첫 번째 빠른 통로 AI 채용 도우미
고급
이것은자동화 워크플로우로, 50개의 노드를 포함합니다.주로 If, Set, Code, Merge, Slack 등의 노드를 사용하며. Gemini AI를 통해 Gmail에서 Slack과 Google Sheets로의 빠른 이력서 필터링 분석
사전 요구사항
- •Slack Bot Token 또는 Webhook URL
- •Google Drive API 인증 정보
- •대상 API의 인증 정보가 필요할 수 있음
- •Google 계정 및 Gmail API 인증 정보
- •Google Sheets API 인증 정보
- •Google Gemini API Key
사용된 노드 (50)
카테고리
-
워크플로우 미리보기
노드 연결 관계를 시각적으로 표시하며, 확대/축소 및 이동을 지원합니다
워크플로우 내보내기
다음 JSON 구성을 복사하여 n8n에 가져오면 이 워크플로우를 사용할 수 있습니다
{
"id": "qDFNsMCGATg1FRWI",
"meta": {
"instanceId": "90dd23d886c9cb675f452d0fb004af6ee783e4e974ef4384cbfad1854c68a875",
"templateCredsSetupCompleted": true
},
"name": "First-Round Fast Track AI Recruiter Assistant",
"tags": [],
"nodes": [
{
"id": "67ba2250-7192-46bb-ae03-3f52e52d1212",
"name": "Trigger Google Docs Conversion",
"type": "n8n-nodes-base.httpRequest",
"position": [
-1920,
-208
],
"parameters": {
"url": "={{ $json.headers.location }}",
"method": "PUT",
"options": {
"response": {
"response": {
"fullResponse": true
}
}
},
"sendBody": true,
"sendQuery": true,
"contentType": "binaryData",
"sendHeaders": true,
"authentication": "predefinedCredentialType",
"queryParameters": {
"parameters": [
{
"name": "fields",
"value": "=id,name,mimeType,webViewLink,parents"
}
]
},
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "={{ ( (b => {\n const fn = (b?.fileName || '').toLowerCase();\n const mt = (b?.mimeType || '').toLowerCase();\n if (fn.endsWith('.doc')) return 'application/msword';\n if (fn.endsWith('.docx')) return 'application/vnd.openxmlformats-officedocument.wordprocessingml.document';\n if (mt && mt !== 'application/octet-stream') return mt;\n return 'application/vnd.openxmlformats-officedocument.wordprocessingml.document';\n})(Object.values($binary || {})[0] || {})).replace(/[\\u0000-\\u001F\\u007F]+/g,'').trim() }}"
}
]
},
"inputDataFieldName": "={{ Object.keys($binary || {})[0] }}",
"nodeCredentialType": "googleDriveOAuth2Api"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "IjoB5flCkLlcfjdH",
"name": "Google Drive account"
}
},
"typeVersion": 4.2
},
{
"id": "51864d3b-8a5d-472b-9d69-61f3c0b810fb",
"name": "Stream Doc/Docx File",
"type": "n8n-nodes-base.httpRequest",
"position": [
-2320,
-112
],
"parameters": {
"url": "https://www.googleapis.com/upload/drive/v3/files?uploadType=resumable&supportsAllDrives=true",
"method": "POST",
"options": {
"response": {
"response": {
"fullResponse": true
}
}
},
"jsonBody": "={\n \"name\": \"{{ ( () => {\n const sender = ($json.from?.value?.[0]?.name || '').toString().trim().replace(/[\\r\\n]+/g,'');\n const fallback = (Object.values($binary||{})[0]?.fileName || 'document').replace(/\\\\.[^/.]+$/,'');\n const who = sender || fallback;\n\n const parts = new Intl.DateTimeFormat('en-GB', {\n timeZone: 'UTC',\n year: 'numeric', month: '2-digit', day: '2-digit',\n hour: '2-digit', minute: '2-digit', second: '2-digit',\n hour12: false\n }).formatToParts(new Date());\n\n const p = {}; parts.forEach(x => p[x.type] = x.value);\n const date = `${p.year}-${p.month}-${p.day}`; // YYYY-MM-DD\n const time = `${p.hour}${p.minute}${p.second}`;\n\n return `${who} - CV - ${date}_${time}`.replace(/_{2,}/g,'_').trim();\n })() }}\",\n \"mimeType\": \"application/vnd.google-apps.document\",\n \"parents\": [\"YOUR_FOLDER_ID_HERE\"]\n}\n",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "=application/json; charset=UTF-8"
}
]
},
"nodeCredentialType": "googleDriveOAuth2Api"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "IjoB5flCkLlcfjdH",
"name": "Google Drive account"
}
},
"typeVersion": 4.2
},
{
"id": "64ce3d1c-ac9d-4562-bbf1-4250f50fb555",
"name": "CV 파일 보존",
"type": "n8n-nodes-base.code",
"position": [
-2320,
-288
],
"parameters": {
"jsCode": "// simply pass the incoming items untouched so downstream Merge gets original binary\nreturn items;\n"
