# Slack Scraper (`leadspicker/slack-scraper`) Actor This Apify actor scrapes members from any Slack workspace and matches them to their LinkedIn profiles using image search and AI-based relevance classification. - **URL**: https://apify.com/leadspicker/slack-scraper.md - **Developed by:** [Leadspicker](https://apify.com/leadspicker) (community) - **Categories:** Lead generation, Social media, AI - **Stats:** 30 total users, 2 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 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 ## Slack Scraper (with LinkedIn resolving) Scrape members from **any Slack workspace you’re a member of** (no admin rights needed) and enrich them with **LinkedIn profiles** using **avatar image search**, **name matching**, and **AI relevancy classification**. 🎥 **Watch how to authorize and run the Actor (Quick Start)** 👉 https://www.youtube.com/watch?v=J6E1SIbHB1k This scraper was built by the www.leadspicker.com team at the Prague $1M Hackathon in January 2026. > **Claim (and why it matters):** This is the **only Slack member scraper that’s actually useful for lead-gen** because it doesn’t stop at scraping and exporting member names - it goes a step further and **finds the LinkedIn profile URLs of those people using avatar image search, LinkedIn name search, and AI-based relevancy classification**, even when multiple people share the same name. --- ### What this Actor does - **Scrapes Slack workspace members** and exports structured member data including full name. - **Optionally resolves LinkedIn profiles** for members with custom avatars via **image search + name matching**. - **Optionally runs AI classification** (customizable prompt) to help you: - filter relevant people (e.g., automation experts / Growth hackers / GTM Engineers, Clay or n8n users, marketing/tech..), - outputs the best match when multiple LinkedIn candidates exist, --- ### Why this is different (and better than any other “Slack scraper”) Most “Slack scrapers” either: - require being an admin, or - only export user names without linkedin profile urls, or - break when the workspace has common names and duplicates, - require you authorize through unsecure chrome extension This Actor is built for real workflows: - ✅ **No admin required** (works for any workspace you can log into) - ✅ **No browser extension required** (recommended flow uses Live View login) - ✅ **Image-based LinkedIn discovery** (great match rate for people with avatars on Slack) - ✅ **Name disambiguation** (multiple people named “John Smith”? handled!) - ✅ **AI relevancy classification** (so you can keep only the people you actually want) --- #### Authentication & basic setup (recommended) 🎥 **Watch how to authorize and run the Actor (Quick Start)** 👉 https://www.youtube.com/watch?v=J6E1SIbHB1k - **Slack Client Link (required)** Enter your workspace URL, for example: `https://app.slack.com/client/T06FSM2U6FJ` This workspace ID determines **which Slack workspace will be scraped**. - **Interactive Login (VNC)** Keep this **enabled**. This allows you to log in via a **virtual browser**: - Start the Actor - Click **Live View** - Sign in to Slack normally The Actor will automatically load the session and cookies for you. > When using Interactive Login, you can leave `xoxc Token` and `xoxd Cookie (d)` **empty**. --- #### Authentication – Option B (manual, not recommended) > ⚠️ **Not recommended**, but works as fallback if the **virtual browser (VNC / Live View) login does not work** for you This option allows you to **manually extract Slack session credentials** (`xoxc` token and `xoxd` cookie) from your browser and paste them into the Actor input fields. Use this only if Interactive Login fails. 