{ "name": "LinkedIn Growth & Intelligence Agent", "nodes": [ { "parameters": { "chatId": "={{ $('Prep - Telegram').item.json.chat_id }}", "text": "=\ud83d\udc4b *Hey! Welcome to LinkedIn Growth & Intelligence Agent*\n\n\ud83c\udfaf *What I do* \nI track public LinkedIn company data in real time and turn it into actionable insights for: \n\u2022 \ud83d\udd0e Smart B2B prospecting (opportunity scoring & next best actions) \n\u2022 \ud83d\udcca Competitive intelligence (hiring spikes, leadership changes, company pivots) \n\u2022 \ud83d\udca1 Content recommendations (top engaging posts & ideas you can reuse) \n\n\u2728 *Commands* \n\u2022 /prospect \u2192 analyze a LinkedIn company URL and score opportunities \n\u2022 /watch \u2192 start monitoring competitors and get alerts on key changes \n\u2022 /content \u2192 get weekly trending content + new post ideas \n\u2022 /clear \u2192 reset your current selection \n\u2022 /help \u2192 show usage tips \n\n\u2139\ufe0f *Good to know* \nOnly public LinkedIn data is processed. Insights are delivered straight to Slack/Telegram or your CRM.\n", "additionalFields": { "appendAttribution": false } }, "type": "n8n-nodes-base.telegram", "typeVersion": 1.2, "position": [ -1248, 640 ], "id": "e1543155-4cd0-4476-b53d-816b1d23bb35", "name": "/start", "credentials": { "telegramApi": { "name": "" } } }, { "parameters": { "chatId": "={{ $('Prep - Telegram').item.json.chat_id }}", "text": "=\ud83e\udd14 *How to use LinkedIn Growth & Intelligence Agent*\n\n\ud83d\udd0e *Step 1 \u2014 Send me a LinkedIn company URL* \nJust type or paste one a public LinkedIn company profiles, like: \nhttps://www.linkedin.com/company/openai/ \nhttps://www.linkedin.com/company/nvidia/ \n\n\ud83d\udee0\ufe0f *Step 2 \u2014 Commands* \n\u2022 /prospect \u2192 analyze the company you sent and score opportunities \n\u2022 /content \u2192 get trending posts & ideas for your own LinkedIn content \n\u2022 /clear \u2192 reset your current company list \n\u2022 /help \u2192 show this help message again \n\n\ud83d\udcca *What you\u2019ll get* \nFor each company, I\u2019ll provide: \n- Size, industry & followers \n- Hiring activity & growth signals \n- Engagement trends on recent posts \n- A *business opportunity score* + recommended next actions \n\n\u2139\ufe0f *Pro tip* \nOnly public LinkedIn data is used. Perfect for *B2B prospecting, competitive intelligence, and content strategy*.\n", "additionalFields": { "appendAttribution": false } }, "type": "n8n-nodes-base.telegram", "typeVersion": 1.2, "position": [ -1472, 1168 ], "id": "616e5d58-0acc-4ffd-b700-5dc93bde4ebc", "name": "/help", "credentials": { "telegramApi": { "name": "" } } }, { "parameters": { "chatId": "={{ $('Prep - Telegram').item.json.chat_id }}", "text": "=\ud83e\uddf9 *Selection cleared* \n\nSend me new LinkedIn company or profile URL, then use: \n\n\u2022 /prospect \u2192 analyze & score \n\u2022 /content \u2192 get content ideas\n", "additionalFields": { "appendAttribution": false } }, "type": "n8n-nodes-base.telegram", "typeVersion": 1.2, "position": [ -1248, 832 ], "id": "ba68206f-6480-4d91-a315-6e81d350fc40", "name": "/clear", "credentials": { "telegramApi": { "name": "" } } }, { "parameters": { "assignments": { "assignments": [ { "id": "765ed8db-7152-4c40-87b8-e0b9cfd3e80f", "name": "chat_id", "value": "={{ $json.message.chat.id }}", "type": "string" }, { "id": "6afe501f-5edc-4c26-b482-f62748f2b245", "name": "text", "value": "={{ $json.message.text }}", "type": "string" } ] }, "options": {} }, "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ -2592, 1072 ], "id": "6618de56-7422-4a27-95df-7568eab561f5", "name": "Prep - Telegram" }, { "parameters": { "operation": "upsert", "schema": { "__rl": true, "mode": "list", "value": "public" }, "table": { "__rl": true, "value": "linkedin_selection", "mode": "list", "cachedResultName": "linkedin_selection" }, "columns": { "mappingMode": "defineBelow", "value": { "chat_id": "={{ $json.chat_id }}", "urls": "={{ $json.urls }}" }, "matchingColumns": [ "chat_id" ], "schema": [ { "id": "chat_id", "displayName": "chat_id", "required": true, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true, "removed": false }, { "id": "urls", "displayName": "urls", "required": false, "defaultMatch": false, "display": true, "type": "array", "canBeUsedToMatch": false }, { "id": "updated_at", "displayName": "updated_at", "required": false, "defaultMatch": false, "display": true, "type": "dateTime", "canBeUsedToMatch": false } ], "attemptToConvertTypes": false, "convertFieldsToString": false }, "options": {} }, "type": "n8n-nodes-base.postgres", "typeVersion": 2.6, "position": [ -1920, 1168 ], "id": "7f59afeb-7a8f-4446-9500-70f9d1347b4b", "name": "Upsert LinkedIn URLs", "credentials": { "postgres": { "name": "" } } }, { "parameters": { "operation": "deleteTable", "schema": { "__rl": true, "mode": "list", "value": "public" }, "table": { "__rl": true, "value": "linkedin_selection", "mode": "list", "cachedResultName": "linkedin_selection" }, "deleteCommand": "delete", "where": { "values": [ { "column": "chat_id", "value": "={{ $('Prep - Telegram').item.json.chat_id }}" } ] }, "options": {} }, "type": "n8n-nodes-base.postgres", "typeVersion": 2.6, "position": [ -1472, 832 ], "id": "3f1ef9ab-a8af-415e-99d1-c3dd88de751d", "name": "Delete stored chat", "credentials": { "postgres": { "name": "" } } }, { "parameters": { "rules": { "values": [ { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "leftValue": "={{ $('Prep - Telegram').item.json.text }}", "rightValue": "/start", "operator": { "type": "string", "operation": "equals" }, "id": "a26ffeca-a4f9-43ef-83c7-0c6c0adaa733" } ], "combinator": "and" }, "renameOutput": true, "outputKey": "start" }, { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "9f7c3717-89b2-4da6-8f9b-6893921eaea2", "leftValue": "={{ $('Prep - Telegram').item.json.text }}", "rightValue": "/clear", "operator": { "type": "string", "operation": "equals", "name": "filter.operator.equals" } } ], "combinator": "and" }, "renameOutput": true, "outputKey": "clear" }, { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "64c4d192-61dc-4886-b8e0-16628b3ecada", "leftValue": "={{ $('Prep - Telegram').item.json.text }}", "rightValue": "/help", "operator": { "type": "string", "operation": "equals", "name": "filter.operator.equals" } } ], "combinator": "and" }, "renameOutput": true, "outputKey": "help" }, { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "5ae6aa3e-2b59-4fd5-8688-abc31697bd5c", "leftValue": "={{ $('Prep - Telegram').item.json.text }}", "rightValue": "", "operator": { "type": "string", "operation": "notEmpty", "singleValue": true } } ], "combinator": "and" }, "renameOutput": true, "outputKey": "default" } ] }, "options": {} }, "type": "n8n-nodes-base.switch", "typeVersion": 3.2, "position": [ -1696, 1040 ], "id": "1dd96b17-8160-4c87-9b50-031d3aeadcc7", "name": "Check for global commands" }, { "parameters": { "chatId": "={{ $('Prep - Telegram').item.json.chat_id }}", "text": "=\ud83d\udc4b *Welcome back!* \n\nI found *{{ $json.urls.length }} LinkedIn URL(s)* already saved for you. \n\nYou can now: \n\u2022 `/prospect` \u2192 analyze and score them \n\u2022 `/watch` \u2192 start monitoring for changes \n\u2022 `/content` \u2192 get top posts & new ideas \n\nOr send new LinkedIn URLs to update your selection.", "additionalFields": { "appendAttribution": false } }, "type": "n8n-nodes-base.telegram", "typeVersion": 1.2, "position": [ -1248, 448 ], "id": "6dc20f46-b87c-4e70-b051-d80e9d5356c4", "name": "welcome-back", "credentials": { "telegramApi": { "name": "" } } }, { "parameters": { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "34043973-4b4f-4b67-9989-0dd5c3af105a", "leftValue": "={{ $json.urls.urls }}", "rightValue": "", "operator": { "type": "array", "operation": "notEmpty", "singleValue": true } } ], "combinator": "and" }, "options": {} }, "type": "n8n-nodes-base.if", "typeVersion": 2.2, "position": [ -1472, 448 ], "id": "2a9b258e-375c-469c-a085-21f821053396", "name": "User has set URLS" }, { "parameters": { "chatId": "={{ $('Prep - Telegram').item.json.chat_id }}", "text": "=Please send public LinkedIn company URL first, then /prospect.", "additionalFields": { "appendAttribution": false } }, "type": "n8n-nodes-base.telegram", "typeVersion": 1.2, "position": [ -1248, 1456 ], "id": "5931c1f8-00a1-44bc-9225-bf3f7b444d6d", "name": "No URLs set by user", "credentials": { "telegramApi": { "name": "" } } }, { "parameters": { "jsCode": "// Code \u00e0 utiliser dans un n\u0153ud Code n8n\nconst inputData = $('Has URLs Stored').all();\n\nconst result = inputData.map(item => {\n const data = item.json;\n \n // Transformer le tableau d'URLs en tableau d'objets\n const urlObjects = data.urls.map(url => ({\n url: url\n }));\n \n return {\n json: {\n urls: urlObjects\n }\n };\n});\n\nreturn result;" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -1024, 1216 ], "id": "1c0f5348-d845-400a-9504-dc36bfbafa69", "name": "Code" }, { "parameters": { "chatId": "={{ $('Prep - Telegram').item.json.chat_id }}", "text": "=\ud83d\udd0e Analyzing {{$json.urls.length}} LinkedIn URL(s)\u2026 I\u2019ll send results here.", "additionalFields": { "appendAttribution": false } }, "type": "n8n-nodes-base.telegram", "typeVersion": 1.2, "position": [ -1248, 1216 ], "id": "2a9db65c-526a-4931-b94f-29df84a29c1a", "name": "Send Analyzing", "credentials": { "telegramApi": { "name": "" } } }, { "parameters": { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "b935ae85-a3d7-4cfb-a545-bbc92bee3cb4", "leftValue": "={{ $json.urls }}", "rightValue": "", "operator": { "type": "array", "operation": "empty", "singleValue": true } } ], "combinator": "and" }, "options": {} }, "type": "n8n-nodes-base.if", "typeVersion": 2.2, "position": [ -2144, 1072 ], "id": "986828da-f58e-424a-bc7e-5ef0727baabd", "name": "If" }, { "parameters": { "operation": "select", "schema": { "__rl": true, "mode": "list", "value": "public" }, "table": { "__rl": true, "value": "linkedin_selection", "mode": "list", "cachedResultName": "linkedin_selection" }, "limit": 1, "where": { "values": [ { "column": "chat_id", "value": "={{ $json.chat_id }}" } ] }, "options": {} }, "type": "n8n-nodes-base.postgres", "typeVersion": 2.6, "position": [ -1920, 976 ], "id": "0f41e7e3-4de2-4938-8ec6-5b424c0b679e", "name": "Select rows from a table", "alwaysOutputData": true, "credentials": { "postgres": { "name": "" } }, "onError": "continueRegularOutput" }, { "parameters": { "workflowInputs": { "values": [ { "name": "snapshot_id" } ] } }, "type": "n8n-nodes-base.executeWorkflowTrigger", "typeVersion": 1.1, "position": [ -2816, 2160 ], "id": "956eadf4-cb21-4b8b-a294-89eafeb60e7e", "name": "BrightData Scraper Sub-Workflow" }, { "parameters": { "resource": "webScrapper", "operation": "triggerCollectionByUrl", "dataset_id": { "__rl": true, "value": "gd_l1vikfnt1wgvvqz95w", "mode": "list", "cachedResultName": "LinkedIn company information" }, "urls": "={{ $json.urls.toJsonString() }}", "requestOptions": {} }, "type": "@brightdata/n8n-nodes-brightdata.brightData", "typeVersion": 1, "position": [ -800, 1216 ], "id": "fc279645-5c1f-4651-b04c-0eab1e09c97a", "name": "Initiate batch extraction for Companies", "credentials": { "brightdataApi": { "name": "" } } }, { "parameters": { "workflowId": { "__rl": true, "value": "yNGc5OuaemnrXMaQ", "mode": "list", "cachedResultName": "LinkedIn Growth & Intelligence Agent" }, "workflowInputs": { "mappingMode": "defineBelow", "value": {}, "matchingColumns": [], "schema": [ { "id": "snapshot_id", "displayName": "snapshot_id", "required": false, "defaultMatch": false, "display": true, "canBeUsedToMatch": true, "type": "string", "removed": false } ], "attemptToConvertTypes": false, "convertFieldsToString": true }, "mode": "each", "options": { "waitForSubWorkflow": true } }, "type": "n8n-nodes-base.executeWorkflow", "typeVersion": 1.2, "position": [ -576, 1216 ], "id": "a20d7eba-47e5-4c40-90b1-c4f39d20df2d", "name": "Execute Bright Data Sub workflow Loop for Companies" }, { "parameters": { "options": { "reset": true } }, "type": "n8n-nodes-base.splitInBatches", "typeVersion": 3, "position": [ -2592, 2160 ], "id": "c02bb61b-82e4-4c23-bf1c-86f8231d8f17", "name": "Loop Over Items" }, { "parameters": { "resource": "webScrapper", "operation": "monitorProgressSnapshot", "snapshot_id": "={{ $json.snapshot_id }}", "requestOptions": {} }, "type": "@brightdata/n8n-nodes-brightdata.brightData", "typeVersion": 1, "position": [ -2368, 2096 ], "id": "a27175df-9ca6-43aa-9d28-d861818847b2", "name": "Check the status of a batch extraction", "credentials": { "brightdataApi": { "name": "" } } }, { "parameters": {}, "type": "n8n-nodes-base.wait", "typeVersion": 1.1, "position": [ -2144, 2096 ], "id": "cc2cfcee-f81e-4f15-83b1-9bcd53ed3883", "name": "Wait 5 seconds" }, { "parameters": { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "aaf6fec4-cbe7-4fa3-94c7-d169dcb83ac1", "leftValue": "={{ $('Check the status of a batch extraction').item.json.status }}", "rightValue": "ready", "operator": { "type": "string", "operation": "equals", "name": "filter.operator.equals" } } ], "combinator": "and" }, "options": {} }, "type": "n8n-nodes-base.if", "typeVersion": 2.2, "position": [ -1920, 2096 ], "id": "49277942-0704-4638-872d-683fb92639d5", "name": "Check if Batch ready" }, { "parameters": { "resource": "webScrapper", "operation": "downloadSnapshot", "snapshot_id": "={{ $('Loop Over Items').item.json.snapshot_id }}", "requestOptions": {} }, "type": "@brightdata/n8n-nodes-brightdata.brightData", "typeVersion": 1, "position": [ -1696, 2048 ], "id": "3e583d1f-1d7d-4685-b90b-2bfae047cdbc", "name": "Download the snapshot content", "credentials": { "brightdataApi": { "name": "" } } }, { "parameters": {}, "type": "n8n-nodes-base.noOp", "name": "Check Snapshot Again for Success", "typeVersion": 1, "position": [ -1696, 2256 ], "id": "d445bf9d-efed-4d25-b053-411f10185416" }, { "parameters": { "mode": "runOnceForEachItem", "jsCode": "const cleanInt = (s) =>\n s == null ? null : (Number(String(s).replace(/[^0-9]/g, \"\")) || 0);\n\nconst asStr = (v, max = 4000) =>\n (v == null ? \"\" : String(v)).slice(0, max);\n\nconst source = $json;\nconst p0 = source || {};\n\nconst followers = cleanInt(p0.followers);\nconst sizeRange = p0.company_size || p0.size_range || null;\n\n// Map des posts depuis \"updates\" Bright Data\nconst posts = Array.isArray(p0.updates)\n ? p0.updates.map(u => ({\n id: u.post_id ?? null,\n date: u.date ?? null,\n text: asStr(u.text, 1500),\n likes: cleanInt(u.likes_count),\n comments: cleanInt(u.comments_count),\n post_url: u.post_url ?? null\n }))\n : [];\n\nlet jobsOpenProxy = null;\nif (typeof p0.additional_information === \"string\") {\n const m =\n p0.additional_information.match(/(\\d+)\\s+open jobs/i) ||\n p0.additional_information.match(/\\((\\d+)\\s+open jobs\\)/i);\n jobsOpenProxy = m ? Number(m[1]) : null;\n}\n\nreturn {\n sourceUrl: p0.input?.url || p0.url,\n name: p0.name,\n linkedin_url: p0.url,\n industry: p0.industries || p0.specialties || null,\n size_range: sizeRange,\n employee_count_est: cleanInt(p0.employees_in_linkedin),\n followers,\n description: asStr(p0.description),\n last_posts: posts,\n jobs_open_proxy: jobsOpenProxy,\n executives: p0.employees || [],\n metrics: {},\n};\n" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 96, 896 ], "id": "912a7c77-1955-4799-8b7c-efd579a95f54", "name": "Normalizer" }, { "parameters": { "mode": "runOnceForEachItem", "jsCode": "const j = $json;\nconst followers = j.followers || 1;\nconst posts = Array.isArray(j.last_posts) ? j.last_posts : [];\n\nconst now = new Date();\nconst D30 = 1000 * 60 * 60 * 24 * 30;\n\nconst recentPosts = posts.filter(p => {\n if (!p?.date) return false;\n const t = Date.parse(p.date);\n return Number.isFinite(t) && (now - t) <= D30;\n});\n\n// fallback: si aucun post horodat\u00e9 dans 30j, on prend les 10 plus r\u00e9cents\nconst postsForER = recentPosts.length\n ? recentPosts\n : posts.slice(0, 10);\n\nconst avgER = postsForER.length\n ? postsForER.reduce((s, p) => {\n const l = Number(p.likes || 0);\n const c = Number(p.comments || 0);\n const sh = Number(p.shares || 0);\n return s + (l + c + sh) / followers;\n }, 0) / postsForER.length\n : 0;\n\n// Sans la liste des jobs dat\u00e9s 30j, on renvoie 0 par d\u00e9faut\n// (on conserve jobs_open_proxy pour d'autres features/affichage)\nconst hiring30 = 0;\n\nreturn {\n ...j,\n metrics: {\n ...j.metrics,\n avg_eng_rate_30d: avgER,\n jobs_last_30d: hiring30,\n },\n features: {\n // Fit taille: favorise 100\u20131000 employ\u00e9s si employee_count_est dispo\n size_fit: (() => {\n const x = j.employee_count_est || 0;\n if (!x) return 0.5;\n if (x >= 100 && x <= 1000) return 1;\n return Math.max(0.2, Math.exp(-Math.abs(x - 500) / 500));\n })(),\n growth_headcount: 0.5, // \u00e0 am\u00e9liorer avec historique\n hiring_volume: Math.min(1, (j.jobs_open_proxy || 0) / 20),\n icp_relevance: 0.6, // \u00e0 ajuster selon ton ICP/mots-cl\u00e9s\n engagement_activity: Math.min(1, (postsForER.length / 8) * 0.5 + avgER * 0.5),\n leadership_change: 0,\n risk_noise: (j.followers < 500 ? 