This workflow follows the Agent → Anthropic Chat recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
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{
"updatedAt": "2026-06-03T10:11:59.136Z",
"createdAt": "2026-05-25T13:51:11.721Z",
"id": "olgL8wb7nrJumkct",
"name": "Jobby - LinkedIn 2 Notion",
"description": null,
"active": true,
"isArchived": false,
"nodes": [
{
"parameters": {
"promptType": "define",
"text": "=Company: {{ $('CV Factory').item.json.query.company }}\njon title : {{ $('CV Factory').item.json.query.job_title }}\njob Url: {{ $('CV Factory').item.json.query.job_url }}\nDescription: {{ $('CV Factory').item.json.query.job_description }}\n",
"options": {
"systemMessage": "=# Tu es Jobby 3.0\nL'assistant strat\u00e9gique de recherche d'imploi pour Julien. \n\n## Identity & Tone\nTon ton est neutre, analytique et orient\u00e9 r\u00e9sultats, comme un coach en recrutement senior. Ta valeur ajout\u00e9e r\u00e9side dans la d\u00e9tection pr\u00e9cise des \u00e9carts techniques et organisationnels.\n\n## Purpose\n\u00c9valuer l'ad\u00e9quation entre le profil de Julien (Solutions Engineer, expert automation/IA) et une offre d'emploi sp\u00e9cifique, puis g\u00e9n\u00e9rer le CV Markdown optimal pour maximiser le score ATS.\n\n## Context & Data\nPour r\u00e9aliser cette analyse et cette g\u00e9n\u00e9ration, voici les donn\u00e9es consolid\u00e9es issues du syst\u00e8me :\n\n## 1. Fiche de Poste Aspir\u00e9e (LinkedIn)\n{{ $json.job_description }}\n\n## 2. Directives de Positionnement (La Graine)\n- Baseline attendue : {{ $json.seed_baseline }}\n- Consignes de filtrage : {{ $json.seed_directives }}\n\n## 3. Graphe de Comp\u00e9tences & Exp\u00e9riences (CV Atomique)\n{{ $json.cv_master_payload }}\n\n# Evaluation Criteria\n1. Score (1-10) : Bas\u00e9 strictement sur l'ad\u00e9quation technique, l'exp\u00e9rience pass\u00e9e et le niveau de s\u00e9niorit\u00e9 requis.\n2. Gap Analysis : Identifier les technologies ou m\u00e9thodologies manquantes sans fioritures.\n3. Reskilling & Tailoring : S\u00e9lectionner uniquement les 4 derni\u00e8res activit\u00e9s du CV Atomique et adapter subtilement les puces d'impact en utilisant les mots-cl\u00e9s de la fiche de poste et l'orientation de la Graine.\n\n**CRITICAL NOTION LIMIT**: Le texte dans `\"generated_cv_markdown\"` doit \u00eatre concis et synth\u00e9tique. Chaque section `(Summary, Exp\u00e9rience, etc.)`` doit \u00eatre s\u00e9par\u00e9e par le caract\u00e8re \"|\" pour nous permettre de d\u00e9couper le texte apr\u00e8s coup et \u00e9viter de d\u00e9passer la limite de 2000 caract\u00e8res par bloc impos\u00e9e par l'API Notion.\n\n# Output Format (STRICT JSON)\nR\u00e9ponds exclusivement par un objet JSON valide. Le CV au format Markdown et en anglais doit \u00eatre enti\u00e8rement encapsul\u00e9 dans la cl\u00e9 \"generated_cv_markdown\". Ne mets pas de balises de code (comme `markdown`) \u00e0 l'int\u00e9rieur du JSON.\n\n```{\n \"domain\": \"string (ex: Generative AI, Automation, API Management)\",\n \"job_title\": \"string (Titre exact)\",\n \"company\": \"string\",\n \"match_score\": number,\n \"quick_summary\": \"string (MAX 150 caract\u00e8res)\",\n \"analysis_pros\": \"string (Points forts techniques et m\u00e9tier - Max 800 caract\u00e8res)\",\n \"analysis_cons\": \"string (Points de friction et manques - Max 800 caract\u00e8res)\",\n \"action_plan\": \"string (Conseil imm\u00e9diat pour la candidature - Max 400 caract\u00e8res)\",\n \"generated_cv_markdown\": \"string (Le CV V2.0 complet en Markdown, structur\u00e9 avec l'Aside de d\u00e9part contenant la baseline, puis les 4 derniers jobs adapt\u00e9s aux crit\u00e8res ATS)\"\n}"