🎥 **Fall back option how to authorize manually:** 👉 https://youtu.be/-T4Eei-ZYVA --- ##### Step 1: Sign in to Slack (browser only) - Make sure you are fully logged in to your Slack workspace at: https://app.slack.com - You must be logged in via the **web app (browser)** *(the desktop app alone is not sufficient)* --- ##### Step 2: Install Cookie-Editor (Chrome extension) Install the **Cookie-Editor** extension from the Chrome Web Store: 👉 https://chromewebstore.google.com/detail/cookie-editor/hlkenndednhfkekhgcdicdfddnkalmdm?hl=en --- ##### Step 3: Retrieve the `xoxc` token 1. Open the Slack workspace you want to scrape 2. Open **Developer Tools** (`F12`) 3. Go to the **Console** tab 4. Paste and run the following command: ```` JSON.stringify(Object.fromEntries(Object.entries(JSON.parse(localStorage.localConfig\_v2)?.teams ?? {}).map((\[teamKey, team]) => \[teamKey, team?.token]))); ```` - Copy the output — this is your **`xoxc` token** 📹 **How-to video:** 👉 https://www.loom.com/share/f96ec08772ab49bc8afa6f63b8130742 --- ##### Step 4: Open Cookie-Editor - Click the **Cookie-Editor** icon (🍪) in your Chrome toolbar - If you don’t see it, open the **Extensions (puzzle)** menu and pin it - Make sure Cookie-Editor is opened on the **app.slack.com** page --- ##### Step 5: Export the `xoxd` cookie - In Cookie-Editor, search for the cookie named **`d`** - Copy its value This value is your **`xoxd` cookie (`d`)**. --- ##### Step 6: Paste credentials into the Actor Paste the following values into the Actor input fields: - **`xoxc Token`** - **`xoxd Cookie (d)`** You can now run the Actor **without Interactive Login**. #### LinkedIn enrichment - **Resolve LinkedIn** Keep this **enabled** if you want the Actor to also find **LinkedIn profile URLs** for scraped Slack members. - **LinkedIn URLs on New Lines** Keep this **enabled** if you want **one LinkedIn profile per row**. If multiple relevant LinkedIn profiles match the same Slack name, each match will be returned as a **separate row**. --- #### Filtering & matching - **Search Query** Optional. Allows you to narrow down the Slack directory search. **Recommended:** leave empty to scrape all members. - **Prompt** This prompt helps decide **which people with the same name should be included** in the export. It is used during AI-based relevancy classification and LinkedIn disambiguation. **Example prompt:** ```text You are a simple classifier. Question: Does this person have ANY connection to sales, marketing, AI, lead generation, growth, automation, or technology? Say YES if you see ANY of these (current or past): - Sales (any role) - Marketing (any role) - Business development - Growth - RevOps, Sales Ops, Marketing Ops - GTM, Go-to-Market - Outreach, Lead generation - CRM, Salesforce, HubSpot - Automation, integrations, Zapier - Agency, consulting - SaaS, B2B, tech startup - Founder, CEO, entrepreneur - Account management - Customer success - Partnerships - Product (PM, Product Manager) - Data, analytics for business Say NO only if none of the above apply (e.g. finance, legal). Return ONLY: YES or NO Profile: {profile_json} ```` *** ### Output Each dataset item represents **one Slack member**, optionally enriched with a **LinkedIn profile match**. If multiple relevant LinkedIn profiles are found for the same Slack user (for example, when several people share the same name), the Actor can return **multiple rows for the same Slack member**, each with a different `linkedin_url`. #### Output fields Each row may contain the following fields: - **Slack identity** - `id` — Slack user ID - `team` — Slack workspace ID - `username` — Slack username - `real_name` — full name from Slack - `display_name` — display name (if set) - **Profile details** - `title` — job title (if available) - `email` — email address (if available) - `phone` — phone number (if available) - **Profile images** - `image_1024` — Slack profile image (1024px) - `image_original` — original avatar image - `is_custom_image` — whether the user has a custom avatar - **LinkedIn enrichment (optional)** - `linkedin_url` — a resolved LinkedIn profile URL - If multiple relevant matches exist, **each LinkedIn profile is returned as a separate row** for the same Slack user. This row-based output makes it easy to: - export data to CSV / spreadsheets, - review multiple LinkedIn candidates, - apply additional filtering or scoring in downstream tools. #### LinkedIn output formats You can work in two practical modes: 1. **One LinkedIn profile per row** (best match only) — great for exports and CRMs. 2. **All potential matches per Slack user** (kept together) — great for manual review and QA. *(If you’re not sure which to use: start with “all matches”, then tighten your `prompt` until “best match” is reliably correct.)