0.6 : 0.2),\n }\n};\n" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 320, 896 ], "id": "04df0561-34de-4ea1-bb77-71caad7b994b", "name": "Metrics" }, { "parameters": { "promptType": "define", "text": "=You are a B2B sales intelligence assistant. \nAnalyze the company profile below and return a **strict JSON object**, matching the schema exactly. \nUse the data provided to generate a factual summary, identify post-related insights, and recommend sales actions. \nAdapt dynamically to the data (e.g., if no job postings, reflect that accordingly).\n\n---\n\nCompany data:\n- Name: {{$json.name}}\n- Industry: {{$json.industry}}\n- Size: {{$json.employee_count_est}} employees ({{$json.size_range}})\n- Followers: {{$json.followers}}\n- Jobs posted in last 30 days: {{$json.metrics.jobs_last_30d}}\n- Average engagement rate (30 days): {{$json.metrics.avg_eng_rate_30d}}\n- Description: {{$json.description}}\n- Recent posts: {{ JSON.stringify($json.last_posts.slice(0,3)) }}\n\n---\n\nSchema (valid JSON only, no markdown, no comments):\n\n{\n \"summary\": [\n \ud83e\udde0 *Industry*: {{$json.industry}} \n \ud83d\udc65 *Employees*: {{$json.employee_count_est}} (size range: {{$json.size_range}}) \n \ud83d\udce3 *Followers*: {{$json.followers}} \n \ud83d\udcca *Engagement Rate (30d)*: {{$json.metrics.avg_eng_rate_30d}}\n \ud83d\udcbc *Jobs (30 d)*: {{$json.metrics.jobs_last_30d}}\n ],\n \"post_insights\": [\n {\n \"key_themes\": [...],\n \"summary\": \"...\",\n \"potential_opportunity\": \"...\"\n }\n ],\n \"next_best_actions\": [\n {\n \"action\": \"...\",\n \"description\": \"...\"\n },\n {\n \"action\": \"...\",\n \"description\": \"...\"\n },\n {\n \"action\": \"...\",\n \"description\": \"...\"\n }\n ]\n}\n\n---\n\nInstructions:\n- Replace all placeholders with actual values from the input JSON.\n- In the `\"summary\"`, write one sentence about job activity:\n - If `jobs_last_30d > 0`: mention it as active hiring.\n - If `jobs_last_30d = 0`: mention absence of job postings and suggest possible internal focus.\n- In the `\"summary\"`, the value for `\"avg_eng_rate_30d\"` must be **rounded to 2 decimal places**.\n- `\"post_insights\"`: summarize themes in recent posts (e.g., product launches, geographic expansion, hiring signals, use cases).\n- `\"next_best_actions\"`: must be concrete and sales-relevant (LinkedIn outreach, email, engagement strategy, etc.).\n- Do not include any explanations, markdown, or formatting. Return JSON only.\n", "hasOutputParser": true, "options": { "systemMessage": "You are a B2B Sales Intelligence Assistant.\n\nYour mission is to analyze raw company profiles (provided as structured JSON input) and return **only a JSON object**, strictly following a predefined schema.\n\n---\n\n\ud83c\udfaf Core task:\n- Extract actionable insights useful for B2B sales or prospecting.\n- Adapt dynamically to the presence or absence of data (e.g., hiring activity, post frequency).\n- Always include: a factual summary, key insights from recent social posts, and 3 high-value sales actions.\n\n---\n\n\ud83e\udde0 Behavior Rules:\n- Never ask questions.\n- Never return markdown, explanations, HTML, or comments.\n- Output **JSON only**. No introductory or closing sentences.\n- If a data field is missing or null, adapt the language accordingly (e.g., \"no recent job postings\").\n- Keep tone professional, concise, and neutral.\n- Output should be ready for use in a CRM, sales dashboard, or automation tool.\n\n---\n\n\ud83d\udcca Output JSON Schema:\nYour output must follow this structure, every time:\n\n1. `\"summary\"`: factual, readable overview based on the input.\n2. `\"post_insights\"`: extract key themes and signals from the latest social posts (launches, growth, partnerships, topics).\n3. `\"next_best_actions\"`: 3 actionable recommendations for sales outreach, tailored to the company's context.\n\n---\n\n\ud83e\udde9 Input Structure:\nEach request will provide:\n- A reminder of your role\n- A structured list of company data fields (name, size, industry, etc.)\n- A JSON schema defining the expected output\n- Instructions for dynamic formatting (e.g., conditional language based on hiring activity)\n\n---\n\n\ud83d\udeab Strict Do-Nots:\n- Never output \u201cHere is the JSON\u201d or similar.\n- Never include free-form explanation.\n- Never include markdown formatting or non-JSON content.\n\n---\n\n\ud83c\udfaf Goal:\nBe ready to integrate into Make.com, Slack, Airtable, Zapier, or a GPT-based assistant. \nYour output must be 100% clean, reusable, and directly usable by a person or system.\n\n" } }, "type": "@n8n/n8n-nodes-langchain.agent", "typeVersion": 2.2, "position": [ 544, 800 ], "id": "3fa484ae-1743-4177-b8d0-20fe71165cc3", "name": "AI Agent" }, { "parameters": { "mode": "runOnceForEachItem", "jsCode": "// Scoring (n8n Function node)\n\n// 1) Pull canonical input from the Metrics node (has features + metrics)\nconst metricsNode = $('Metrics').first();\nif (!metricsNode) {\n // Hard fail-safe: no Metrics node found\n return {\n json: {\n error: \"Metrics node not found for scoring.\",\n score: 0,\n priority: \"C\"\n }\n };\n}\n\nconst canonical = metricsNode.json || {};\nconst features = canonical.features || {};\nconst nz = v => (v == null || v === '' ? 0 : Number(v));\n\nconst W = {\n size_fit: 0.18,\n growth_headcount: 0.22,\n hiring_volume: 0.15,\n icp_relevance: 0.20,\n engagement_activity: 0.15,\n leadership_change: 0.05,\n risk_noise: -0.05\n};\n\n// 2) Compute score 's' from features\nlet s = 0;\ns += W.size_fit * nz(features.size_fit);\ns += W.growth_headcount * nz(features.growth_headcount);\ns += W.hiring_volume * nz(features.hiring_volume);\ns += W.icp_relevance * nz(features.icp_relevance);\ns += W.engagement_activity * nz(features.engagement_activity);\ns += W.leadership_change * nz(features.leadership_change);\ns += W.risk_noise * nz(features.risk_noise);\n\n// 3) Normalize to 0..100, set priority\nconst score = Math.max(0, Math.min(100, Math.round(100 * s)));\nconst priority = score >= 75 ? 