},
"typeVersion": 2
},
{
"id": "18fa7d4c-12b3-45a3-8aac-e756f2c08ef5",
"name": "병합",
"type": "n8n-nodes-base.merge",
"position": [
-2096,
-208
],
"parameters": {
"mode": "combine",
"options": {},
"combineBy": "combineByPosition"
},
"typeVersion": 3.2
},
{
"id": "c3d0d223-88c6-4af6-8e43-454c779c3a60",
"name": "표준화",
"type": "n8n-nodes-base.set",
"position": [
-992,
48
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "b4b5c9c5-fe76-40eb-b234-48c042f0baa7",
"name": "cv_text",
"type": "string",
"value": "={{ $json.cv_text }}"
},
{
"id": "e6933fbf-fcf7-48cc-a763-9ca67de28322",
"name": "cv_webviewlink",
"type": "string",
"value": "={{ $json.cv_webviewlink }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "f3219f5d-cda5-41b6-94fc-743d0b1e8279",
"name": "파일에서 추출",
"type": "n8n-nodes-base.extractFromFile",
"position": [
1296,
-80
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
},
{
"id": "bdc929ce-fc8e-4779-b0e1-f74965bfd44e",
"name": "정보 추출기",
"type": "@n8n/n8n-nodes-langchain.informationExtractor",
"position": [
-512,
448
],
"parameters": {
"text": "={{ $('Standardize').first().json.cv_text }}",
"options": {},
"attributes": {
"attributes": [
{
"name": "First Name",
"required": true,
"description": "First name of the candidtae"
},
{
"name": "Last Name",
"required": true,
"description": "Last name of the candidate"
},
{
"name": "Email Address",
"description": "Email address of the candidate"
}
]
}
},
"typeVersion": 1.2
},
{
"id": "73b50c74-acf9-4e0e-8881-0b5860a19136",
"name": "시트에 행 추가",
"type": "n8n-nodes-base.googleSheets",
"position": [
-176,
448
],
"parameters": {
"columns": {
"value": {
"CV": "={{ $('Standardize').first().json.cv_webviewlink }}",
"Date": "={{ $now.setZone('UTC').format('yyyy-MM-dd HH:mm') }}",
"Email": "={{ $json.output['Email Address'] }}",
"JD Match": "={{ $('Recruiter Scoring Agent').item.json.output.selected_jd_filename }}",
"Last Name": "={{ $json.output['Last Name'] }}",
"Strengths": "={{ $('Recruiter Scoring Agent').item.json.output.candidate_strengths.join(\"\\n\\n\") }}",
"First Name": "={{ $json.output['First Name'] }}",
"Weaknesses": "={{ $('Recruiter Scoring Agent').item.json.output.candidate_weaknesses.join(\"\\n\\n\") }}",
"Overall Fit": "={{ $('Recruiter Scoring Agent').item.json.output.overall_fit_rating }}",
"Risk Factor": "={{ $('Recruiter Scoring Agent').item.json.output.risk_factor.score }} \n\n{{ $('Recruiter Scoring Agent').item.json.output.risk_factor.explanation }}",
"Justification": "={{ $('Recruiter Scoring Agent').item.json.output.justification_for_rating }}",
"Reward Factor": "={{ $('Recruiter Scoring Agent').item.json.output.reward_factor.score }}\n\n{{ $('Recruiter Scoring Agent').item.json.output.reward_factor.explanation }}",
"Submission ID": "={{ \n (( $json.output?.['Last Name'] || 'unknown' ) + '')\n .toLowerCase()\n .replace(/[^a-z0-9]+/g, '') // sanitize\n + '_' +\n new Date().toISOString().replace(/[-:TZ.]/g, '').slice(0,14) // UTC YYYYMMDDHHmmss\n}}",
"Sent From Email": "={{ $('Receive CV via Email').first().json.from.value[0].address }}"
},
"schema": [
{
"id": "Submission ID",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Submission ID",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Date",
"type": "string",
"display": true,
"required": false,
"displayName": "Date",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "CV",
"type": "string",
"display": true,
"required": false,
"displayName": "CV",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "First Name",
"type": "string",
"display": true,
"required": false,
"displayName": "First Name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Last Name",
"type": "string",
"display": true,
"required": false,
"displayName": "Last Name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Email",
