# LinkedIn Post Engagers Email Finder (`data-slayer/linkedin-post-engagers-email-finder-no-cookies`) Actor Paste any LinkedIn post URL → get enriched profiles and verified work emails of every commenter, reactor, and reposter. Full pipeline: engagement extraction + profile enrichment + email finding + email verification in one step. No cookies, no login, no subscription. Pay per lead. - **URL**: https://apify.com/data-slayer/linkedin-post-engagers-email-finder-no-cookies.md - **Developed by:** [Data Slayer](https://apify.com/data-slayer) (community) - **Categories:** Lead generation, Social media - **Stats:** 9 total users, 7 monthly users, 60.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $15.00 / 1,000 enriched leads 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/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 ### 📬 What is LinkedIn Post to Verified Leads? **LinkedIn Post to Verified Leads** extracts every person who engaged with a LinkedIn post — commenters, reactors, reposters — and enriches each one with full LinkedIn profile data and optionally a verified work email. Paste a post URL. Choose who to extract. Get a structured lead list ready for outreach. No LinkedIn cookies. No login. No Chrome extension. No subscription. Pay only for results. ### ⚡ How it works 1. **Paste** any LinkedIn post URL 2. **Choose** commenters, reactors, reposters — or all three 3. **Toggle** email enrichment on or off 4. **Get** a structured dataset: full profile + engagement context + verified email Each lead includes their current job title, company, industry, headcount, location, LinkedIn URL, what they said or how they reacted, and — if email is enabled — a verified work email with deliverability status. ### 💰 Pricing Two-component pricing — you only pay for what you get: | Component | Price | When it's charged | | --- | --- | --- | | **Lead enriched** (base) | $20 / 1,000 leads | Every lead in the output | | **Verified email found** | $29 / 1,000 emails | Only when a valid/catch-all email is found | **Example:** A post with 1,000 engagers where 500 verified emails are found → $20 + $14.50 = **$34.50 total**. Emails disabled? You pay only the $20/1K base for full enriched profiles. No monthly subscription. No minimum. No wasted credits. ### 🔄 Why use this over Jungler, Trigify, or Clay? | SaaS tools | This Actor | | --- | --- | | $99–549/month subscription required | **Pay per lead, no subscription** | | Engagement data only — no emails included | **Verified work emails included** | | Need Clay ($149–800/mo) to enrich + find emails | **Full pipeline in one step** | | Require LinkedIn cookies or account connection | **No cookies, no login, zero account risk** | | Fixed monthly credits — use them or lose them | **Pay only when you run it** | **Replace a $500–1,500/month tool stack with a single actor run.** ### 📊 What you get per lead **Always included (base):** - Full name, first name, last name - LinkedIn profile URL, Sales Navigator URL - Current job title, company, industry, company size, company website - Headline, about/description - Location, country - Connections, followers - Profile flags (premium, creator, open to connect, job seeker) - Engagement type (commented / reacted / reposted) - Comment text + comment URL (if commenter) - Reaction type (if reactor) - Post URL, post author, post text **With email enrichment enabled:** - Verified work email address - Email verification status (Valid, Catch-All, Invalid, Unknown) - Email quality score **Full raw data (trailing columns):** - Complete experience history (all roles, not just current) - Education history - Skills list (up to 20) - Certifications, languages, volunteering, publications, projects, patents ### 🎯 Use cases - **Sales teams:** Get verified emails of people actively engaging with industry content - **Recruiters:** Find candidates commenting on relevant job posts - **ABM campaigns:** Build lead lists from competitor post engagement - **Event follow-up:** Extract attendees who commented on event recap posts - **Content creators:** Identify your most engaged audience with contact info ### 📥 Input | Field | Description | | --- | --- | | **LinkedIn post URL** | Any LinkedIn post URL (required) | | **Extract commenters** | Include people who commented | | **Extract reactors** | Include people who reacted (liked, celebrated, etc.) | | **Extract reposters** | Include people who reposted | | **Find verified work emails** | Discover and verify work emails (adds email fields + email pricing tier) | ### 📤 Output Results are saved to the default Apify dataset. Download as JSON, CSV, or Excel. **JSON output:** Clean top-level fields + nested arrays for experience, education, skills. **CSV output:** All top-level fields as clean columns. Experience/education/skills auto-flatten into trailing columns — your key fields are always in the first 30 columns. #### Sample output (JSON) ```json { "full_name": "Sarah Chen", "first_name": "Sarah", "last_name": "Chen", "email": "sarah.chen@acme.com", "millionverifier_result": "ok", "job_title": "VP of Sales", "current_company_name": "Acme Corp", "company_industry": "Software Development", "company_website": "https://www.acme.com", "location": "San Francisco, California", "profile_link": "https://www.linkedin.com/in/sarahchen", "engagement_type": "commented", "comment_text": "This resonates — we saw the same pattern at Acme.", "post_url": "https://www.linkedin.com/posts/...", "post_author": "Jane Smith", "experience": [...], "skills": [...] } ```` ### ❓ FAQ **Does this require my LinkedIn account or cookies?