This workflow follows the Agent → Gmail recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"name": "CV-analyzer",
"nodes": [
{
"parameters": {
"promptType": "define",
"text": "=Zwr\u00f3\u0107 wynik w formacie JSON o strukturze:\n {\n \"full_name\": \"...\",\n \"email\": \"...\",\n \"phone\": \"...\",\n \"years_experience\": ...,\n \"key_skills\": [\"...\", \"...\"],\n \"job_history\": [{\"company\": \"...\", \"role\": \"...\", \"period\": \"...\"}],\n \"education\": \"...\",\n \"match_score\": ...,\n \"match_reason\": \"...\"\n }\n\nCa\u0142e CV jest zawarte w {{ $json.text }}",
"options": {
"systemMessage": "Jeste\u015b ekspertem HR w agencji marketingowej. Otrzymujesz CV kandydata na stanowisko [wstaw stanowisko, np. \"Specjalista ds. marketingu cyfrowego\"]. \nTwoim zadaniem jest:\n1. Wyodr\u0119bnij: imi\u0119 i nazwisko, e\u2011mail, telefon, lata do\u015bwiadczenia, kluczowe umiej\u0119tno\u015bci (wymie\u0144 max 5), dotychczasowe stanowiska (firma, rola, okres), wykszta\u0142cenie.\n2. Oce\u0144 dopasowanie kandydata do stanowiska w skali 0-100%, uwzgl\u0119dniaj\u0105c: do\u015bwiadczenie w marketingu (waga 40%), znajomo\u015b\u0107 narz\u0119dzi (Google Ads, Meta Ads, SEO \u2013 waga 30%), umiej\u0119tno\u015bci mi\u0119kkie (waga 20%), dodatkowe osi\u0105gni\u0119cia (waga 10%).\n3. Podaj kr\u00f3tkie uzasadnienie oceny (2-3 zdania)."
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3.1,
"position": [
-1648,
-112
],
"id": "35876bb9-c5a5-44e4-9d6a-8efa6103fec3",
"name": "Analyze CV"
},
{
"parameters": {
"jsCode": "const aiOutput = $('Analyze CV').first().json.output; // dostosuj pole je\u015bli trzeba\n\n// Wyci\u0105gnij JSON z markdown code blocka lub czystego tekstu\nfunction extractJSON(text) {\n // Usu\u0144 ```json ... ``` lub ``` ... ```\n const codeBlockMatch = text.match(/```(?:json)?\\s*([\\s\\S]*?)```/);\n if (codeBlockMatch) {\n return JSON.parse(codeBlockMatch[1].trim());\n }\n\n // Spr\u00f3buj wyci\u0105gn\u0105\u0107 surowy JSON (np. { ... })\n const rawMatch = text.match(/\\{[\\s\\S]*\\}/);\n if (rawMatch) {\n return JSON.parse(rawMatch[0].trim());\n }\n\n throw new Error('Nie znaleziono JSON-a w odpowiedzi AI');\n}\n\nconst parsed = extractJSON(aiOutput);\n\nreturn [{ json: parsed }];"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
-1296,
-112
],
"id": "4b2e90a1-187a-4180-9d84-8b3bf47321e0",
"name": "Extract JSON"
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"typeVersion": 1,
"position": [
-1312,
160
],
"id": "6f749f42-f32b-473f-98cf-8d93b75a1f2f",
"name": "Google Gemini Chat Model",
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"resource": "databasePage",
"databaseId": {
"__rl": true,