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3.1,
"position": [
352,
48
],
"id": "b28e3fb0-c3bf-42f4-9a63-a95c5c8d4055",
"name": "AI Agent",
"onError": "continueErrorOutput"
},
{
"parameters": {
"modelName": "models/gemini-2.5-flash-lite",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"typeVersion": 1.1,
"position": [
352,
320
],
"id": "c09c542b-80ca-4d47-a542-15fc14eabccd",
"name": "Google Gemini Chat Model",
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "claude-sonnet-4-6",
"cachedResultName": "Claude Sonnet 4.6"
},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatAnthropic",
"typeVersion": 1.4,
"position": [
-544,
384
],
"id": "29dd6f16-98a6-48f5-ad0b-7a76b5eb335a",
"name": "Anthropic Chat Model",
"credentials": {
"anthropicApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"jsCode": "// 1. On r\u00e9cup\u00e8re le texte g\u00e9n\u00e9r\u00e9 par l'Agent\nconst text = $json.output || \"\"; \nconst match = text.match(/\\{[\\s\\S]*\\}/);\n\nif (match) {\n try {\n // On parse uniquement le bloc JSON trouv\u00e9\n const data = JSON.parse(match);\n\n // Helper pour les tableaux Notion (Multi-select)\n const toSimpleArray = (val) => {\n if (!val) return [];\n const rawItems = Array.isArray(val) ? val : val.toString().split(/[|,\\/&]|\\band\\b/);\n return rawItems.map(i => i.trim()).filter(i => i.length > 1);\n };\n\n // 2. On reconstruit l'objet propre pour alimenter le n\u0153ud \"Update Notion\"\n return [{\n json: {\n domain: toSimpleArray(data.domain),\n category: toSimpleArray(data.category || data.job_title),\n job_title: data.job_title || \"Position inconnue\",\n company: data.company || \"Entreprise inconnue\",\n score: parseInt(data.match_score) || 0,\n status: \"To review\",\n summary: data.quick_summary || \"Analyse termin\u00e9e\",\n analysis_pros: data.analysis_pros || \"N/A\",\n analysis_cons: data.analysis_cons || \"N/A\",\n action_plan: data.action_plan || \"N/A\",\n generated_cv_markdown: data.generated_cv_markdown || \"\",\n response_date: new Date().toISOString().split('T')\n }\n }];\n\n } catch (e) {\n return [{ json: { error: \"JSON Parsing Error\", message: e.message, raw: text.substring(0, 200) } }];\n }\n}\n\nreturn [{ json: { error: \"No JSON found in AI output\", raw: text.substring(0, 100) } }];"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
704,
-48
],
"id": "bc6d9e53-7c47-4b19-97f4-e78868ea7cdd",
"name": "JSON Cleaner",
"onError": "continueErrorOutput"
},
{
"parameters": {
"resource": "database",
"databaseId": {
"__rl": true,
"value": "5ea1407d-50b4-4527-bd86-21f3c58535fa",
"mode": "list",
"cachedResultName": "Complete Resume DB",
"cachedResultUrl": "https://www.notion.so/5ea1407d50b44527bd8621f3c58535fa"
}
},
"type": "n8n-nodes-base.notion",
"typeVersion": 2.2,
"position": [
-320,
48
],
"id": "98b735c7-3087-4ff3-83a7-c749cba35b8b",
"name": "Get CV data",
"credentials": {
"notionApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"resource": "database",
"databaseId": {
"__rl": true,
"value": "642d04c9-1c1f-4c01-a4d3-89822f02556b",
"mode": "list",
"cachedResultName": "CV Seeds",
"cachedResultUrl": "https://www.notion.so/642d04c91c1f4c01a4d389822f02556b"
}
},
"type": "n8n-nodes-base.notion",
"typeVersion": 2.2,
"position": [
-96,
48
],
"id": "1c3e6440-cbf2-472c-ab63-959a496a7e6f",
"name": "CV Seed",
"credentials": {
"notionApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"resource": "databasePage",
"databaseId": {
