# Linkedin Lead Qualification Research (`arakyet/linkedin-lead-qualification-research`) Actor Arakyet Lead Qualification API helps revenue and hiring teams qualify LinkedIn profiles in a single call. Send one linkedin profile URL with your criteria (industry, job changes, tech stack, role fit, skills, education, funding signals, and posting behavior), and get qualified/not qualified. - **URL**: https://apify.com/arakyet/linkedin-lead-qualification-research.md - **Developed by:** [ArakYet .com](https://apify.com/arakyet) (community) - **Categories:** Automation, Agents, AI - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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 ## LinkedIn Lead Qualification Research Qualify a single LinkedIn lead through your API and store the result in Apify. This Actor is built for teams that want to evaluate one LinkedIn profile at a time using structured filters such as industry, recent job changes, tech stack, job titles, skills, education, posting behavior, and company funding context. ### What This Actor Does The Actor takes a LinkedIn URL plus optional qualification filters, sends them to your `/qualify-single` endpoint, and saves the result to: - The default dataset - The `OUTPUT` record in the default key-value store This makes the Actor easy to use from the Apify Console, schedules, integrations, or the Apify API. ### How It Works When a run starts, the Actor: 1. Reads the input from Apify. 2. Builds a form-data request for your qualification API. 3. Sends a `POST` request to `apiBaseUrl/qualify-single`. 4. Parses the API response. 5. Stores the first item from `enriched_data` when present. 6. Adds top-level fields such as `status`, `message`, `error`, and `skipped_ai_processing` when they are returned by the API. If the request fails or required fields are missing, the Actor stores a structured error result instead. ### Required Input The following fields are required: - `apiBaseUrl` - `linkedinUrl` #### `apiBaseUrl` Base URL for your lead qualification API. Default: ```text https://qualify-leads-932925834372.europe-west1.run.app ```` #### `linkedinUrl` A single LinkedIn profile URL to qualify. Example: ```text https://www.linkedin.com/in/example-person/ ``` ### Optional Filters You can refine qualification by filling any of the following fields. #### Industry and company filters - `industryType`: Industry to qualify against - `workingInFundedCompany`: Whether the person should work at a funded company - `followerCount`: Minimum follower count - `techStack`: One technology selected from the predefined list #### Career change filters - `recentJobChangeValue`: Numeric job-change window - `recentJobChangeUnit`: `day`, `week`, `month`, or `year` These two values are combined and sent to the API as a single `changed_job_time` object. #### Role and expertise filters - `jobTitlesIncluded`: Comma-separated titles to include - `jobTitlesExcluded`: Comma-separated titles to exclude - `skills`: Comma-separated skills - `education`: Education filter value Example: ```text jobTitlesIncluded = Founder, CEO, Head of Growth jobTitlesExcluded = Intern, Student skills = B2B SaaS, outbound, GTM education = Engineering ``` #### Content and activity filters - `postingFrequency`: Posting frequency filter - `postedAbout`: Topic the lead has posted about - `commentedAbout`: Topic the lead has commented about Example: ```text postingFrequency = weekly postedAbout = sales automation commentedAbout = lead generation ``` ### Example Input ```json { "apiBaseUrl": "https://qualify-leads-932925834372.europe-west1.run.app", "linkedinUrl": "https://www.linkedin.com/in/example-person/", "industryType": "SaaS", "recentJobChangeValue": 6, "recentJobChangeUnit": "month", "techStack": "HubSpot", "followerCount": 1000, "jobTitlesIncluded": "Founder, CEO, Head of Growth", "jobTitlesExcluded": "Intern, Student", "skills": "B2B SaaS, outbound, GTM", "education": "Engineering", "workingInFundedCompany": true, "postingFrequency": "weekly", "postedAbout": "sales automation", "commentedAbout": "lead generation" } ``` ### Output The Actor pushes one result item to the default dataset. In successful runs, the stored item is usually the first object from the API response's `enriched_data` array. If the API also returns fields such as `status`, `message`, `error`, or `skipped_ai_processing`, those are copied into the saved result. Because the response body comes from your API, the exact output structure depends on what `/qualify-single` returns. #### Typical output behavior - If `enriched_data` exists, the first item is stored. - If the API returns status metadata, it is added to the stored item. - If the API returns an invalid response or the request fails, an error object is stored. ### Error Handling The Actor saves a structured error result for cases such as: - Missing `apiBaseUrl` - Missing `linkedinUrl` - Network or timeout failures - Invalid or unexpected API response bodies Example: ```json { "linkedin_url": "https://www.linkedin.com/in/example-person/", "status": "error", "error": "API request failed:
" } ``` ### How To Use In Apify #### In the Apify Console 1. Open the Actor. 2. Click **Start**. 3. Fill in `apiBaseUrl` and `linkedinUrl`. 4. Add any optional filters you want. 5. Run the Actor. 