# Podcast Guest Discovery from Social Signals (`kayhermes/podcast-guest-discovery-from-social-signals`) Actor

- **URL**: https://apify.com/kayhermes/podcast-guest-discovery-from-social-signals.md
- **Developed by:** [Khoa Nguyen](https://apify.com/kayhermes) (community)
- **Categories:** Social media
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $15.00 / 1,000 qualified guests

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

## Podcast Guest Discovery from Social Signals

Ranks podcast guest candidates using topic expertise, social momentum, story hooks, speaking evidence, audience fit, contact readiness, prior-guest cooldown, conflicts, and brand-safety inputs.

### Input

Provide `people[]`, an Apify `datasetId`, or public CSV/JSON `sourceUrls`. See `sample_input.json`.

### Output

The Actor writes structured Dataset records, an `OUTPUT` run summary, and `REPORT.html`.

### Important limitations

This Actor is decision support. Results depend on supplied data and should be reviewed by a human before outreach, contracting, advertising, employment, or reputational decisions.

# Actor input Schema

## `people` (type: `array`):

Ranks podcast guest candidates using topic expertise, social momentum, story hooks, speaking evidence, audience fit, contact readiness, prior-guest cooldown, conflicts, and brand-safety inputs.

## `datasetId` (type: `string`):

Configure source dataset id for this Actor.

## `sourceUrls` (type: `array`):

Configure csv or json source urls for this Actor.

## `brief` (type: `object`):

Configure podcast brief for this Actor.

## `options` (type: `object`):

Configure analysis options for this Actor.

## `stateStoreName` (type: `string`):

Configure optional named state store for this Actor.

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

Configure maximum records for this Actor.

## `webhookUrl` (type: `string`):

Configure alert or completion webhook for this Actor.

## Actor input object example

```json
{
  "people": [
    {
      "personId": "guest-1",
      "name": "Maya Patel",
      "title": "VP Customer Research",
      "company": "RetentionLab",
      "bio": "Customer research leader helping SaaS companies reduce churn and improve retention.",
      "topics": [
        "customer research",
        "retention",
        "SaaS"
      ],
      "followers": 18000,
      "expertiseScore": 0.95,
      "originalResearchCount": 7,
      "recentPosts": [
        {
          "publishedAt": "2026-07-10T12:00:00Z",
          "text": "Our new SaaS retention cohort study found that onboarding interviews predict churn better than NPS.",
          "views": 35000,
          "likes": 1400,
          "comments": 180,
          "shares": 260
        }
      ],
      "podcastAppearances": [
        "show-a",
        "show-b"
      ],
      "videos": 4,
      "speakingQualityScore": 0.9,
      "email": "maya@example.com",
      "acceptsPodcastInvites": true,
      "storyStrengthScore": 0.88,
      "newsHook": "A new cohort study challenges common churn assumptions.",
      "audienceInterests": [
        "SaaS",
        "product leadership"
      ],
      "audienceRegions": {
        "US": 65,
        "CA": 12,
        "GB": 8
      }
    },
    {
      "personId": "guest-2",
      "name": "Alex Star",
      "bio": "Lifestyle and fashion creator.",
      "topics": [
        "fashion",
        "travel"
      ],
      "followers": 2500000,
      "expertiseScore": 0.2,
      "recentPosts": [
        {
          "publishedAt": "2026-07-11T12:00:00Z",
          "text": "Summer travel outfits",
          "views": 900000,
          "likes": 45000
        }
      ],
      "podcastAppearances": [
        "pop-show"
      ],
      "speakingQualityScore": 0.55,
      "email": "alex@example.com"
    },
    {
      "personId": "guest-3",
      "name": "Jordan Lee",
      "bio": "SaaS retention consultant and former product leader.",
      "topics": [
        "retention",
        "product"
      ],
      "followers": 24000,
      "expertiseScore": 0.82,
      "recentPosts": [
        {
          "publishedAt": "2026-07-05T12:00:00Z",
          "text": "Three customer research mistakes that increase churn",
          "views": 12000,
          "likes": 500,
          "comments": 55,
          "shares": 70
        }
      ],
      "podcastAppearances": [
        "our-show"
      ],
      "lastGuestDate": "2026-06-20T12:00:00Z",
      "email": "jordan@example.com"
    }
  ],
  "sourceUrls": [],
  "brief": {
    "topics": [
      "retention",
      "customer research"
    ],
    "description": "Practical interviews for SaaS founders and product leaders about customer retention and research.",
    "targetAudience": "SaaS founders and product leaders",
    "targetRegions": [
      "US",
      "CA"
    ],
    "guestCooldownDays": 180
  },
  "options": {
    "asOfDate": "2026-07-14T00:00:00Z",
    "minimumScore": 45
  },
  "maxItems": 10000
}
```

# Actor output Schema

## `dataset` (type: `string`):

No description

## `output` (type: `string`):

No description

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

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("kayhermes/podcast-guest-discovery-from-social-signals").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("kayhermes/podcast-guest-discovery-from-social-signals").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 '{}' |
apify call kayhermes/podcast-guest-discovery-from-social-signals --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,kayhermes/podcast-guest-discovery-from-social-signals"
        }
    }
}
```

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/bgPsMwg9QS8jWCA7z/builds/CzIEMYpJe1Z9Dwta6/openapi.json