* *** ### Typical use cases - **Lead generation** from partner/customer communities (filter by roles + match to LinkedIn) - **Recruiting / sourcing** from niche Slack groups - **Partnership mapping** (who’s inside a workspace + what they do) - **Community analytics** (titles, domains, seniority distribution) - **Outbound personalization** (Slack profile → LinkedIn → enrichment pipeline) *** ### Notes, limitations, and best practices - Scrape only workspaces you have legitimate access to and permission to use. :contentReference\[oaicite:19]{index=19} - Slack may rate-limit large exports. Use `maxResults`, filtering `query`, and reasonable timeouts. - For best LinkedIn matching: - enable `resolveAvatarLinkedins`, - use a stricter `prompt` when your workspace has many repeated names, - prefer “all matches per row” until you’re happy with quality. *** ### Support / feedback Found an edge case (duplicate names, missing avatars, wrong LinkedIn picks)? Open an issue in the Actor’s **Issues** tab on Apify and include: - a short description, - a sample output row (without sensitive data), - what you expected vs what happened. *** #### Keywords Slack scraper · Slack members export · LinkedIn matching · avatar image search · AI classification · lead generation · enrichment # Actor input Schema ## `xoxcToken` (type: `string`): Slack xoxc form token used for directory search requests. You can also pass a JSON object mapping team IDs to tokens; the token is selected by clientPath team ID. Optional when Interactive Login is enabled. ## `xoxdToken` (type: `string`): Value of the Slack cookie named d (xoxd token). Optional when Interactive Login is enabled. ## `clientPath` (type: `string`): Slack client URL like https://app.slack.com/client/TXXXXXX. ## `interactiveLogin` (type: `boolean`): When enabled, opens a browser via VNC so you can log in and the Actor will extract xoxc and xoxd tokens automatically. ## `interactiveLoginTimeoutSecs` (type: `number`): How long to wait for you to log in before timing out. ## `resolveAvatarLinkedins` (type: `boolean`): When enabled, resolves custom avatar images to LinkedIn URLs. ## `linkedinUrlsNewline` (type: `boolean`): When enabled, outputs each LinkedIn URL on its own line instead of separating by semicolons. ## `query` (type: `string`): Optional search query for filtering people. ## `prompt` (type: `string`): Prompt template used for classification. ## `maxResults` (type: `integer`): Maximum number of people to fetch. ## `proxyUrl` (type: `string`): Optional proxy URL applied to HTTP and HTTPS requests. ## `timeoutSecs` (type: `number`): Request timeout in seconds. ## Actor input object example ```json { "clientPath": "https://app.slack.com/client/TXXXXXXXX", "interactiveLogin": false, "interactiveLoginTimeoutSecs": 900, "resolveAvatarLinkedins": false, "linkedinUrlsNewline": false, "query": "", "prompt": "You are a simple classifier.\n\nQuestion: Does this person have ANY connection to business, sales, marketing, or technology?\n\nSay YES if you see ANY of these (current or past):\nSales (any role)\nMarketing (any role)\nBusiness development\nGrowth\nRevOps, Sales Ops, Marketing Ops\nGTM, Go-to-Market\nOutreach, Lead generation\nCRM, Salesforce, HubSpot\nAutomation, integrations, Zapier\nAgency, consulting\nSaaS, B2B, tech startup\nFounder, CEO, entrepreneur\nAccount management\nCustomer success\nPartnerships\nProduct (PM, Product Manager)\nData, analytics for business\n\nSay NO only if none of the above seems to be a good match.\n\nWhen unsure, say YES.\n\nReturn ONLY: YES or NO\n\nProfile:\n{profile_json}", "maxResults": 50, "timeoutSecs": 30 } ``` # 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 = { "clientPath": "https://app.slack.com/client/TXXXXXXXX" }; // Run the Actor and wait for it to finish const run = await client.actor("leadspicker/slack-scraper").