6. Open the dataset to inspect the result. #### Via Apify API Run the Actor with the same JSON input you would use in the Console, then read the dataset items or the `OUTPUT` record after the run finishes. ### Best Practices - Start with only `linkedinUrl` to validate the API connection. - Add filters gradually so you can see which criteria most affect qualification. - Keep `apiBaseUrl` pointed at the right environment, such as staging or production. - Review several dataset outputs to confirm your API response format matches expectations. ### Notes - This Actor qualifies one LinkedIn URL per run. - Requests are sent as form-data, not JSON. - `techStack` is a single-select field in the input UI. - Fields like job titles and skills are passed as comma-separated strings. ### Summary Use this Actor when you want an Apify wrapper around your LinkedIn qualification API: - Input one LinkedIn URL - Add structured qualification filters - Call `/qualify-single` - Save the result for downstream automation # Actor input Schema ## `apiBaseUrl` (type: `string`): Base URL for qualify leads API. ## `linkedinUrl` (type: `string`): Single LinkedIn profile URL to qualify. ## `industryType` (type: `string`): Filter by industry type. ## `recentJobChangeValue` (type: `integer`): Numeric value for recent job change window. ## `recentJobChangeUnit` (type: `string`): Unit for recent job change window. ## `techStack` (type: `string`): Select one technology from the predefined list. ## `followerCount` (type: `integer`): Minimum follower count. ## `jobTitlesIncluded` (type: `string`): Comma separated values. ## `jobTitlesExcluded` (type: `string`): Comma separated values. ## `skills` (type: `string`): Comma separated values. ## `education` (type: `string`): Education filter value. ## `workingInFundedCompany` (type: `boolean`): Whether the lead should be in a funded company. ## `postingFrequency` (type: `string`): Posting frequency filter. ## `postedAbout` (type: `string`): Topic the lead has posted about. ## `commentedAbout` (type: `string`): Topic the lead has commented about. ## Actor input object example ```json { "apiBaseUrl": "https://qualify-leads-932925834372.europe-west1.run.app", "workingInFundedCompany": false } ``` # Actor output Schema ## `datasetUrl` (type: `string`): No description ## `datasetItemsUrl` (type: `string`): No description ## `outputUrl` (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("arakyet/linkedin-lead-qualification-research").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("arakyet/linkedin-lead-qualification-research").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 arakyet/linkedin-lead-qualification-research --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=arakyet/linkedin-lead-qualification-research", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Linkedin Lead Qualification Research", "description": "Arakyet Lead Qualification API helps revenue and hiring teams qualify LinkedIn profiles in a single call. Send one linkedin profile URL with your criteria (industry, job changes, tech stack, role fit, skills, education, funding signals, and posting behavior), and get qualified/not qualified.", "version": "0.0", "x-build-id": "PgidQ63NrCt9Zemfj" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/arakyet~linkedin-lead-qualification-research/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-arakyet-linkedin-lead-qualification-research", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/arakyet~linkedin-lead-qualification-research/runs": { "post": { "operationId": "runs-sync-arakyet-linkedin-lead-qualification-research", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/arakyet~linkedin-lead-qualification-research/run-sync": { "post": { "operationId": "run-sync-arakyet-linkedin-lead-qualification-research", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "apiBaseUrl", "linkedinUrl" ], "properties": { "apiBaseUrl": { "title": "API Base URL", "type": "string", "description": "Base URL for qualify leads API.", "default": "https://qualify-leads-932925834372.europe-west1.run.app" }, "linkedinUrl": { "title": "LinkedIn URL", "type": "string", "description": "Single LinkedIn profile URL to qualify." }, "industryType": { "title": "Industry type", "type": "string", "description": "Filter by industry type." }, "recentJobChangeValue": { "title": "Recent Job Change time (value)", "minimum": 0, "type": "integer", "description": "Numeric value for recent job change window." }, "recentJobChangeUnit": { "title": "Recent Job Change time (unit)", "enum": [ "day", "week", "month", "year" ], "type": "string", "description": "Unit for recent job change window." }, "techStack": { "title": "Tech stack", "enum": [ "Google Tag Manager", "Facebook Pixel", "LinkedIn Insight Tag", "Twitter Pixel", "TikTok Pixel", "Pinterest Tag", "Reddit Pixel", "Bing Ads", "HubSpot", "Marketo", "Pardot", "ActiveCampaign", "Klaviyo", "Mailchimp", "Brevo (Sendinblue)", "Customer.io", "Intercom", "Drift", "Zendesk", "Crisp", "Tawk.to", "Freshchat", "Tidio", "LiveChat", "Shopify", "WooCommerce", "Stripe", "Paddle", "Chargebee", "Recurly", "Recharge", "WordPress", "Webflow", "Framer", "Elementor", "Squarespace", "Wix", "Ghost", "Contentful", "Sanity", "Drupal", "Joomla", "React", "Next.js", "Vue.js", "Angular", "Nuxt.js", "Gatsby", "Svelte", "Remix", "Google Cloud", "AWS", "Microsoft Azure", "Vercel", "Netlify", "Heroku", "DigitalOcean", "Cloudflare", "Firebase", "Vultr", "Linode", "Optimizely", "VWO", "Google Optimize", "Convert.com", "Apollo", "Clearbit", "Qualified", "Chili Piper", "Calendly", "Google Analytics", "Mixpanel", "Amplitude", "Segment", "Hotjar", "Heap", "FullStory", "Matomo", "SendGrid", "Mailgun", "Postmark", "Amazon SES", "Resend", "Twilio" ], "type": "string", "description": "Select one technology from the predefined list." }, "followerCount": { "title": "Follower count", "minimum": 0, "type": "integer", "description": "Minimum follower count." }, "jobTitlesIncluded": { "title": "Job titles (included)", "type": "string", "description": "Comma separated values." }, "jobTitlesExcluded": { "title": "Job titles (excluded)", "type": "string", "description": "Comma separated values." }, "skills": { "title": "Skills", "type": "string", "description": "Comma separated values." }, "education": { "title": "Education", "type": "string", "description": "Education filter value." }, "workingInFundedCompany": { "title": "Working in a funded company", "type": "boolean", "description": "Whether the lead should be in a funded company.", "default": false }, "postingFrequency": { "title": "Posting frequency", "type": "string", "description": "Posting frequency filter." }, "postedAbout": { "title": "Posted about", "type": "string", "description": "Topic the lead has posted about." }, "commentedAbout": { "title": "Commented about", "type": "string", "description": "Topic the lead has commented about." } } }, "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 } } } } } } } } } } ```