# 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, 50.0% 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/platform/actors/running/actors-in-store#pay-per-usage ## 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## 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 '' 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("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 '' with your token. client = ApifyClient("") # 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("💾 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 kayhermes/podcast-guest-discovery-from-social-signals --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=kayhermes/podcast-guest-discovery-from-social-signals", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Podcast Guest Discovery from Social Signals", "version": "2.0", "x-build-id": "CzIEMYpJe1Z9Dwta6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/kayhermes~podcast-guest-discovery-from-social-signals/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-kayhermes-podcast-guest-discovery-from-social-signals", "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/kayhermes~podcast-guest-discovery-from-social-signals/runs": { "post": { "operationId": "runs-sync-kayhermes-podcast-guest-discovery-from-social-signals", "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/kayhermes~podcast-guest-discovery-from-social-signals/run-sync": { "post": { "operationId": "run-sync-kayhermes-podcast-guest-discovery-from-social-signals", "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", "properties": { "people": { "title": "people", "type": "array", "description": "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.", "default": [ { "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" } ] }, "datasetId": { "title": "Source Dataset ID", "type": "string", "description": "Configure source dataset id for this Actor." }, "sourceUrls": { "title": "CSV or JSON source URLs", "type": "array", "description": "Configure csv or json source urls for this Actor.", "default": [], "items": { "type": "string" } }, "brief": { "title": "Podcast brief", "type": "object", "description": "Configure podcast brief for this Actor.", "default": { "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": { "title": "Analysis options", "type": "object", "description": "Configure analysis options for this Actor.", "default": { "asOfDate": "2026-07-14T00:00:00Z", "minimumScore": 45 } }, "stateStoreName": { "title": "Optional named state store", "type": "string", "description": "Configure optional named state store for this Actor." }, "maxItems": { "title": "Maximum records", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Configure maximum records for this Actor.", "default": 10000 }, "webhookUrl": { "title": "Alert or completion webhook", "type": "string", "description": "Configure alert or completion webhook for this Actor." } } }, "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 } } } } } } } } } } ```