"__rl": true,
"value": "127bad1f-b25a-4b6b-8eec-7b342e3aa504",
"mode": "list",
"cachedResultName": "Linked Detector",
"cachedResultUrl": "https://www.notion.so/127bad1fb25a4b6b8eec7b342e3aa504"
},
"title": "LinkedIn Detector",
"simple": false,
"propertiesUi": {
"propertyValues": [
{
"key": "Job Title|title",
"title": "={{ $json.job_title || \"Nouvelle offre LinkedIn (Titre non extrait)\" }}\n"
},
{
"key": "category|multi_select",
"multiSelectValue": "={{ $json.category }}"
},
{
"key": "URL|url",
"urlValue": "={{ $('CV Factory').item.json.query.job_url }}"
},
{
"key": "Match Score|number",
"numberValue": "={{ $json.score }}"
},
{
"key": "Status|status",
"statusValue": "={{ $json.status }}"
},
{
"key": "Company|select",
"selectValue": "={{ $json.company }}"
},
{
"key": "domain|multi_select",
"multiSelectValue": "={{ $json.domain }}S"
},
{
"key": "analysis|rich_text",
"textContent": "={{ $json.summary }}"
}
]
},
"blockUi": {
"blockValues": [
{
"type": "heading_1",
"textContent": "Analyse compl\u00e8te"
},
{
"textContent": "={{ $json.summary }}"
},
{
"type": "heading_2",
"textContent": "Pros"
},
{
"textContent": "={{ $json.analysis_pros }}"
},
{
"type": "heading_2",
"textContent": "Cons"
},
{
"textContent": "={{ $json.analysis_cons }}"
},
{
"type": "heading_2",
"textContent": "Action Plan"
},
{
"textContent": "={{ $json.action_plan }}"
},
{
"type": "heading_1",
"textContent": "Raw job description "
},
{
"type": "toggle",
"textContent": "=About the job"
},
{
"textContent": "={{ $('CV Factory').item.json.query.job_description }}"
}
]
},
"options": {
"iconType": "emoji",
"icon": "\ud83c\udd95"
}
},
"type": "n8n-nodes-base.notion",
"typeVersion": 2.2,
"position": [
928,
-48
],
"id": "a801be3c-190f-4c32-a564-050e9ba194f2",
"name": "update prospect",
"retryOnFail": false,
"executeOnce": false,
"credentials": {
"notionApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"path": "cv-factory",
"authentication": "headerAuth",
"options": {
"responseData": "{\"status\": \"processing\"}"
}
},
"type": "n8n-nodes-base.webhook",
"typeVersion": 2.1,
"position": [
-544,
48
],
"id": "4f70d8ad-1003-4506-b630-04d85c94d2e1",
"name": "CV Factory",
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"jsCode": "// 1. R\u00e9cup\u00e9ration des lignes du CV Atomique\nlet cvRows = [];\ntry {\n cvRows = $('Get CV data').all();\n} catch (e) {\n cvRows = [];\n}\n\n// Extraction universelle pour le CV\nconst cleanExperiences = cvRows.map((row, index) => {\n const props = row.json?.properties || {};\n \n // Fonction helper pour extraire le texte brut d'une propri\u00e9t\u00e9 Notion, quel que soit son type (title ou rich_text)\n const extractText = (propObj) => {\n if (!propObj) return \"\";\n const arr = propObj.rich_text || propObj.title || [];\n return arr?.plain_text || propObj.value || \"\";\n };\n\n // On scanne dynamiquement les variantes de noms de colonnes possibles\n const entreprise = extractText(props.Entreprise) || extractText(props.Company) || \"Entreprise \" + (index + 1);\n const poste = extractText(props.Poste) || extractText(props.Role) || \"Solutions Engineer\";\n const description = extractText(props.Description) || \"\";\n const dates = extractText(props.Dates) || extractText(props.Date) || \"\";\n\n return `### ${poste} chez ${entreprise} (${dates})\\n${description}`;\n}).join(\"\\n\\n\");\n\n// 2. R\u00e9cup\u00e9ration et isolation de la Graine UNIQUE\nlet seedBaseline = \"Solutions Engineer\";\nlet seedDirectives = \"Standard tailoring\";\n\ntry {\n const allSeeds = $('CV Seed').all();\n // On cherche la graine