{ "id": "nBuvaxvgtOYI2xmk", "meta": { "templateCredsSetupCompleted": true }, "name": "2025 Nov - Slack Slash command - check-linkedin - for publication", "tags": [ { "id": "1XLW1DVao1oAC5iT", "name": "Customer Success", "createdAt": "2025-11-15T17:21:00.676Z", "updatedAt": "2025-11-15T17:21:00.676Z" }, { "id": "1qjph8VqWHRhXnlO", "name": "Published", "createdAt": "2025-11-15T18:14:39.908Z", "updatedAt": "2025-11-15T18:14:39.908Z" }, { "id": "6OaASw2bkjvGJYfM", "name": "Experimentation", "createdAt": "2025-11-15T18:16:11.694Z", "updatedAt": "2025-11-15T18:16:11.694Z" }, { "id": "rapjVlcQ0SoPnEXg", "name": "AI", "createdAt": "2025-11-15T18:14:48.492Z", "updatedAt": "2025-11-15T18:14:48.492Z" } ], "nodes": [ { "id": "95f47a65-415c-4915-a9b0-fc272958edb1", "name": "Webhook", "type": "n8n-nodes-base.webhook", "position": [ 0, 0 ], "parameters": { "path": "path-placeholder-to-be-replaced-by-your-webhook", "options": {}, "httpMethod": "POST" }, "typeVersion": 2.1 }, { "id": "e9e68ae9-d835-4580-a73f-b11c66aac32b", "name": "Aggregate", "type": "n8n-nodes-base.aggregate", "position": [ 1872, -96 ], "parameters": { "options": {}, "fieldsToAggregate": { "fieldToAggregate": [ { "fieldToAggregate": "posted_at.date" }, { "fieldToAggregate": "text" } ] } }, "typeVersion": 1 }, { "id": "c8643766-8325-495e-8d2c-a02312585c64", "name": "Structured Output Parser", "type": "@n8n/n8n-nodes-langchain.outputParserStructured", "position": [ 2272, 112 ], "parameters": { "schemaType": "manual", "inputSchema": "{\n \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n \"title\": \"LinkedInPostsAnalysis\",\n \"type\": \"object\",\n \"additionalProperties\": false,\n \"required\": [\n \"latest_post_date\",\n \"most_recent_posts_summary\",\n \"posting_frequency\",\n \"top_topics\",\n \"recent_highlights\",\n \"data_quality\",\n \"notes\"\n ],\n \"properties\": {\n \"latest_post_date\": {\n \"type\": \"string\",\n \"format\": \"date\",\n \"description\": \"Exact date of the most recent post, YYYY-MM-DD.\"\n },\n \"most_recent_posts_summary\": {\n \"type\": \"string\",\n \"minLength\": 1,\n \"description\": \"2\u20134 sentence overview of the latest posts.\"\n },\n \"posting_frequency\": {\n \"type\": \"object\",\n \"additionalProperties\": false,\n \"required\": [\n \"average_days_between_posts\",\n \"cadence_label\",\n \"posts_per_month_estimate\"\n ],\n \"properties\": {\n \"average_days_between_posts\": {\n \"type\": \"number\",\n \"minimum\": 0\n },\n \"cadence_label\": {\n \"type\": \"string\",\n \"enum\": [\n \"~multiple per week\",\n \"~weekly\",\n \"~biweekly\",\n \"~monthly\",\n \"~every 1\u20132 months\",\n \"~sporadic\"\n ]\n },\n \"posts_per_month_estimate\": {\n \"type\": \"number\",\n \"minimum\": 0\n }\n }\n },\n \"top_topics\": {\n \"type\": \"array\",\n \"minItems\": 3,\n \"maxItems\": 3,\n \"items\": {\n \"type\": \"object\",\n \"additionalProperties\": false,\n \"required\": [\"topic\", \"keywords\", \"rationale\"],\n \"properties\": {\n \"topic\": {\n \"type\": \"string\",\n \"minLength\": 1\n },\n \"keywords\": {\n \"type\": \"array\",\n \"minItems\": 2,\n \"maxItems\": 6,\n \"items\": {\n \"type\": \"string\",\n \"minLength\": 1\n }\n },\n \"rationale\": {\n \"type\": \"string\",\n \"minLength\": 1\n }\n }\n }\n },\n \"recent_highlights\": {\n \"type\": \"array\",\n \"minItems\": 2,\n \"maxItems\": 5,\n \"items\": {\n \"type\": \"object\",\n \"additionalProperties\": false,\n \"required\": [\"date\", \"summary\", \"why_it_matters\"],\n \"properties\": {\n \"date\": {\n \"type\": \"string\",\n \"format\": \"date\"\n },\n \"summary\": {\n \"type\": \"string\",\n \"minLength\": 1\n },\n \"why_it_matters\": {\n \"type\": \"string\",\n \"minLength\": 1\n }\n }\n }\n },\n \"data_quality\": {\n \"type\": \"object\",\n \"additionalProperties\": false,\n \"required\": [\"num_posts_detected\", \"num_dates_used\", \"date_range\"],\n \"properties\": {\n \"num_posts_detected\": {\n \"type\": \"integer\",\n \"minimum\": 0\n },\n \"num_dates_used\": {\n \"type\": \"integer\",\n \"minimum\": 0\n },\n \"date_range\": {\n \"type\": \"object\",\n \"additionalProperties\": false,\n \"required\": [\"start\", \"end\"],\n \"properties\": {\n \"start\": {\n \"type\": \"string\",\n \"format\": \"date\"\n },\n \"end\": {\n \"type\": \"string\",\n \"format\": \"date\"\n }\n }\n }\n }\n },\n \"notes\": {\n \"type\": \"string\",\n \"description\": \"Assumptions, ambiguities, or caveats; empty string if none.\"\n }\n }\n}" }, "typeVersion": 1.3 }, { "id": "be2accab-b963-4aed-9280-76d427e93597", "name": "Summer McBriefing", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ 2080, -96 ], "parameters": { "text": "=Dates:\n{{ $json.date }}\n\nPosts: \n{{ $json.text }}", "options": { "systemMessage": "You are an analytical assistant that receives:\n1) A list of dates for a single person\u2019s recent LinkedIn posts, and\n2) One aggregated text block containing the corresponding posts with the most recent first.\n\nYour job is to return a concise, factual analysis with:\n- Latest post exact date\n- Brief summary of the received posts\n- Average posting frequency (numeric average days between posts, a cadence label, and a posts-per-month estimate)\n- Exactly three main topics (with keywords and a one-line rationale each)\n- A few recent highlights (date + why it matters)\n- Data quality stats and any notes\n\nRules:\n- Use only provided dates/text; no outside knowledge.\n- Normalize dates to YYYY-MM-DD. If times exist, ignore time-of-day.\n- Frequency: sort dates desc, compute gaps between consecutive dates, average the gaps. If <3 dates, compute with what\u2019s available and note the limitation.\n- Cadence labels:\n \u22645 \u2192 \"~multiple per week\"\n 6\u201310 \u2192 \"~weekly\"\n 11\u201320 \u2192 \"~biweekly\"\n 21\u201345 \u2192 \"~monthly\"\n 46\u201390 \u2192 \"~every 1\u20132 months\"\n >90 \u2192 \"~sporadic\"\n- Topics: detect recurring themes; exactly 3 topics, each with 2\u20136 keywords and a one-line rationale.\n- Highlights: focus on last 3\u20135 posts (by date), each with date + significance.\n- If parsing is uncertain, state assumptions in \"notes\".\n\nOutput:\n- Return **ONLY** valid JSON matching the provided JSON Schema. No prose, no extra fields, no nulls. " }, "promptType": "define", "hasOutputParser": true }, "typeVersion": 3 }, { "id": "853a0068-09e8-4368-a735-a1e149549fc2", "name": "HTML", "type": "n8n-nodes-base.html", "position": [ 2528, -96 ], "parameters": { "html": "\n\n\n \n \n Profile Activity Brief\n \n\n\n\n \n \n \n \n
\n \n \n \n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n\n
\n