"value": "32c30768-f350-807c-8712-c0b84b0f4048",
"mode": "list",
"cachedResultName": "CV\u2019s",
"cachedResultUrl": "https://www.notion.so/32c30768f350807c8712c0b84b0f4048"
},
"propertiesUi": {
"propertyValues": [
{
"key": "Full Name|title",
"title": "={{ $json.full_name }}"
},
{
"key": "Email|rich_text",
"textContent": "={{ $json.email }}"
},
{
"key": "Phone|phone_number",
"phoneValue": "={{ $json.phone }}"
},
{
"key": "Match Score|rich_text",
"textContent": "={{ $json.match_score.toString() }}"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.notion",
"typeVersion": 2.2,
"position": [
-992,
-240
],
"id": "e0723bf0-f683-487d-822d-05f5dcaac355",
"name": "Create a database page",
"credentials": {
"notionApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"promptType": "define",
"text": "=Dane kandydata:\n- Imi\u0119 i nazwisko: {{$json.full_name}}\n- Dopasowanie: {{$json.match_score}}%\n- Kluczowe umiej\u0119tno\u015bci: {{$json.key_skills}}\n- Lata do\u015bwiadczenia: {{$json.years_experience}}\n- Uzasadnienie: {{$json.match_reason}}\n\nNazwa firmy: [Twoja agencja, np. \u201eGG Automations\u201d]\nStanowisko, na kt\u00f3re aplikuje: Specjalista ds. automatyzacji marketingu\n\nWygeneruj draft e\u2011maila zgodnie z powy\u017cszymi wytycznymi. Napisz sam\u0105 wiadomo\u015b\u0107 bez tematu ",
"options": {
"systemMessage": "=Jeste\u015b asystentem HR w ma\u0142ej agencji marketingowej. Twoim zadaniem jest napisanie spersonalizowanego e\u2011maila do kandydata na podstawie dostarczonych danych z analizy CV.\n\nOtrzymujesz:\n- Imi\u0119 i nazwisko kandydata\n- Ocen\u0119 dopasowania (0\u2013100%)\n- Kluczowe umiej\u0119tno\u015bci\n- Lata do\u015bwiadczenia\n- Uzasadnienie oceny\n\nTwoja odpowied\u017a powinna by\u0107 w j\u0119zyku polskim, profesjonalna, ale ciep\u0142a. Dopasuj tre\u015b\u0107 do wyniku:\n- Je\u015bli dopasowanie >= 70%: napisz e\u2011mail z zaproszeniem na rozmow\u0119 kwalifikacyjn\u0105. Wspomnij o konkretnych umiej\u0119tno\u015bciach kandydata, kt\u00f3re zrobi\u0142y wra\u017cenie. Zaproponuj termin spotkania (lub popro\u015b o propozycj\u0119 terminu). Podaj kr\u00f3tki opis firmy i stanowiska.\n- Je\u015bli dopasowanie < 70%: napisz uprzejm\u0105 odmow\u0119, podzi\u0119kuj za zainteresowanie, zach\u0119\u0107 do \u015bledzenia przysz\u0142ych ofert. Mo\u017cesz delikatnie zasugerowa\u0107 obszary rozwoju, ale zachowaj pozytywny ton.\n\nStruktura e\u2011maila:\n- Temat: [odpowiedni do decyzji]\n- Zwrot grzeczno\u015bciowy\n- Tre\u015b\u0107 \u2013 3\u20134 zdania, plus ewentualnie pro\u015bba o termin rozmowy\n- Podpis: Zesp\u00f3\u0142 rekrutacyjny [nazwa firmy]\n\nNie u\u017cywaj zmiennych w stylu {{$json.full_name}} \u2013 podstaw dane bezpo\u015brednio."