active via la checkbox, sinon on prend la premi\u00e8re disponible\n const selectedSeedItem = allSeeds.find(item => {\n const props = item.json?.properties || {};\n // G\u00e8re la checkbox Notion (qu'elle s'appelle Active ou active)\n const checkObj = props.Active || props.active;\n return checkObj?.checkbox === true;\n }) || allSeeds;\n\n if (selectedSeedItem && selectedSeedItem.json?.properties) {\n const sProps = selectedSeedItem.json.properties;\n \n const extractSeedText = (propObj) => {\n if (!propObj) return null;\n const arr = propObj.title || propObj.rich_text || [];\n return arr?.plain_text || null;\n };\n\n seedBaseline = extractSeedText(sProps.Baseline) || extractSeedText(sProps.baseline) || seedBaseline;\n seedDirectives = extractSeedText(sProps.Directives) || extractSeedText(sProps.directives) || seedDirectives;\n }\n} catch (e) {\n // En cas de probl\u00e8me de lecture de la graine, on garde les valeurs par d\u00e9faut pour ne pas bloquer le flux\n}\n\n// 3. Payload plat final garanti sans crash\nreturn [{\n json: {\n job_description: $json.job_description || \"Pas de description\",\n seed_baseline: seedBaseline,\n seed_directives: seedDirectives,\n cv_master_payload: cleanExperiences || \"CV Master vide\"\n }\n}];"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
128,
48
],
"id": "6fc7730a-c6d3-48db-919d-c700c1c6bed1",
"name": "Clean CV"
},
{
"parameters": {
"errorMessage": "={{ $('AI Agent').item.json.error }}"
},
"type": "n8n-nodes-base.stopAndError",
"typeVersion": 1,
"position": [
704,
144
],
"id": "0334fb31-bc67-410f-89bc-7e0de9ccaa43",
"name": "Stop and Error"
}
],
"connections": {
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"AI Agent": {
"main": [
[
{
"node": "JSON Cleaner",
"type": "main",
"index": 0
}
],
[
{
"node": "Stop and Error",
"type": "main",
"index": 0
}
]
]
},
"Anthropic Chat Model": {
"ai_languageModel": [
[]
]
},
"JSON Cleaner": {
"main": [
[
{
"node": "update prospect",
"type": "main",
"index": 0
}
],
[]
]
},
"Get CV data": {
"main": [
[
{
"node": "CV Seed",
"type": "main",
"index": 0
}
]
]
},
"CV Seed": {
"main": [
[
{
"node": "Clean CV",
"type": "main",
"index": 0
}
]
]
},
"update prospect": {
"main": [
[],
[]
]
},
"CV Factory": {
"main": [
[
{
"node": "Get CV data",
"type": "main",
"index": 0
}
]
]
},
"Clean CV": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1",
"binaryMode": "separate",
"timeSavedMode": "fixed",
"timezone": "Europe/Paris",
"callerPolicy": "workflowsFromSameOwner",
"availableInMCP": false
},
"staticData": null,
"meta": {
"templateCredsSetupCompleted": true
},
"versionId": "1f38b6c8-cab7-4bb2-a645-f6c092f23ca5",
"activeVersionId": "1f38b6c8-cab7-4bb2-a645-f6c092f23ca5",
"versionCounter": 167,
"triggerCount": 1,
"shared": [
{
"updatedAt": "2026-05-25T13:51:11.748Z",
"createdAt": "2026-05-25T13:51:11.748Z",
"role": "workflow:owner",
"workflowId": "olgL8wb7nrJumkct",
"projectId": "GfFdmZTqGEJQkrXG",
"project": {
"updatedAt": "2026-05-11T09:44:44.757Z",
"createdAt": "2026-05-11T09:34:45.695Z",
"id": "GfFdmZTqGEJQkrXG",
"name": "\u00c9ole Wind <megazef@gmail.com>",
"type": "personal",
"icon": null,
"description": null,
"creatorId": "2792484d-cba3-4156-adba-fbc49134eb55"
}
}
],
"tags": [
{
"updatedAt": "2026-05-29T11:31:39.972Z",
"createdAt": "2026-05-29T11:31:39.972Z",
"id": "5zynHpPHQi9PRMyD",
"name": "jobby"
},
{
"updatedAt": "2026-05-11T12:19:25.326Z",
"createdAt": "2026-05-11T12:19:25.326Z",
"id": "8yiCAJZW8fFXfFqI",
"name": "notion"
},
{
"updatedAt": "2026-05-29T11:33:37.867Z",
"createdAt": "2026-05-29T11:33:37.867Z",