{{ $('Extract firstName and lastName').item.json.output.firstName }} {{ $('Extract firstName and lastName').item.json.output.lastName }}

\n Profile Activity Brief\n
\n \n \n \n \n \n \n \n \n \n \n
\n Latest Post\n {{ $json.output.latest_post_date }}\n  \n Cadence\n {{ $json.output.posting_frequency.cadence_label }}\n  \n Est. Monthly\n {{ $json.output.posting_frequency.posts_per_month_estimate }}\n
\n\n \n
\n Top Focus Areas\n \n {{ $json.output.top_topics[0].topic }}\n {{ $json.output.top_topics[1].topic }}\n
\n\n \n

Recent Activity Summary

\n

\n {{ $json.output.most_recent_posts_summary }}\n

\n\n \n

Detailed Analysis

\n \n \n
\n

{{ $json.output.top_topics[0].topic }}

\n

\n {{ $json.output.top_topics[0].rationale }}\n

\n
\n Keywords: \n \n {{ $json.output.top_topics[0].keywords }}\n
\n
\n\n \n
\n

{{ $json.output.top_topics[1].topic }}

\n

\n {{ $json.output.top_topics[1].rationale }}\n

\n
\n Keywords: \n {{ $json.output.top_topics[1].keywords }}\n
\n
\n\n
\n Generated on {{ $json.output.latest_post_date }}\n
\n \n\n
\n\n\n" }, "typeVersion": 1.2 }, { "id": "af48e025-998b-4711-aa79-dd2241234a25", "name": "Send report via Email", "type": "n8n-nodes-base.gmail", "position": [ 2736, -96 ], "parameters": { "sendTo": "admin@example.com", "message": "={{ $json.html }}", "options": {}, "subject": "=Recent LinkedIn Activity about {{ $('Get Linkedin Profile URL').item.json.personName }}" }, "credentials": { "gmailOAuth2": { "name": "" } }, "typeVersion": 2.1 }, { "id": "1a5ff79c-1fe3-4df6-bd1e-2a4c0057c531", "name": "Sticky Note12", "type": "n8n-nodes-base.stickyNote", "position": [ 2960, -112 ], "parameters": { "width": 320, "height": 144, "content": "## Questions or Feedback?\n\n\ud83d\udce7 emir.belkahia@gmail.com \n\ud83d\udcbc [linkedin.com/in/emirbelkahia](https://www.linkedin.com/in/emirbelkahia/)" }, "typeVersion": 1 }, { "id": "38325b74-a6ad-4113-a296-4de8de4462dc", "name": "Tell on slack that no full name was found", "type": "n8n-nodes-base.slack", "position": [ 640, 336 ], "parameters": { "text": "={{ $json.body.text }} is not a full name. Please send a full name to get information.", "select": "channel", "channelId": { "__rl": true, "mode": "id", "value": "={{ $json.body.channel_id }}" }, "otherOptions": {}, "authentication": "oAuth2" }, "credentials": { "slackOAuth2Api": { "name": "" } }, "typeVersion": 2.3 }, { "id": "78626cb3-6788-42c2-9eba-bea75a2fc638", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [ 544, 288 ], "parameters": { "color": 3, "width": 304, "height": 320, "content": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n## Error handling\nNo full name found in the slack command." }, "typeVersion": 1 }, { "id": "399b976a-3c5a-4f6f-a79f-b6fb356dd61f", "name": "GPT 4.1-mini for classification", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 160, 192 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4.1-mini" }, "options": {}, "responsesApiEnabled": false }, "credentials": { "openAiApi": { "name": "" } }, "typeVersion": 1.3 }, { "id": "fdcd4e6b-6e08-466c-9c06-d8d8517bb50b", "name": "Do we have a full name ?", "type": "@n8n/n8n-nodes-langchain.textClassifier", "position": [ 208, 0 ], "parameters": { "options": {}, "inputText": "={{ $json.body.text }}", "categories": { "categories": [ { "category": "full-name", "description": "The input text is a full name of a person" }, { "category": "not-full-name", "description": "The input text is not a full name of a person" } ] } }, "typeVersion": 1.1 }, { "id": "95d18f4f-c8ee-43ba-88a9-527829a23061", "name": "GPT 4.1-mini to extract firstName + lastName", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 640, 96 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4.1-mini" }, "options": {}, "builtInTools": {} }, "credentials": { "openAiApi": { "name": "" } }, "typeVersion": 1.3 }, { "id": "cc26d2f5-4377-4f78-8f7b-13cae8edeedb", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ -960, -288 ], "parameters": { "width": 880, "height": 832, "content": "# Slack Slash Command: LinkedIn Profile Recent Activity Summary\n- Run `/check-linkedin [firstName lastName]` in Slack to get an AI-powered brief on someone's LinkedIn activity.