'A' : score >= 50 ? 'B' : 'C';\n\n// 4) Get current branch item (likely holds the AI Agent output)\nconst current = $json || {};\nconst aiOutput = current.output || current; // supports either {output:{...}} or the object itself\n\n// 5) Build a clean 'input' payload from Metrics (avoid echoing 'output' if present)\nconst { output: _drop, ...inputPayload } = canonical;\n\n// 6) Return final unified object\nreturn {\n json: {\n input: inputPayload, // original normalized data + metrics + features\n output: aiOutput, // AI Agent result\n score,\n priority\n }\n};" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 896, 896 ], "id": "75b19056-8a4f-4aa7-bd74-4c39c7b132f6", "name": "Scoring" }, { "parameters": { "jsCode": "function filterCompanyUrls(chatData) {\n // Check if input is valid and convert to array if needed\n if (!chatData) {\n console.log(\"No input data provided\");\n return [];\n }\n \n // Convert single object to array\n let dataArray = Array.isArray(chatData) ? chatData : [chatData];\n console.log(\"Processing data:\", dataArray);\n \n // Helper function to check if a URL is a company URL (more flexible patterns)\n function isCompanyUrl(url) {\n return url.includes('/company/');\n }\n \n // Helper function to clean company URLs (remove everything after company name)\n function cleanCompanyUrl(url) {\n if (!url.includes('/company/')) return url;\n \n // Find the company part and extract just the base company URL\n const companyMatch = url.match(/https?:\\/\\/[^\\/]+\\/company\\/[^\\/\\?#]+/);\n return companyMatch ? companyMatch[0] : url;\n }\n \n // Helper function to extract URLs from text\n function extractUrls(text) {\n if (typeof text !== 'string') return [];\n const urlRegex = /https?:\\/\\/[^\\s]+/g;\n const urls = text.match(urlRegex) || [];\n console.log(\"Found URLs:\", urls);\n const companyUrls = urls.filter(isCompanyUrl).map(cleanCompanyUrl);\n console.log(\"Cleaned company URLs:\", companyUrls);\n return companyUrls;\n }\n \n // Group data by chat_id and collect company URLs\n const result = {};\n \n dataArray.forEach(item => {\n const chatId = item.chat_id;\n const companyUrls = extractUrls(item.text);\n \n if (!result[chatId]) {\n result[chatId] = {\n chat_id: chatId,\n urls: []\n };\n }\n \n // Add company URLs to the existing array\n result[chatId].urls.push(...companyUrls);\n });\n \n // Convert result object to array\n return Object.values(result);\n}\n\nreturn filterCompanyUrls($input.first().json);\n" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -2368, 1072 ], "id": "fd4749a2-9ffd-4d82-b8fe-2b13c5cf6037", "name": "Extract URLs" }, { "parameters": { "schemaType": "manual", "inputSchema": "{\n \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n \"title\": \"Company Engagement Schema\",\n \"type\": \"object\",\n \"properties\": {\n \"summary\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n }\n },\n \"post_insights\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"key_themes\": {\n \"type\": \"array\",\n \"items\": { \"type\": \"string\" }\n },\n \"summary\": { \"type\": \"string\" },\n \"potential_opportunity\": { \"type\": \"string\" }\n },\n \"required\": [\"key_themes\", \"summary\", \"potential_opportunity\"]\n }\n },\n \"next_best_actions\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"action\": { \"type\": \"string\" },\n \"description\": { \"type\": \"string\" }\n },\n \"required\": [\"action\", \"description\"]\n }\n }\n },\n \"required\": [\"summary\", \"next_best_actions\"]\n}\n" }, "type": "@n8n/n8n-nodes-langchain.outputParserStructured", "typeVersion": 1.3, "position": [ 688, 1024 ], "id": "71069b27-216f-4bc8-8b58-200b42576bf4", "name": "Structured Output Parser" }, { "parameters": { "rules": { "values": [ { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "leftValue": "={{ $('Prep - Telegram').item.json.text }}", "rightValue": "/prospect", "operator": { "type": "string", "operation": "equals" }, "id": "8d84603b-6201-4553-b55b-e001c7a3fe9f" } ], "combinator": "and" }, "renameOutput": true, "outputKey": "prospect" }, { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "0bf8c52b-6e4d-4901-91a2-c8ad9e563124", "leftValue": "={{ $('Prep - Telegram').item.json.text }}", "rightValue": "/content", "operator": { "type": "string", "operation": "equals", "name": "filter.operator.equals" } } ], "combinator": "and" }, "renameOutput": true, "outputKey": "content" }, { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "1c5d74e3-1258-4afd-9e6c-563191165768", "leftValue": "={{ $('Prep - Telegram').item.json.text }}", "rightValue": "", "operator": { "type": "string", "operation": "notEmpty", "singleValue": true } } ], "combinator": "and" }, "renameOutput": true, "outputKey": "other" } ] }, "options": {} }, "type": "n8n-nodes-base.switch", "typeVersion": 3.2, "position": [ -352, 1200 ], "id": "b843904b-0ffe-4c1b-b898-532738411790", "name": "Switch" }, { "parameters": { "jsCode": "const cleanInt = s => s==null ? null : (Number(String(s).replace(/[^0-9]/g,'')) || 0);\nconst asStr = (v, max=1500) => (v==null?'':String(v)).slice(0,max);\n\nconst json = $input.first().json;\n\n// Followers & URL\nconst followers = cleanInt(json.followers) || 0;\nconst F = followers > 0 ? followers : 1;\n\nconst postsRaw = Array.isArray(json.updates) ? json.updates : [];\n\n// Normalizerr by post + ER fallback (likes+comments+shares)/max(1,followers)\nconst posts = postsRaw.map(u => {\n const likes = cleanInt(u.likes_count ?? u.likes) || 0;\n const comments = cleanInt(u.comments_count ?? u.comments) || 0;\n\n const date = u.date || null;\n const er = (likes + comments) / F;\n\n return {\n id: u.post_id ?? u.id ?? null,\n date,\n text: asStr(u.text ?? u.text_html?.replace(/<[^>]*>/g,' ') ?? ''),\n likes, comments,\n engagement_rate: er,\n post_url: u.post_url ?? u.url ?? null,\n title: u.title ?? null,\n };\n});\n\nreturn [{\n json: {\n company: json.name || json.title || 'Company',\n followers: followers || null,\n linkedin_url: json.url || json.input?.url || null,\n posts\n }\n}];\n" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -128, 1456 ], "id": "3e0bc147-15d7-4430-8f6a-3b3c112554b2", "name": "Normalize Posts" }, { "parameters": { "mode": "runOnceForEachItem", "jsCode": "const now = Date.now(), D30 = 1000*60*60*24*30;\nconst data = $json;\n\nconst parseTs = (s) => {\n const t = Date.parse(s || '');\n return Number.isFinite(t) ? t : null;\n};\n\n// filter last 30 days ; fallback = keep last 10\nconst recent = (data.posts || []).filter(p => {\n const t = parseTs(p.date);\n return t && (now - t) <= D30;\n});\n\nconst candidates = recent.length ? recent : (data.posts || []).slice(0, 10);\n\nfunction keywords(s){\n return (String(s||'').toLowerCase()\n .replace(/[^a-z0-9\\s]/g,' ')\n .split(/\\s+/)\n .filter(w => w.length >= 4)\n .slice(0,8));\n}\n\nconst used = new Set();\nconst top_posts = candidates\n .slice()\n .sort((a,b) => (b.engagement_rate||0) - (a.engagement_rate||0))\n .filter(p => {\n const key = (keywords(p.text).slice(0,2).join('|')) || 'misc';\n if (used.has(key)) return false;\n used.add(key);\n return true;\n })\n .slice(0,3);\n\nreturn { json: {\n ...data,\n recent_window_days: 30,\n top_posts,\n has_posts: !!top_posts.length\n}};\n" }, "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 96, 1456 ], "id": "2e3b533d-1baa-4fdd-bcd5-e073d8346ed1", "name": "Rank Posts" }, { "parameters": { "promptType": "define", "text": "=Company: {{$json.company}}\nFollowers: {{$json.followers}}\nWindow (days): {{$json.recent_window_days}}\n\nTopPosts (JSON):\n{{ JSON.stringify($json.top_posts) }}\n\nTask:\n1. Extract 3\u20135 short insights about what performs in these posts (themes, formats, angles).\n2. Generate 3\u20135 LinkedIn post ideas inspired by those posts (no copy-paste).\n3. Each idea must include: title, hook, angle, 3 bullets, CTA.\n4. Return valid JSON only, matching this structure:\n\n\nAdd company, recent_window_days and top_posts on output\nIf you cannot extract insights from the posts, write: [\"No insights available.\"]\nIf you cannot generate post ideas, write 3 ideas based on company\u2019s context alone.\n\n", "hasOutputParser": true, "options": { "systemMessage": "You are a B2B LinkedIn content strategist.\n\nYour role:\n- Read the company profile and its top-performing LinkedIn posts.\n- Extract 3\u20135 key content insights.\n- Generate 3\u20135 new LinkedIn post ideas based on what worked, without copying.\n\nEach idea includes:\n- title\n- hook (max 120 chars)\n- angle\n- 3 short bullets\n- CTA (call to action)\n\nTone: expert, concise, realistic for LinkedIn.\n\nIf input is limited, still generate 3 ideas using only company info.\n\n\u26a0\ufe0f Return JSON only (no markdown, no prose). \n" } }, "type": "@n8n/n8n-nodes-langchain.agent", "typeVersion": 2.2, "position": [ 544, 1200 ], "id": "dba6068a-fc8f-43fb-b000-f2471e1b8098", "name": "AI Ideas Content" }, { "parameters": { "chatId": "={{ $('Prep - Telegram').item.json.chat_id }}", "text": "=\u2139\ufe0f No recent public posts found.\nTry widening the window or adding more sources. You can still use /prospect for scoring.\n", "additionalFields": { "appendAttribution": false } }, "type": "n8n-nodes-base.telegram", "typeVersion": 1.2, "position": [ 608, 1600 ], "id": "e104f8a7-35d8-4eb0-84a5-457cfd6371a1", "name": "Fallback", "credentials": { "telegramApi": { "name": "" } } }, { "parameters": { "schemaType": "manual", "inputSchema": "{\n \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n \"title\": \"Content Generation Output\",\n \"type\": \"object\",\n \"required\": [\"input\", \"insights\", \"post_ideas\"],\n \"properties\": {\n \"input\": {\n \"type\": \"object\",\n \"required\": [\"company\", \"followers\", \"recent_window_days\", \"top_posts\"],\n \"properties\": {\n \"company\": { \"type\": \"string\" },\n \"followers\": { \"type\": \"number\" },\n \"recent_window_days\": { \"type\": \"number\" },\n \"top_posts\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"required\": [\"id\", \"date\", \"text\", \"likes\", \"comments\", \"engagement_rate\", \"post_url\", \"title\"],\n \"properties\": {\n \"id\": { \"type\": \"string\" },\n \"date\": { \"type\": \"string\", \"format\": \"date-time\" },\n \"text\": { \"type\": \"string\" },\n \"likes\": { \"type\": \"number\" },\n \"comments\": { \"type\": \"number\" },\n \"engagement_rate\": { \"type\": \"number\" },\n \"post_url\": { \"type\": \"string\", \"format\": \"uri\" },\n \"title\": { \"type\": \"string\" }\n }\n }\n }\n }\n },\n \"insights\": {\n \"type\": \"array\",\n \"minItems\": 1,\n \"items\": { \"type\": \"string\" }\n },\n \"post_ideas\": {\n \"type\": \"array\",\n \"minItems\": 1,\n \"items\": {\n \"type\": \"object\",\n \"required\": [\"title\", \"hook\", \"angle\", \"bullets\", \"cta\"],\n \"properties\": {\n \"title\": { \"type\": \"string\" },\n \"hook\": { \"type\": \"string\", \"maxLength\": 120 },\n \"angle\": { \"type\": \"string\" },\n \"bullets\": {\n \"type\": \"array\",\n \"minItems\": 3,\n \"maxItems\": 3,\n \"items\": { \"type\": \"string\" }\n },\n \"cta\": { \"type\": \"string\" }\n }\n }\n }\n }\n}\n" }, "type": "@n8n/n8n-nodes-langchain.outputParserStructured", "typeVersion": 1.3, "position": [ 688, 1424 ], "id": "2c41a141-dd96-4958-ac87-01ae8d08664a", "name": "Structured Output Parser1" }, { "parameters": { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "ecacd325-3734-4558-bc7f-5ba8c27a2b80", "leftValue": "={{ $json.has_posts }}", "rightValue": "", "operator": { "type": "boolean", "operation": "true", "singleValue": true } } ], "combinator": "and" }, "options": {} }, "type": "n8n-nodes-base.if", "typeVersion": 2.2, "position": [ 320, 1456 ], "id": "65bdbe0b-4283-40a8-b0c2-055b789aaa8a", "name": "HasPost" }, { "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4.1-mini" }, "options": {} }, "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "typeVersion": 1.2, "position": [ 560, 1024 ], "id": "372746e3-8b6e-4ed2-9ca3-26381a204568", "name": "OpenAI Chat Prospect", "credentials": { "openAiApi": { "name": "" } } }, { "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4.1-mini" }, "options": {} }, "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "typeVersion": 1.2, "position": [ 560, 1424 ], "id": "c7470e38-fafc-4ff2-bd45-0631150f0be5", "name": "OpenAI Chat Content", "credentials": { "openAiApi": { "name": "" } } }, { "parameters": { "updates": [ "message" ], "additionalFields": {} }, "type": "n8n-nodes-base.telegramTrigger", "typeVersion": 1.2, "position": [ -2816, 1072 ], "id": "913c6534-76aa-42d4-b24c-c506c3b19da8", "name": "Telegram Trigger", "credentials": { "telegramApi": { "name": "" } } }, { "parameters": { "chatId": "={{ $('Prep - Telegram').item.json.chat_id }}", "text": "=\ud83d\udca1 *Content Digest \u2013 {{ $json.output.input.company }}*\n\n\ud83d\uddd3\ufe0f *Top Posts (last {{$json.output.input.recent_window_days}} days)*:\n{{ $json.output.input.top_posts.map((item) => {\n return `\u2022 ${item.date.slice(0,10)} ${item.text.slice(0,60)} \u2014 ER ${Number(item.engagement_rate).toFixed(3)}`\n}).join('\\n')}}\n\n\ud83d\udccc *Insights*:\n{{ $json.output.insights.map((item) => (`\u2022 ${item}`)).join('\\n')}}\n\n\n\ud83e\udde0 *Post Ideas*:\n{{ $json.output.post_ideas.map((item, index) => {\n return `${index + 1} *${item.title}*\n _Hook_: ${item.hook}\n _Angle_: ${item.angle}\n _Structure_:\n${item.bullets.map((item) => (` \u2022 ${item}`)).join('\\n')}\n _CTA_: ${item.cta}\n`\n}).join('\\n')}}\n\n", "additionalFields": { "appendAttribution": false } }, "type": "n8n-nodes-base.telegram", "typeVersion": 1.2, "position": [ 896, 1296 ], "id": "251a0199-416a-4c9d-85c4-9108ae744b89", "name": "Send Content message", "credentials": { "telegramApi": { "name": "" } } }, { "parameters": { "chatId": "={{ $('Prep - Telegram').item.json.chat_id }}", "text": "=\ud83d\udccc *Company Profile Summary \u2013 {{ $('Normalizer').item.json.name }}*\n\n\u26a0\ufe0f *Priority Score*: *{{$json.priority}}*\n---\n\n{{ $json.output.summary.join('\\n') }}\n---\n\n\ud83c\udfaf *Recommended Next Actions*\n\n1\ufe0f\u20e3 *{{ $json.output.next_best_actions[0].action }}*\n{{ $json.output.next_best_actions[0].description }}\n\n2\ufe0f\u20e3 *{{ $json.output.next_best_actions[1].action }}*\n{{ $json.output.next_best_actions[1].description }}\n\n3\ufe0f\u20e3 *{{ $json.output.next_best_actions[2].action }}*\n{{ $json.output.next_best_actions[2].description }}\n\n\n", "additionalFields": { "appendAttribution": false } }, "type": "n8n-nodes-base.telegram", "typeVersion": 1.2, "position": [ 1120, 896 ], "id": "2ee76108-e2d6-485c-bf5e-5122e537f958", "name": "Send Prospect message", "credentials": { "telegramApi": { "name": "" } } }, { "parameters": { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 2 }, "conditions": [ { "id": "34043973-4b4f-4b67-9989-0dd5c3af105a", "leftValue": "={{ $json.urls }}", "rightValue": "", "operator": { "type": "array", "operation": "notEmpty", "singleValue": true } } ], "combinator": "and" }, "options": {} }, "type": "n8n-nodes-base.if", "typeVersion": 2.2, "position": [ -1472, 1360 ], "id": "043366fb-5e2e-4a05-81a9-837b19c2f75a", "name": "Has URLs Stored" }, { "parameters": { "operation": "executeQuery", "query": "DROP TABLE IF EXISTS linkedin_selection;\nCREATE TABLE IF NOT EXISTS linkedin_selection (\n chat_id TEXT PRIMARY KEY,\n urls text[],\n updated_at TIMESTAMPTZ DEFAULT now()\n);", "options": {} }, "type": "n8n-nodes-base.postgres", "typeVersion": 2.6, "position": [ -2432, 1632 ], "id": "d0549907-1157-4a5c-ba75-acf38c710b5d", "name": "Initialize database tables", "credentials": { "postgres": { "name": "" } } }, { "parameters": { "content": "## \ud83c\udfaf Purpose: Initialize database tables for the Growth Radar bot\n\n- Creates table `linkedin_selection` if it doesn\u2019t exist\n- Stores mapping: { chat_id, urls, last_updated }\n- Ensures session persistence across Telegram messages\n- Run once at workflow start (safe to re-run, idempotent)\n\n\u26a0\ufe0f Important: Only stores *public LinkedIn company URLs*", "height": 256, "width": 672, "color": 3 }, "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -2928, 1536 ], "id": "72139119-3bfb-470a-8a25-904e5972b7ea", "name": "Sticky Note" }, { "parameters": { "content": "## \ud83d\udd04 Purpose: Wait until Bright Data snapshot is ready\n\n- Polls snapshot status every 5s\n- If status != \"ready\" \u2192 loop continues\n- Once ready \u2192 downloads snapshot JSON\n- Ensures we always get complete & valid data\n\n\ud83d\udca1 Could be extended with exponential backoff", "height": 528, "width": 1472, "color": 5 }, "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -2944, 1952 ], "id": "39f64c89-8e7c-41b1-b2ee-730474006db3", "name": "Sticky