# Interview Intel (`stepskop/interview-intel`) Actor - **URL**: https://apify.com/stepskop/interview-intel.md - **Developed by:** [Štěpán Škopek](https://apify.com/stepskop) (community) - **Categories:** Automation, Jobs, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 ## Interview Intel — AI Podcast Briefing for Job Interviews Paste a LinkedIn job URL. Get a two-host podcast briefing in under 2 minutes. **Stack:** Apify (scraping + orchestration) → Claude (script writing) → ElevenLabs (audio) ### Quick Start 1. Deploy this Actor to Apify (push via CLI or GitHub integration) 2. Set environment variables in the Apify Console: - `ANTHROPIC_API_KEY` — your Claude API key - `ELEVENLABS_API_KEY` — your ElevenLabs API key 3. Run the Actor with input: ```json { "linkedinJobUrl": "https://www.linkedin.com/jobs/view/1234567890" } ```` 4. Results include `audioUrl` (direct link to the podcast MP3), `keyFacts`, and `transcript`. ### Before First Run **Verify scraper actor slugs!** The scrapers used are community Actors from the Apify Store. Open `src/scrapers.js` and verify each `ACTORS.*` slug exists and works by running them manually in the Apify Console. **Verify ElevenLabs voice IDs!** Open `src/elevenlabs.js` and replace the `VOICES.*` IDs with voices from your ElevenLabs account. ### Calling from a Frontend Use the synchronous run endpoint (blocks until done, returns results): ``` POST https://api.apify.com/v2/acts/YOUR_USERNAME~interview-intel/run-sync-get-dataset-items?token=YOUR_TOKEN Content-Type: application/json { "linkedinJobUrl": "https://www.linkedin.com/jobs/view/..." } ``` Response is a JSON array — take the first item for `audioUrl`, `keyFacts`, and `transcript`. # Actor input Schema ## `linkedinJobUrl` (type: `string`): Full URL of the LinkedIn job posting (e.g. https://www.linkedin.com/jobs/view/1234567890) ## `linkedinCookie` (type: `array`): LinkedIn cookies for scraping company posts. Export using Cookie-Editor extension. If not provided, LinkedIn post scraping is skipped. ## `linkedinUserAgent` (type: `string`): Your browser's User Agent string. Get it from https://www.google.com/search?q=my+user+agent ## `podcastStyle` (type: `string`): The tone and focus of the generated podcast. ## Actor input object example ```json { "linkedinJobUrl": "https://www.linkedin.com/jobs/view/1234567890", "podcastStyle": "balanced" } ``` # 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 = { "linkedinJobUrl": "https://www.linkedin.com/jobs/view/1234567890" }; // Run the Actor and wait for it to finish const run = await client.actor("stepskop/interview-intel").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/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "linkedinJobUrl": "https://www.linkedin.com/jobs/view/1234567890" } # Run the Actor and wait for it to finish run = client.actor("stepskop/interview-intel").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "linkedinJobUrl": "https://www.linkedin.com/jobs/view/1234567890" }' | apify call stepskop/interview-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=stepskop/interview-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Interview Intel", "version": "0.1", "x-build-id": "aWCdnK9nxjhdmrUfp" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/stepskop~interview-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-stepskop-interview-intel", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/stepskop~interview-intel/runs": { "post": { "operationId": "runs-sync-stepskop-interview-intel", "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/stepskop~interview-intel/run-sync": { "post": { "operationId": "run-sync-stepskop-interview-intel", "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", "required": [ "linkedinJobUrl" ], "properties": { "linkedinJobUrl": { "title": "LinkedIn Job URL", "type": "string", "description": "Full URL of the LinkedIn job posting (e.g. https://www.linkedin.com/jobs/view/1234567890)" }, "linkedinCookie": { "title": "LinkedIn Cookies (optional)", "type": "array", "description": "LinkedIn cookies for scraping company posts. Export using Cookie-Editor extension. If not provided, LinkedIn post scraping is skipped." }, "linkedinUserAgent": { "title": "LinkedIn User Agent (optional)", "type": "string", "description": "Your browser's User Agent string. Get it from https://www.google.com/search?q=my+user+agent" }, "podcastStyle": { "title": "Podcast style", "enum": [ "balanced", "technical", "culture-focused" ], "type": "string", "description": "The tone and focus of the generated podcast.", "default": "balanced" } } }, "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 } } } } } } } } } } ```