\n- Get instant context before outreach or meetings: recent posts, posting patterns, key topics, and engagement insights delivered straight to your inbox. No manual scrolling through profiles needed.\n\n## How it works\n- Type `/check-linkedin` followed by a person's first and last name in any Slack channel\n- Workflow extracts the name and searches LinkedIn via Apify\n- Fetches profile data and recent posts (up to 20)\n- AI analyzes posting frequency, top topics, and recent highlights\n- Structured summary generated via GPT 4.1 with posting cadence and engagement patterns\n- Formatted HTML report sent to your email with profile link and actionable insights\n\n## Setup steps\n- Create a Slack App with `/check-linkedin` slash command via Slack API console\n- Set the slash command Request URL to your n8n webhook endpoint\n- Connect credentials: Slack (chat:write, commands), Apify, Open AI, Gmail\n- Configure two Apify actors: LinkedIn Profile Lookup and LinkedIn Post Scraper\n- Update email recipient in \"Send report via Email\" node\n- Set your Slack workspace ID in error handling nodes\n\n## Customization\n- Name detection: The workflow expects a full name (ideally \"First Last\") format. For LinkedIn URLs or handles, add a conditional branch to parse differently.\n- Post limit: Currently fetches 20 recent posts. Adjust the limit parameter in \"Get recent posts\" node for more/less context.\n- Analysis depth: Modify the JSON schema in \"Structured Output Parser\" to add fields like company mentions, sentiment, or engagement metrics.\n- Cost control:\n - ApiFy actors have per-run costs. \n - The output size can be heavy depending on your parameters, so check it carefully to avoid consuming too many summarization tokens in the following OpenAI node." }, "typeVersion": 1 }, { "id": "0d1bb4d7-213e-4978-bc30-eb54dfdcaa4b", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "position": [ -32, -336 ], "parameters": { "color": 7, "width": 912, "height": 1008, "content": "## Slack Intake & Command Parsing\nFrom /check-linkedin trigger to the point where you extract a clear First Name and a clear Last Name" }, "typeVersion": 1 }, { "id": "a5380069-f6e2-45c8-9054-9f82982c75a4", "name": "Sticky Note3", "type": "n8n-nodes-base.stickyNote", "position": [ 944, -352 ], "parameters": { "color": 7, "width": 816, "height": 496, "content": "## Data Gathering via ApiFy\nThe four ApiFy nodes responsible for fetching LinkedIn data:\n- We get the profile URL from the First Name and Last Name\n- Then we scrap the recent posts of this profile URL\n\n\n\ud83d\udcb0 This zone is cost sensitive and dependent on the ApiFy actors that you select \ud83d\udcb0" }, "typeVersion": 1 }, { "id": "f2439c0b-f6dc-4d35-829d-ee0946c78002", "name": "Sticky Note4", "type": "n8n-nodes-base.stickyNote", "position": [ 1824, -352 ], "parameters": { "color": 7, "width": 592, "height": 768, "content": "## Summarization & Intelligence Layer\nConsolidate the raw data and generate a clean, human-readable summary.\n- Merge JSON\n- GPT 4.1 (LLM summarization)\n- Formatter to get a consistent output format\n\n\ud83d\udcb0 Summarization via GPT 4.1 per-call cost \ud83d\udcb0\n" }, "typeVersion": 1 }, { "id": "a967607c-de2b-4dce-9571-2cb98d48f4ae", "name": "Sticky Note5", "type": "n8n-nodes-base.stickyNote", "position": [ 2480, -352 ], "parameters": { "color": 7, "width": 416, "height": 768, "content": "## HTML Output & Response via Email\nFormat in HTML and send the result back via email to the requester." }, "typeVersion": 1 }, { "id": "69c0d193-2864-45a0-b053-d9ebccea2561", "name": "Extract firstName and lastName", "type": "@n8n/n8n-nodes-langchain.informationExtractor", "position": [ 560, -96 ], "parameters": { "text": "={{ $json.body.text }}", "options": {}, "attributes": { "attributes": [ { "name": "firstName", "required": true, "description": "First Name of the person" }, { "name": "lastName", "required": true, "description": "Last Name of the person" } ] } }, "typeVersion": 