatrix/layoff-hiring-reversal-intelligence/changelog.md

# Actor input Schema

## `currentItems` (type: `array`):

Current source or normalized rows to analyze.

## `currentDatasetId` (type: `string`):

Optional Apify Dataset ID used when currentItems is not supplied.

## `previousItems` (type: `array`):

Optional previous snapshot rows for period-over-period comparison.

## `previousDatasetId` (type: `string`):

Optional previous Apify Dataset ID used instead of previousItems.

## `maxItems` (type: `integer`):

Maximum records loaded from a Dataset input.

## `mcpConnectors` (type: `array`):

Optional MCP connectors authorized in your Apify account. Use them to send or write this Actor result to tools such as Slack, Notion, GitHub, Sentry, Supabase, or another compatible MCP service.

## `mcpToolName` (type: `string`):

Optional exact MCP tool name. Leave blank to let the selected MCP action preset discover a compatible tool automatically.

## `mcpToolArguments` (type: `object`):

JSON object passed to the selected MCP tool. String values may use {{actor\_title}}, {{result\_summary}}, or {{result\_json}} placeholders.

## `mcpFailOnError` (type: `boolean`):

When enabled, an MCP delivery error fails the Actor run. Disabled by default so data extraction and intelligence results remain available even if the external destination is unavailable.

## `mcpActionPreset` (type: `string`):

Choose a safe action pattern. AUTO\_SAFE\_WRITE discovers a compatible non-destructive write tool automatically; use a specific preset for Slack, GitHub, Notion, or database delivery.

## Actor input object example

```json
{
  "currentItems": [
    {
      "id": "h1",
      "company": "Acme",
      "eventType": "hiring",
      "role": "Engineer",
      "openings": 45,
      "netHiringSignal": 45
    },
    {
      "id": "h2",
      "company": "Acme",
      "eventType": "hiring",
      "role": "Sales",
      "openings": 20,
      "netHiringSignal": 20
    }
  ],
  "previousItems": [
    {
      "id": "l1",
      "company": "Acme",
      "eventType": "layoff",
      "affectedWorkers": 300,
      "netHiringSignal": -300
    }
  ],
  "maxItems": 20000,
  "mcpToolName": "",
  "mcpToolArguments": {},
  "mcpFailOnError": false,
  "mcpActionPreset": "AUTO_SAFE_WRITE"
}
```

# Actor output Schema

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

Decision-ready intelligence report.

# 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 = {
    "currentItems": [
        {
            "id": "h1",
            "company": "Acme",
            "eventType": "hiring",
            "role": "Engineer",
            "openings": 45,
            "netHiringSignal": 45
        },
        {
            "id": "h2",
            "company": "Acme",
            "eventType": "hiring",
            "role": "Sales",
            "openings": 20,
            "netHiringSignal": 20
        }
    ],
    "previousItems": [
        {
            "id": "l1",
            "company": "Acme",
            "eventType": "layoff",
            "affectedWorkers": 300,
            "netHiringSignal": -300
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("quanmatrix/layoff-hiring-reversal-intelligence").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 = {
    "currentItems": [
        {
            "id": "h1",
            "company": "Acme",
            "eventType": "hiring",
            "role": "Engineer",
            "openings": 45,
            "netHiringSignal": 45,
        },
        {
            "id": "h2",
            "company": "Acme",
            "eventType": "hiring",
            "role": "Sales",
            "openings": 20,
            "netHiringSignal": 20,
        },
    ],
    "previousItems": [{
            "id": "l1",
            "company": "Acme",
            "eventType": "layoff",
            "affectedWorkers": 300,
            "netHiringSignal": -300,
        }],
}

# Run the Actor and wait for it to finish
run = client.actor("quanmatrix/layoff-hiring-reversal-intelligence").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 '{
  "currentItems": [
    {
      "id": "h1",
      "company": "Acme",
      "eventType": "hiring",
      "role": "Engineer",
      "openings": 45,
      "netHiringSignal": 45
    },
    {
      "id": "h2",
      "company": "Acme",
      "eventType": "hiring",
      "role": "Sales",
      "openings": 20,
      "netHiringSignal": 20
    }
  ],
  "previousItems": [
    {
      "id": "l1",
      "company": "Acme",
      "eventType": "layoff",
      "affectedWorkers": 300,
      "netHiringSignal": -300
    }
  ]
}' |
apify call quanmatrix/layoff-hiring-reversal-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,quanmatrix/layoff-hiring-reversal-intelligence"
        }
    }
}
```

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/Nv9k07HYPGjL9MwEi/builds/mUwwagvuB4pc6ww8z/openapi.json