"type": "string",
"display": true,
"required": false,
"displayName": "Email",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Sent From Email",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Sent From Email",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Strengths",
"type": "string",
"display": true,
"required": false,
"displayName": "Strengths",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Weaknesses",
"type": "string",
"display": true,
"required": false,
"displayName": "Weaknesses",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Risk Factor",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Risk Factor",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Reward Factor",
"type": "string",
"display": true,
"required": false,
"displayName": "Reward Factor",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "JD Match",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "JD Match",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Overall Fit",
"type": "string",
"display": true,
"required": false,
"displayName": "Overall Fit",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Justification",
"type": "string",
"display": true,
"required": false,
"displayName": "Justification",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "TC Decision",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "TC Decision",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1ZNXqOSfSusmQxmCbNQ61Y-Ln9l0jCTzSfRc-JA_y9Oo/edit#gid=0",
"cachedResultName": "1. AI Candidate Screening"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1ZNXqOSfSusmQxmCbNQ61Y-Ln9l0jCTzSfRc-JA_y9Oo",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1ZNXqOSfSusmQxmCbNQ61Y-Ln9l0jCTzSfRc-JA_y9Oo/edit?usp=drivesdk",
"cachedResultName": "AI Candidate Screening"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "TjOctR8CYkO1SVp8",
"name": "Google Sheets account 2"
}
},
"typeVersion": 4.7
},
{
"id": "8e9c7f2c-3cb6-44cb-936f-27bd5e1f3dd7",
"name": "웹 링크 가져오기",
"type": "n8n-nodes-base.httpRequest",
"position": [
-1728,
-208
],
"parameters": {
"url": "=https://www.googleapis.com/drive/v3/files/{{ $json.body.id }}?fields=id,name,mimeType,webViewLink,webContentLink,parents",
"options": {},
"authentication": "predefinedCredentialType",
"nodeCredentialType": "googleDriveOAuth2Api"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "IjoB5flCkLlcfjdH",
"name": "Google Drive account"
}
},
"typeVersion": 4.2
},
{
"id": "341f3daf-bffa-4388-97a0-457334d61774",
"name": "CV 다운로드 - PDF",
"type": "n8n-nodes-base.googleDrive",
"position": [
-1840,
208
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.id }}"
},
"options": {},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "IjoB5flCkLlcfjdH",
"name": "Google Drive account"
}
},
"typeVersion": 3
},
{
"id": "56d16d49-6123-482b-b2a5-ea1919fc3ce2",
"name": "CV 다운로드 - GDoc을 PDF로",
"type": "n8n-nodes-base.googleDrive",
"position": [
-1536,
-208
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.id }}"
},
"options": {
"googleFileConversion": {
"conversion": {
"docsToFormat": "application/pdf"
}
}
},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "IjoB5flCkLlcfjdH",
"name": "Google Drive account"
}
},
"typeVersion": 3
},
{
"id": "7b7210cb-fde5-4f8f-a4ba-b68af8711f17",
"name": "스위치 - 파일 유형",
"type": "n8n-nodes-base.switch",
"position": [
-2624,
48
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "Doc-Docx",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "f3cab524-be2f-44ad-bc73-664ee982e4ff",
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ (Object.values($binary || {})[0]?.mimeType || '').toLowerCase() }}",
"rightValue": "={{ \"application/vnd.openxmlformats-officedocument.wordprocessingml.document\" || \"application/msword\" }}"
}
]
},
"renameOutput": true
},
{