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 = { "clientPath": "https://app.slack.com/client/TXXXXXXXX" } # Run the Actor and wait for it to finish run = client.actor("leadspicker/slack-scraper").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 '{ "clientPath": "https://app.slack.com/client/TXXXXXXXX" }' | apify call leadspicker/slack-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=leadspicker/slack-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Slack Scraper", "description": "This Apify actor scrapes members from any Slack workspace and matches them to their LinkedIn profiles using image search and AI-based relevance classification.", "version": "0.0", "x-build-id": "xMuTEz8U49sxPwQGk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/leadspicker~slack-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-leadspicker-slack-scraper", "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/leadspicker~slack-scraper/runs": { "post": { "operationId": "runs-sync-leadspicker-slack-scraper", "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/leadspicker~slack-scraper/run-sync": { "post": { "operationId": "run-sync-leadspicker-slack-scraper", "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": [ "clientPath" ], "properties": { "xoxcToken": { "title": "xoxc Token", "type": "string", "description": "Slack xoxc form token used for directory search requests. You can also pass a JSON object mapping team IDs to tokens; the token is selected by clientPath team ID. Optional when Interactive Login is enabled." }, "xoxdToken": { "title": "xoxd Cookie (d)", "type": "string", "description": "Value of the Slack cookie named d (xoxd token). Optional when Interactive Login is enabled." }, "clientPath": { "title": "Slack Client Link", "type": "string", "description": "Slack client URL like https://app.slack.com/client/TXXXXXX." }, "interactiveLogin": { "title": "Interactive Login (VNC)", "type": "boolean", "description": "When enabled, opens a browser via VNC so you can log in and the Actor will extract xoxc and xoxd tokens automatically.", "default": false }, "interactiveLoginTimeoutSecs": { "title": "Interactive Login Timeout (seconds)", "minimum": 30, "type": "number", "description": "How long to wait for you to log in before timing out.", "default": 900 }, "resolveAvatarLinkedins": { "title": "Resolve LinkedIn", "type": "boolean", "description": "When enabled, resolves custom avatar images to LinkedIn URLs.", "default": false }, "linkedinUrlsNewline": { "title": "LinkedIn URLs on New Lines", "type": "boolean", "description": "When enabled, outputs each LinkedIn URL on its own line instead of separating by semicolons.", "default": false }, "query": { "title": "Search Query", "type": "string", "description": "Optional search query for filtering people.", "default": "" }, "prompt": { "title": "Prompt", "type": "string", "description": "Prompt template used for classification.", "default": "You are a simple classifier.\n\nQuestion: Does this person have ANY connection to business, sales, marketing, or technology?\n\nSay YES if you see ANY of these (current or past):\nSales (any role)\nMarketing (any role)\nBusiness development\nGrowth\nRevOps, Sales Ops, Marketing Ops\nGTM, Go-to-Market\nOutreach, Lead generation\nCRM, Salesforce, HubSpot\nAutomation, integrations, Zapier\nAgency, consulting\nSaaS, B2B, tech startup\nFounder, CEO, entrepreneur\nAccount management\nCustomer success\nPartnerships\nProduct (PM, Product Manager)\nData, analytics for business\n\nSay NO only if none of the above seems to be a good match.\n\nWhen unsure, say YES.\n\nReturn ONLY: YES or NO\n\nProfile:\n{profile_json}" }, "maxResults": { "title": "Max Results", "minimum": 1, "type": "integer", "description": "Maximum number of people to fetch.", "default": 50 }, "proxyUrl": { "title": "Proxy URL", "type": "string", "description": "Optional proxy URL applied to HTTP and HTTPS requests." }, "timeoutSecs": { "title": "Timeout (seconds)", "type": "number", "description": "Request timeout in seconds.", "default": 30 } } }, "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 } } } } } } } } } } ```