1.2 }, { "id": "eeae4a59-1f6a-4227-9329-f15d42dbefa5", "name": "Get Linkedin Profile URL", "type": "@apify/n8n-nodes-apify.apify", "position": [ 1200, -96 ], "parameters": { "offset": {}, "resource": "Datasets", "datasetId": "={{ $json.defaultDatasetId }}" }, "credentials": { "apifyApi": { "name": "" } }, "typeVersion": 1 }, { "id": "b1e5322d-e8c2-43da-b58a-d300a4615968", "name": "Find Linkedin Profile", "type": "@apify/n8n-nodes-apify.apify", "position": [ 992, -96 ], "parameters": { "actorId": { "__rl": true, "mode": "url", "value": "https://console.apify.com/actors/FbqC9BRstFBddhUqj/input" }, "timeout": {}, "customBody": "={\n \"firstName\": \"{{ $json.output.firstName }}\",\n \"lastName\": \"{{ $json.output.lastName }}\"\n}" }, "credentials": { "apifyApi": { "name": "" } }, "typeVersion": 1 }, { "id": "f82112e5-7c7e-4a82-b5ab-5291971ea83d", "name": "Scrap what this person posted recently", "type": "@apify/n8n-nodes-apify.apify", "position": [ 1408, -96 ], "parameters": { "actorId": { "__rl": true, "mode": "url", "value": "https://console.apify.com/actors/r4oNX7IHlW4RQAjKP" }, "timeout": {}, "customBody": "={\n \"usernames\": [\n \"{{ $json.linkedinProfileUrl }}\"\n ]\n}" }, "credentials": { "apifyApi": { "name": "" } }, "typeVersion": 1 }, { "id": "c43e8ab6-e80f-40f6-9f1d-05578ae31ed4", "name": "Structure recent posts", "type": "@apify/n8n-nodes-apify.apify", "position": [ 1600, -96 ], "parameters": { "limit": 20, "offset": {}, "resource": "Datasets", "datasetId": "={{ $json.defaultDatasetId }}" }, "credentials": { "apifyApi": { "name": "" } }, "typeVersion": 1 }, { "id": "db0f0a46-4a09-493c-ae47-a68de7477131", "name": "GPT 4.1", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 2000, 112 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4.1", "cachedResultName": "gpt-4.1" }, "options": {}, "builtInTools": {} }, "credentials": { "openAiApi": { "name": "" } }, "typeVersion": 1.3 }, { "id": "644f0556-3a48-4e63-b80b-54c724c30cf4", "name": "Sticky Note9", "type": "n8n-nodes-base.stickyNote", "position": [ -960, 592 ], "parameters": { "color": 6, "width": 880, "height": 576, "content": "# \ud83c\udf99\ufe0f Video Walkthrough\n@[youtube](-4VwNYsOIPc)" }, "typeVersion": 1 } ], "active": false, "settings": { "executionOrder": "v1" }, "versionId": "a5903b61-eb44-4418-a26b-75e1a3b983a9", "connections": { "HTML": { "main": [ [ { "node": "Send report via Email", "type": "main", "index": 0 } ] ] }, "GPT 4.1": { "ai_languageModel": [ [ { "node": "Summer McBriefing", "type": "ai_languageModel", "index": 0 } ] ] }, "Webhook": { "main": [ [ { "node": "Do we have a full name ?", "type": "main", "index": 0 } ] ] }, "Aggregate": { "main": [ [ { "node": "Summer McBriefing", "type": "main", "index": 0 } ] ] }, "Summer McBriefing": { "main": [ [ { "node": "HTML", "type": "main", "index": 0 } ] ] }, "Find Linkedin Profile": { "main": [ [ { "node": "Get Linkedin Profile URL", "type": "main", "index": 0 } ] ] }, "Structure recent posts": { "main": [ [ { "node": "Aggregate", "type": "main", "index": 0 } ] ] }, "Do we have a full name ?": { "main": [ [ { "node": "Extract firstName and lastName", "type": "main", "index": 0 } ], [ { "node": "Tell on slack that no full name was found", "type": "main", "index": 0 } ] ] }, "Get Linkedin Profile URL": { "main": [ [ { "node": "Scrap what this person posted recently", "type": "main", "index": 0 } ] ] }, "Structured Output Parser": { "ai_outputParser": [ [ { "node": "Summer McBriefing", "type": "ai_outputParser", "index": 0 } ] ] }, "Extract firstName and lastName": { "main": [ [ { "node": "Find Linkedin Profile", "type": "main", "index": 0 } ] ] }, "GPT 4.1-mini for classification": { "ai_languageModel": [ [ { "node": "Do we have a full name ?", "type": "ai_languageModel", "index": 0 } ] ] }, "Scrap what this person posted recently": { "main": [ [ { "node": "Structure recent posts", "type": "main", "index": 0 } ] ] }, "GPT 4.1-mini to extract firstName + lastName": { "ai_languageModel": [ [ { "node": "Extract firstName and lastName", "type": "ai_languageModel", "index": 0 } ] ] } } }