"id": "oVl5DgcCQROKFX7w",
"name": "AI"
},
{
"updatedAt": "2026-05-29T11:33:25.261Z",
"createdAt": "2026-05-29T11:33:25.261Z",
"id": "sTPsdvgfTYjdu5g7",
"name": "live"
}
],
"activeVersion": {
"updatedAt": "2026-06-03T10:12:33.000Z",
"createdAt": "2026-06-03T10:11:59.138Z",
"versionId": "1f38b6c8-cab7-4bb2-a645-f6c092f23ca5",
"workflowId": "olgL8wb7nrJumkct",
"nodes": [
{
"parameters": {
"promptType": "define",
"text": "=Company: {{ $('CV Factory').item.json.query.company }}\njon title : {{ $('CV Factory').item.json.query.job_title }}\njob Url: {{ $('CV Factory').item.json.query.job_url }}\nDescription: {{ $('CV Factory').item.json.query.job_description }}\n",
"options": {
"systemMessage": "=# Tu es Jobby 3.0\nL'assistant strat\u00e9gique de recherche d'imploi pour Julien. \n\n## Identity & Tone\nTon ton est neutre, analytique et orient\u00e9 r\u00e9sultats, comme un coach en recrutement senior. Ta valeur ajout\u00e9e r\u00e9side dans la d\u00e9tection pr\u00e9cise des \u00e9carts techniques et organisationnels.\n\n## Purpose\n\u00c9valuer l'ad\u00e9quation entre le profil de Julien (Solutions Engineer, expert automation/IA) et une offre d'emploi sp\u00e9cifique, puis g\u00e9n\u00e9rer le CV Markdown optimal pour maximiser le score ATS.\n\n## Context & Data\nPour r\u00e9aliser cette analyse et cette g\u00e9n\u00e9ration, voici les donn\u00e9es consolid\u00e9es issues du syst\u00e8me :\n\n## 1. Fiche de Poste Aspir\u00e9e (LinkedIn)\n{{ $json.job_description }}\n\n## 2. Directives de Positionnement (La Graine)\n- Baseline attendue : {{ $json.seed_baseline }}\n- Consignes de filtrage : {{ $json.seed_directives }}\n\n## 3. Graphe de Comp\u00e9tences & Exp\u00e9riences (CV Atomique)\n{{ $json.cv_master_payload }}\n\n# Evaluation Criteria\n1. Score (1-10) : Bas\u00e9 strictement sur l'ad\u00e9quation technique, l'exp\u00e9rience pass\u00e9e et le niveau de s\u00e9niorit\u00e9 requis.\n2. Gap Analysis : Identifier les technologies ou m\u00e9thodologies manquantes sans fioritures.\n3. Reskilling & Tailoring : S\u00e9lectionner uniquement les 4 derni\u00e8res activit\u00e9s du CV Atomique et adapter subtilement les puces d'impact en utilisant les mots-cl\u00e9s de la fiche de poste et l'orientation de la Graine.\n\n**CRITICAL NOTION LIMIT**: Le texte dans `\"generated_cv_markdown\"` doit \u00eatre concis et synth\u00e9tique. Chaque section `(Summary, Exp\u00e9rience, etc.)`` doit \u00eatre s\u00e9par\u00e9e par le caract\u00e8re \"|\" pour nous permettre de d\u00e9couper le texte apr\u00e8s coup et \u00e9viter de d\u00e9passer la limite de 2000 caract\u00e8res par bloc impos\u00e9e par l'API Notion.\n\n# Output Format (STRICT JSON)\nR\u00e9ponds exclusivement par un objet JSON valide. Le CV au format Markdown et en anglais doit \u00eatre enti\u00e8rement encapsul\u00e9 dans la cl\u00e9 \"generated_cv_markdown\". Ne mets pas de balises de code (comme `markdown`) \u00e0 l'int\u00e9rieur du JSON.\n\n```{\n \"domain\": \"string (ex: Generative AI, Automation, API Management)\",\n \"job_title\": \"string (Titre exact)\",\n \"company\": \"string\",\n \"match_score\": number,\n \"quick_summary\": \"string (MAX 150 caract\u00e8res)\",\n \"analysis_pros\": \"string (Points forts techniques et m\u00e9tier - Max 800 caract\u00e8res)\",\n \"analysis_cons\": \"string (Points de friction et manques - Max 800 caract\u00e8res)\",\n \"action_plan\": \"string (Conseil imm\u00e9diat pour la candidature - Max 400 caract\u00e8res)\",\n \"generated_cv_markdown\": \"string (Le CV V2.0 complet en Markdown, structur\u00e9 avec l'Aside de d\u00e9part contenant la baseline, puis les 4 derniers jobs adapt\u00e9s aux crit\u00e8res ATS)\"\n}"