** No. This actor is completely cookieless. Your LinkedIn account is never touched, never exposed, and never at risk. Unlike PhantomBuster or browser extensions, no login credentials are needed. **How long does a run take?** Without emails: 2-5 minutes for most posts. With emails: 5-20 minutes depending on the number of engagers, since each email requires a search + verification step. **What LinkedIn post URL formats are supported?** - `https://www.linkedin.com/posts/username-activity-...` - `https://www.linkedin.com/posts/username-ugcPost-...` - `https://www.linkedin.com/feed/update/urn:li:activity:...` **What's the email hit rate?** Typically 40-70% of leads get a verified work email. People at larger companies with established domains have higher hit rates. Freelancers and people at very small companies often don't have findable work emails. **Can I process multiple posts in one run?** Currently one post per run. Multi-post support is coming soon. **What if I only want commenters, not reactors?** Toggle each engager type on or off in the input. You can extract any combination. ### 🔗 Related actors - [LinkedIn Profile Scraper](https://apify.com/data-slayer/linkedin-profile-scraper) — Scrape full profiles from profile URLs - [LinkedIn Company Scraper](https://apify.com/data-slayer/linkedin-company-scraper) — Scrape company pages - [LinkedIn Post Analytics Scraper](https://apify.com/data-slayer/linkedin-post-analytics-scraper) — Get post engagement analytics # Actor input Schema ## `linkedin_post_url` (type: `string`): One LinkedIn post URL in a supported format (e.g. linkedin.com/posts/… or feed/update/urn:li:activity:…). Bulk multiple URLs will be added later. ## `include_commenters` (type: `boolean`): Include people who commented on the post ## `include_reactors` (type: `boolean`): Include people who reacted (liked, celebrated, etc.) ## `include_reposters` (type: `boolean`): Include people who reposted the post ## `find_verified_work_emails` (type: `boolean`): Unchecked: full LinkedIn profile + engagement columns only. Checked: also discover work emails and verify with MillionVerifier. ## Actor input object example ```json { "include_commenters": false, "include_reactors": false, "include_reposters": false, "find_verified_work_emails": true } ``` # Actor output Schema ## `results` (type: `string`): Dataset rows with profile and email fields as configured # 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("data-slayer/linkedin-post-engagers-email-finder-no-cookies").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("data-slayer/linkedin-post-engagers-email-finder-no-cookies").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 data-slayer/linkedin-post-engagers-email-finder-no-cookies --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=data-slayer/linkedin-post-engagers-email-finder-no-cookies", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Post Engagers Email Finder", "description": "Paste any LinkedIn post URL → get enriched profiles and verified work emails of every commenter, reactor, and reposter. Full pipeline: engagement extraction + profile enrichment + email finding + email verification in one step. No cookies, no login, no subscription. Pay per lead.", "version": "1.0", "x-build-id": "PMop6ASWtKwCKamvb" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/data-slayer~linkedin-post-engagers-email-finder-no-cookies/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-data-slayer-linkedin-post-engagers-email-finder-no-cookies", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/data-slayer~linkedin-post-engagers-email-finder-no-cookies/runs": { "post": { "operationId": "runs-sync-data-slayer-linkedin-post-engagers-email-finder-no-cookies", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/data-slayer~linkedin-post-engagers-email-finder-no-cookies/run-sync": { "post": { "operationId": "run-sync-data-slayer-linkedin-post-engagers-email-finder-no-cookies", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "linkedin_post_url" ], "properties": { "linkedin_post_url": { "title": "LinkedIn post URL", "type": "string", "description": "One LinkedIn post URL in a supported format (e.g. linkedin.com/posts/… or feed/update/urn:li:activity:…). Bulk multiple URLs will be added later." }, "include_commenters": { "title": "Extract commenters", "type": "boolean", "description": "Include people who commented on the post", "default": false }, "include_reactors": { "title": "Extract reactors", "type": "boolean", "description": "Include people who reacted (liked, celebrated, etc.)", "default": false }, "include_reposters": { "title": "Extract reposters", "type": "boolean", "description": "Include people who reposted the post", "default": false }, "find_verified_work_emails": { "title": "Find verified work emails", "type": "boolean", "description": "Unchecked: full LinkedIn profile + engagement columns only. Checked: also discover work emails and verify with MillionVerifier.", "default": true } } }, "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 } } } } } } } } } } ```