Note1" }, { "parameters": { "content": "## \ud83e\udd16 Purpose: Transform Bright Data results into actionable insights\n\n- Uses Structured Output Parser with enforced JSON schema:\n {\n \"summary\": [string],\n \"post_insights\": [ { \"text\": string, \"engagement_rate\": number } ],\n \"next_best_actions\": [string]\n }\n\n- Ensures LLM always returns machine-parseable JSON\n- Adds robustness: downstream nodes (Scoring, Telegram Output) consume clean data\n\n\u26a0\ufe0f Fallback handling: if schema fails, default empty arrays are used", "height": 2160, "width": 4336 }, "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -2976, 352 ], "id": "54d807ac-0832-4e0b-af75-2bea2af17f31", "name": "Sticky Note2" } ], "connections": { "Prep - Telegram": { "main": [ [ { "node": "Extract URLs", "type": "main", "index": 0 } ] ] }, "Upsert LinkedIn URLs": { "main": [ [ { "node": "Check for global commands", "type": "main", "index": 0 } ] ] }, "Delete stored chat": { "main": [ [ { "node": "/clear", "type": "main", "index": 0 } ] ] }, "Check for global commands": { "main": [ [ { "node": "User has set URLS", "type": "main", "index": 0 } ], [ { "node": "Delete stored chat", "type": "main", "index": 0 } ], [ { "node": "/help", "type": "main", "index": 0 } ], [ { "node": "Has URLs Stored", "type": "main", "index": 0 } ] ] }, "User has set URLS": { "main": [ [ { "node": "welcome-back", "type": "main", "index": 0 } ], [ { "node": "/start", "type": "main", "index": 0 } ] ] }, "/help": { "main": [ [] ] }, "Code": { "main": [ [ { "node": "Initiate batch extraction for Companies", "type": "main", "index": 0 } ] ] }, "Send Analyzing": { "main": [ [ { "node": "Code", "type": "main", "index": 0 } ] ] }, "If": { "main": [ [ { "node": "Select rows from a table", "type": "main", "index": 0 } ], [ { "node": "Upsert LinkedIn URLs", "type": "main", "index": 0 } ] ] }, "Select rows from a table": { "main": [ [ { "node": "Check for global commands", "type": "main", "index": 0 } ] ] }, "BrightData Scraper Sub-Workflow": { "main": [ [ { "node": "Loop Over Items", "type": "main", "index": 0 } ] ] }, "Initiate batch extraction for Companies": { "main": [ [ { "node": "Execute Bright Data Sub workflow Loop for Companies", "type": "main", "index": 0 } ] ] }, "Execute Bright Data Sub workflow Loop for Companies": { "main": [ [ { "node": "Switch", "type": "main", "index": 0 } ] ] }, "Loop Over Items": { "main": [ [], [ { "node": "Check the status of a batch extraction", "type": "main", "index": 0 } ] ] }, "Check the status of a batch extraction": { "main": [ [ { "node": "Wait 5 seconds", "type": "main", "index": 0 } ] ] }, "Wait 5 seconds": { "main": [ [ { "node": "Check if Batch ready", "type": "main", "index": 0 } ] ] }, "Check if Batch ready": { "main": [ [ { "node": "Download the snapshot content", "type": "main", "index": 0 } ], [ { "node": "Check Snapshot Again for Success", "type": "main", "index": 0 } ] ] }, "Check Snapshot Again for Success": { "main": [ [ { "node": "Loop Over Items", "type": "main", "index": 0 } ] ] }, "Normalizer": { "main": [ [ { "node": "Metrics", "type": "main", "index": 0 } ] ] }, "Metrics": { "main": [ [ { "node": "AI Agent", "type": "main", "index": 0 } ] ] }, "AI Agent": { "main": [ [ { "node": "Scoring", "type": "main", "index": 0 } ] ] }, "Scoring": { "main": [ [ { "node": "Send Prospect message", "type": "main", "index": 0 } ] ] }, "Extract URLs": { "main": [ [ { "node": "If", "type": "main", "index": 0 } ] ] }, "Structured Output Parser": { "ai_outputParser": [ [ { "node": "AI Agent", "type": "ai_outputParser", "index": 0 } ] ] }, "Switch": { "main": [ [ { "node": "Normalizer", "type": "main", "index": 0 } ], [ { "node": "Normalize Posts", "type": "main", "index": 0 } ], [ { "node": "Normalizer", "type": "main", "index": 0 } ] ] }, "Normalize Posts": { "main": [ [ { "node": "Rank Posts", "type": "main", "index": 0 } ] ] }, "Rank Posts": { "main": [ [ { "node": "HasPost", "type": "main", "index": 0 } ] ] }, "AI Ideas Content": { "main": [ [ { "node": "Send Content message", "type": "main", "index": 0 } ] ] }, "Structured Output Parser1": { "ai_outputParser": [ [ { "node": "AI Ideas Content", "type": "ai_outputParser", "index": 0 } ] ] }, "HasPost": { "main": [ [ { "node": "AI Ideas Content", "type": "main", "index": 0 } ], [ { "node": "Fallback", "type": "main", "index": 0 } ] ] }, "OpenAI Chat Prospect": { "ai_languageModel": [ [ { "node": "AI Agent", "type": "ai_languageModel", "index": 0 } ] ] }, "OpenAI Chat Content": { "ai_languageModel": [ [ { "node": "AI Ideas Content", "type": "ai_languageModel", "index": 0 } ] ] }, "Telegram Trigger": { "main": [ [ { "node": "Prep - Telegram", "type": "main", "index": 0 } ] ] }, "Has URLs Stored": { "main": [ [ { "node": "Send Analyzing", "type": "main", "index": 0 } ], [ { "node": "No URLs set by user", "type": "main", "index": 0 } ] ] }, "Initialize database tables": { "main": [ [] ] } }, "active": true, "settings": { "executionOrder": "v1", "callerPolicy": "workflowsFromSameOwner", "errorWorkflow": "tl6MZUA9SY1FvR1b" }, "versionId": "e651fd1c-6fde-4aea-b9d3-704e95ca2754", "meta": { "templateCredsSetupCompleted": true }, "id": "yNGc5OuaemnrXMaQ", "tags": [ { "createdAt": "2025-08-20T14:43:39.486Z", "updatedAt": "2025-08-20T14:43:39.486Z", "id": "gdSMwmdgI9LAuEUK", "name": "Challenge" }, { "createdAt": "2025-08-20T14:42:15.849Z", "updatedAt": "2025-08-20T14:42:15.849Z", "id": "lJrhCYT58pnNO3s6", "name": "n8n" }, { "createdAt": "2025-08-03T20:18:57.239Z", "updatedAt": "2025-08-03T20:18:57.239Z", "id": "pSYRZNvJjRmhwDfL", "name": "LinkedIn" }, { "createdAt": "2025-08-20T14:43:14.428Z", "updatedAt": "2025-08-20T14:43:14.428Z", "id": "qZNchUGmNPT9hMjL", "name": "Bright Data" } ] }