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3.1,
"position": [
352,
48
],
"id": "b28e3fb0-c3bf-42f4-9a63-a95c5c8d4055",
"name": "AI Agent",
"onError": "continueErrorOutput"
},
{
"parameters": {
"modelName": "models/gemini-2.5-flash-lite",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"typeVersion": 1.1,
"position": [
352,
320
],
"id": "c09c542b-80ca-4d47-a542-15fc14eabccd",
"name": "Google Gemini Chat Model",
"credentials": {
"googlePalmApi": {
"id": "QZGttnIQoPa4rsAg",
"name": "Google Gemini(PaLM) Api account"
}
}
},
{
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "claude-sonnet-4-6",
"cachedResultName": "Claude Sonnet 4.6"
},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatAnthropic",
"typeVersion": 1.4,
"position": [
-544,
384
],
"id": "29dd6f16-98a6-48f5-ad0b-7a76b5eb335a",
"name": "Anthropic Chat Model",
"credentials": {
"anthropicApi": {
"id": "ZLxe6Yv6rEgDYs00",
"name": "Anthropic account"
}
}
},
{
"parameters": {
"jsCode": "// 1. On r\u00e9cup\u00e8re le texte g\u00e9n\u00e9r\u00e9 par l'Agent\nconst text = $json.output || \"\"; \nconst match = text.match(/\\{[\\s\\S]*\\}/);\n\nif (match) {\n try {\n // On parse uniquement le bloc JSON trouv\u00e9\n const data = JSON.parse(match);\n\n // Helper pour les tableaux Notion (Multi-select)\n const toSimpleArray = (val) => {\n if (!val) return [];\n const rawItems = Array.isArray(val) ? val : val.toString().split(/[|,\\/&]|\\band\\b/);\n return rawItems.map(i => i.trim()).filter(i => i.length > 1);\n };\n\n // 2. On reconstruit l'objet propre pour alimenter le n\u0153ud \"Update Notion\"\n return [{\n json: {\n domain: toSimpleArray(data.domain),\n category: toSimpleArray(data.category || data.job_title),\n job_title: data.job_title || \"Position inconnue\",\n company: data.company || \"Entreprise inconnue\",\n score: parseInt(data.match_score) || 0,\n status: \"To review\",\n summary: data.quick_summary || \"Analyse termin\u00e9e\",\n analysis_pros: data.analysis_pros || \"N/A\",\n analysis_cons: data.analysis_cons || \"N/A\",\n action_plan: data.action_plan || \"N/A\",\n generated_cv_markdown: data.generated_cv_markdown || \"\",\n response_date: new Date().toISOString().split('T')\n }\n }];\n\n } catch (e) {\n return [{ json: { error: \"JSON Parsing Error\", message: e.message, raw: text.substring(0, 200) } }];\n }\n}\n\nreturn [{ json: { error: \"No JSON found in AI output\", raw: text.substring(0, 100) } }];"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
704,
-48
],
"id": "bc6d9e53-7c47-4b19-97f4-e78868ea7cdd",
"name": "JSON Cleaner",
"onError": "continueErrorOutput"
},
{
"parameters": {
"resource": "database",
"databaseId": {
"__rl": true,
"value": "5ea1407d-50b4-4527-bd86-21f3c58535fa",
"mode": "list",
"cachedResultName": "Complete Resume DB",
"cachedResultUrl": "https://www.notion.so/5ea1407d50b44527bd8621f3c58535fa"
}
},
"type": "n8n-nodes-base.notion",
"typeVersion": 2.2,
"position": [
-320,
48
],
"id": "98b735c7-3087-4ff3-83a7-c749cba35b8b",
"name": "Get CV data",
"credentials": {
"notionApi": {
"id": "KYrsxF0plfLkPpqW",
"name": "Notion LinkedIn Auto"
}
}
},
{
"parameters": {
"resource": "database",
"databaseId": {
"__rl": true,
"value": "642d04c9-1c1f-4c01-a4d3-89822f02556b",
"mode": "list",
"cachedResultName": "CV Seeds",
"cachedResultUrl": "https://www.notion.so/642d04c91c1f4c01a4d389822f02556b"
}
},
"type": "n8n-nodes-base.notion",
"typeVersion": 2.2,
"position": [
-96,
48
],