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3.1,
"position": [
-1024,
-48
],
"id": "6c3a1243-0fe7-414c-a77a-d4a04c65094e",
"name": "Create draft"
},
{
"parameters": {},
"type": "n8n-nodes-base.noOp",
"typeVersion": 1,
"position": [
-464,
-48
],
"id": "763bcca4-a7d0-433e-84b9-ea58c71a7c9b",
"name": "Notification"
},
{
"parameters": {
"resource": "draft",
"subject": "Zaproszenie na rozmow\u0119 kwalifikacyjn\u0105",
"message": "={{ $json.output }}",
"options": {}
},
"type": "n8n-nodes-base.gmail",
"typeVersion": 2.2,
"position": [
-672,
-48
],
"id": "514d57d8-e88c-4488-98b1-8bb990cd15c4",
"name": "Create a draft in Gmail",
"credentials": {
"gmailOAuth2": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"pollTimes": {
"item": [
{
"mode": "everyMinute"
}
]
},
"simple": false,
"filters": {
"readStatus": "unread"
},
"options": {
"downloadAttachments": true
}
},
"type": "n8n-nodes-base.gmailTrigger",
"typeVersion": 1.3,
"position": [
-2624,
-112
],
"id": "e471a84d-3e20-413c-93f9-7051352f97d0",
"name": "On message received",
"credentials": {
"gmailOAuth2": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"operation": "get",
"messageId": "={{ $json.id }}",
"simple": false,
"options": {
"downloadAttachments": true
}
},
"type": "n8n-nodes-base.gmail",
"typeVersion": 2.2,
"position": [
-2032,
-112
],
"id": "b9a478bf-3d59-4b80-a204-7f73aaa5f4ba",
"name": "Get a message",
"credentials": {
"gmailOAuth2": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"operation": "pdf",
"binaryPropertyName": "={{ $('On message received').item.binary.attachment_0 }}",
"options": {}
},
"type": "n8n-nodes-base.extractFromFile",
"typeVersion": 1.1,
"position": [
-1856,
-112
],
"id": "bfa1771b-0dd1-4f85-a601-854c4af4e6aa",
"name": "Extract from File"
},
{
"parameters": {
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "strict",
"version": 3
},
"conditions": [
{
"id": "9d650dcd-03e9-42b3-9d00-e8b9fd7425d1",
"leftValue": "={{ $('On message received').item.binary.attachment_0 }}",
"rightValue": "",
"operator": {
"type": "object",
"operation": "exists",
"singleValue": true
}
}
],
"combinator": "and"
},
"options": {}
},
"type": "n8n-nodes-base.filter",
"typeVersion": 2.3,
"position": [
-2432,
-112
],
"id": "22996390-a92c-4c93-88d7-25d984792c71",
"name": "Filter"
},
{
"parameters": {
"operation": "markAsRead",
"messageId": "={{ $json.id }}"
},
"type": "n8n-nodes-base.gmail",
"typeVersion": 2.2,
"position": [
-2240,
-112
],
"id": "a328b139-63ff-4139-bcd0-7f4a0d8e22d2",
"name": "Mark a message as read",
"credentials": {
"gmailOAuth2": {
"name": "<your credential>"
}
}
}
],
"connections": {
"Analyze CV": {
"main": [
[
{
"node": "Extract JSON",
"type": "main",
"index": 0
}
]
]
},
"Extract JSON": {
"main": [
[
{
"node": "Create draft",
"type": "main",
"index": 0
},
{
"node": "Create a database page",
"type": "main",
"index": 0
}
]
]
},
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "Create draft",
"type": "ai_languageModel",
"index": 0
},
{
"node": "Analyze CV",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Create draft": {
"main": [
[
{
"node": "Create a draft in Gmail",
"type": "main",
"index": 0
}
]
]
},
"Create a draft in Gmail": {
"main": [
[
{
"node": "Notification",
"type": "main",
"index": 0
}
]
]
},
"On message received": {
"main": [
[
{
"node": "Filter",
"type": "main",
"index": 0
}
]
]
},
"Get a message": {
"main": [
[
{
"node": "Extract from File",
"type": "main",
"index": 0
}
]
]
},
"Extract from File": {
"main": [
[
{
"node": "Analyze CV",
"type": "main",
"index": 0
}
]
]
},
"Filter": {
"main": [
[
{
"node": "Mark a message as read",
"type": "main",
"index": 0
}
]
]
},
"Mark a message as read": {
"main": [
[
{
"node": "Get a message",
"type": "main",
"index": 0
}
]
]
}
},
"active": false,
"settings": {
"executionOrder": "v1",
"binaryMode": "separate",
"availableInMCP": false
},
"versionId": "a43ff4b0-a6c3-46a7-9376-5517ebcf6e6a",
"meta": {
"templateCredsSetupCompleted": true
},
"id": "kuJMOtnfKAxoVPnZ",
"tags": []
}
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.
gmailOAuth2googlePalmApinotionApi
Pro
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About this workflow
CV-analyzer. Uses agent, lmChatGoogleGemini, notion, gmail. Event-driven trigger; 12 nodes.
Source: https://github.com/berozzi/n8n-workflows/blob/a016ccdfdf581c531f9d4880deecd737f7f750ed/workflows/CV-analyzer.json — original creator credit. Request a take-down →
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