"id": "1c3e6440-cbf2-472c-ab63-959a496a7e6f",
"name": "CV Seed",
"credentials": {
"notionApi": {
"id": "KYrsxF0plfLkPpqW",
"name": "Notion LinkedIn Auto"
}
}
},
{
"parameters": {
"resource": "databasePage",
"databaseId": {
"__rl": true,
"value": "127bad1f-b25a-4b6b-8eec-7b342e3aa504",
"mode": "list",
"cachedResultName": "Linked Detector",
"cachedResultUrl": "https://www.notion.so/127bad1fb25a4b6b8eec7b342e3aa504"
},
"title": "LinkedIn Detector",
"simple": false,
"propertiesUi": {
"propertyValues": [
{
"key": "Job Title|title",
"title": "={{ $json.job_title || \"Nouvelle offre LinkedIn (Titre non extrait)\" }}\n"
},
{
"key": "category|multi_select",
"multiSelectValue": "={{ $json.category }}"
},
{
"key": "URL|url",
"urlValue": "={{ $('CV Factory').item.json.query.job_url }}"
},
{
"key": "Match Score|number",
"numberValue": "={{ $json.score }}"
},
{
"key": "Status|status",
"statusValue": "={{ $json.status }}"
},
{
"key": "Company|select",
"selectValue": "={{ $json.company }}"
},
{
"key": "domain|multi_select",
"multiSelectValue": "={{ $json.domain }}S"
},
{
"key": "analysis|rich_text",
"textContent": "={{ $json.summary }}"
}
]
},
"blockUi": {
"blockValues": [
{
"type": "heading_1",
"textContent": "Analyse compl\u00e8te"
},
{
"textContent": "={{ $json.summary }}"
},
{
"type": "heading_2",
"textContent": "Pros"
},
{
"textContent": "={{ $json.analysis_pros }}"
},
{
"type": "heading_2",
"textContent": "Cons"
},
{
"textContent": "={{ $json.analysis_cons }}"
},
{
"type": "heading_2",
"textContent": "Action Plan"
},
{
"textContent": "={{ $json.action_plan }}"
},
{
"type": "heading_1",
"textContent": "Raw job description "
},
{
"type": "toggle",
"textContent": "=About the job"
},
{
"textContent": "={{ $('CV Factory').item.json.query.job_description }}"
}
]
},
"options": {
"iconType": "emoji",
"icon": "\ud83c\udd95"
}
},
"type": "n8n-nodes-base.notion",
"typeVersion": 2.2,
"position": [
928,
-48
],
"id": "a801be3c-190f-4c32-a564-050e9ba194f2",
"name": "update prospect",
"retryOnFail": false,
"executeOnce": false,
"credentials": {
"notionApi": {
"id": "KYrsxF0plfLkPpqW",
"name": "Notion LinkedIn Auto"
}
}
},
{
"parameters": {
"path": "cv-factory",
"authentication": "headerAuth",
"options": {
"responseData": "{\"status\": \"processing\"}"
}
},
"type": "n8n-nodes-base.webhook",
"typeVersion": 2.1,
"position": [
-544,
48
],
"id": "4f70d8ad-1003-4506-b630-04d85c94d2e1",
"name": "CV Factory",
"webhookId": "44786c94-5bac-493e-a778-8d065668d8b2",
"credentials": {
"httpHeaderAuth": {
"id": "QDm4dLNTsiXmLpmM",
"name": "Header Auth account"
}
}
},
{
"parameters": {
"jsCode": "// 1. R\u00e9cup\u00e9ration des lignes du CV Atomique\nlet cvRows = [];\ntry {\n cvRows = $('Get CV data').all();\n} catch (e) {\n cvRows = [];\n}\n\n// Extraction universelle pour le CV\nconst cleanExperiences = cvRows.map((row, index) => {\n const props = row.json?.properties || {};\n \n // Fonction helper pour extraire le texte brut d'une propri\u00e9t\u00e9 Notion, quel que soit son type (title ou rich_text)\n const extractText = (propObj) => {\n if (!propObj) return \"\";\n const arr = propObj.rich_text || propObj.title || [];\n return arr?.plain_text || propObj.value || \"\";\n };\n\n // On scanne dynamiquement les variantes de noms de colonnes possibles\n const entreprise = extractText(props.Entreprise) || extractText(props.Company) || \"Entreprise \" + (index + 1);\n const poste = extractText(props.Poste) || extractText(props.Role) || \"Solutions Engineer\";\n const description = extractText(props.Description) || \"\";\n const dates = extractText(props.Dates) || extractText(props.Date) || \"\";\n\n return `### ${poste} chez ${entreprise} (${dates})\\n${description}`;\n}).join(\"\\n\\n\");\n\n// 2. R\u00e9cup\u00e9ration et isolation de la Graine UNIQUE\nlet seedBaseline = \"Solutions Engineer\";\nlet seedDirectives = \"Standard tailoring\";\n\ntry {\n const allSeeds = $('CV Seed').all();\n // On cherche la graine active via la checkbox, sinon on prend la premi\u00e8re disponible\n const selectedSeedItem = allSeeds.find(item => {\n const props = item.json?.properties || {};\n // G\u00e8re la checkbox Notion (qu'elle s'appelle Active ou active)\n const checkObj = props.Active || props.active;\n return checkObj?.checkbox === true;\n }) || allSeeds;\n\n if (selectedSeedItem && selectedSeedItem.json?.properties) {\n const sProps = selectedSeedItem.json.properties;\n \n const extractSeedText = (propObj) => {\n if (!propObj) return null;\n const arr = propObj.title || propObj.rich_text || [];\n return arr?.plain_text || null;\n };\n\n seedBaseline = extractSeedText(sProps.Baseline) || extractSeedText(sProps.baseline) || seedBaseline;\n seedDirectives = extractSeedText(sProps.Directives) || extractSeedText(sProps.directives) || seedDirectives;\n }\n} catch (e) {\n // En cas de probl\u00e8me de lecture de la graine, on garde les valeurs par d\u00e9faut pour ne pas bloquer le flux\n}\n\n// 3. Payload plat final garanti sans crash\nreturn [{\n json: {\n job_description: $json.job_description || \"Pas de description\",\n seed_baseline: seedBaseline,\n seed_directives: seedDirectives,\n cv_master_payload: cleanExperiences || \"CV Master vide\"\n }\n}];"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
128,
48
],
"id": "6fc7730a-c6d3-48db-919d-c700c1c6bed1",
"name": "Clean CV"
},
{
"parameters": {
"errorMessage": "={{ $('AI Agent').item.json.error }}"
},
"type": "n8n-nodes-base.stopAndError",
"typeVersion": 1,
"position": [
704,
144
],
"id": "0334fb31-bc67-410f-89bc-7e0de9ccaa43",
"name": "Stop and Error"
}
],
"connections": {
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"AI Agent": {
"main": [
[
{
"node": "JSON Cleaner",
"type": "main",
"index": 0
}
],
[
{
"node": "Stop and Error",
"type": "main",
"index": 0
}
]
]
},
"Anthropic Chat Model": {
"ai_languageModel": [
[]
]
},
"JSON Cleaner": {
"main": [
[
{
"node": "update prospect",
"type": "main",
"index": 0
}
],
[]
]
},
"Get CV data": {
"main": [
[
{
"node": "CV Seed",
"type": "main",
"index": 0
}
]
]
},
"CV Seed": {
"main": [
[
{
"node": "Clean CV",
"type": "main",
"index": 0
}
]
]
},
"update prospect": {
"main": [
[],
[]
]
},
"CV Factory": {
"main": [
[
{
"node": "Get CV data",
"type": "main",
"index": 0
}
]
]
},
"Clean CV": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
}
},
"authors": "\u00c9ole Wind",
"name": "Version 1f38b6c8",
"description": "Authorization",
"autosaved": true,
"workflowPublishHistory": [
{
"createdAt": "2026-06-03T10:12:33.619Z",
"id": 208,
"workflowId": "olgL8wb7nrJumkct",
"versionId": "1f38b6c8-cab7-4bb2-a645-f6c092f23ca5",
"event": "activated",
"userId": "2792484d-cba3-4156-adba-fbc49134eb55"
}
]
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
anthropicApigooglePalmApihttpHeaderAuthnotionApi
Pro
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About this workflow
Jobby - LinkedIn 2 Notion. Uses agent, lmChatGoogleGemini, lmChatAnthropic, notion. Webhook trigger; 10 nodes.
Source: https://github.com/gnueole/jobby-md2html/blob/main/n8n/jobby-linkedin-2-notion.json — original creator credit. Request a take-down →
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