"outputKey": "PDF",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "dac4774b-bbf3-4033-a144-15802529d97a",
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ (Object.values($binary || {})[0]?.mimeType || '').toLowerCase() }}",
"rightValue": "application/pdf"
}
]
},
"renameOutput": true
}
]
},
"options": {}
},
"typeVersion": 3.2
},
{
"id": "10b57b25-585c-4e27-8024-945763f66d99",
"name": "CV 업로드 - PDF",
"type": "n8n-nodes-base.googleDrive",
"position": [
-2144,
208
],
"parameters": {
"name": "={{ $('Receive CV via Email').item.json.from.value[0].name + ' - CV - ' + (()=>{const parts=new Intl.DateTimeFormat('en-GB',{timeZone:'UTC',year:'numeric',month:'2-digit',day:'2-digit',hour:'2-digit',minute:'2-digit',second:'2-digit',hour12:false}).formatToParts(new Date());const p={};parts.forEach(x=>p[x.type]=x.value);return `${p.year}-${p.month}-${p.day}_${p.hour}${p.minute}${p.second}`;})() }}",
"driveId": {
"__rl": true,
"mode": "list",
"value": "My Drive"
},
"options": {},
"folderId": {
"__rl": true,
"mode": "list",
"value": "1tabXACSCjelI0y82m7M7TKlvm0ta6_06",
"cachedResultUrl": "https://drive.google.com/drive/folders/1tabXACSCjelI0y82m7M7TKlvm0ta6_06",
"cachedResultName": "Candidate CVs"
},
"inputDataFieldName": "={{ Object.keys($binary || {})[0] }}"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "IjoB5flCkLlcfjdH",
"name": "Google Drive account"
}
},
"typeVersion": 3
},
{
"id": "8616f65e-71e2-453c-bfd8-cda7c512fe35",
"name": "PDF 다운로드에서 추출",
"type": "n8n-nodes-base.extractFromFile",
"position": [
-1344,
-208
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
},
{
"id": "59848ee7-40b0-4d99-8b27-49088f249fbb",
"name": "PDF에서 추출",
"type": "n8n-nodes-base.extractFromFile",
"position": [
-1536,
208
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
},
{
"id": "7461a572-e6c2-4e6a-b63c-88788e1c5a1e",
"name": "선택된 JD 다운로드",
"type": "n8n-nodes-base.googleDrive",
"position": [
944,
-256
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.output.email_match.jd_file_id }}"
},
"options": {
"googleFileConversion": {
"conversion": {
"docsToFormat": "application/pdf"
}
}
},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "IjoB5flCkLlcfjdH",
"name": "Google Drive account"
}
},
"typeVersion": 3
},
{
"id": "19be4be4-3335-4f53-9bb2-103c9a1e5cca",
"name": "이메일로 CV 수신",
"type": "n8n-nodes-base.gmailTrigger",
"position": [
-2848,
48
],
"parameters": {
"simple": false,
"filters": {
"labelIds": [
"Label_5882671977694855295"
]
},
"options": {
"downloadAttachments": true,
"dataPropertyAttachmentsPrefixName": "cv_"
},
"pollTimes": {
"item": [
{
"mode": "everyMinute"
}
]
}
},
"credentials": {
"gmailOAuth2": {
"id": "51mlyK2w5LA9QaKi",
"name": "Gmail account"
}
},
"typeVersion": 1.3
},
{
"id": "0fae31aa-69ea-4176-9b4c-f4ab7fa2fdf1",
"name": "JD 매칭 에이전트",
"type": "@n8n/n8n-nodes-langchain.agent",
"onError": "continueRegularOutput",
"position": [
-672,
-272
],
"parameters": {
"text": "=## Candidate's Email (Prority JD matching method)\nEMAIL SUBJECT: {{ $('Receive CV via Email').item.json.subject }}\nEMAIL BODY: {{ $('Receive CV via Email').item.json.text }}\n\n## CANDIDATE CV (Fallback JD matching method if candidate's email does not state which role they're applying for)\n{{ $json.cv_text }}\n\n\n## AVAILABLE JOB DESCRIPTIONS:\nUse the Google Drvie tool to see the list of our current job description files and file IDs. They are each aptly named with a job title in the file name.\n\nAnalyze the email context first, and secondly the candidate's CV as a fallback method.\n- Select the SINGLE most appropriate job description if there is one that clearly relates to candidate's email contents. IF EMAIL SUBJECT OR BODY MENTIONS A ROLE THAT IS IDENTICAL OR CLOSELY RELATES TO A JD YOU SEE IN THE LIST PROVIDED, YOU MUST SELECT THAT JD.\n- OR, as the fallback method, select up to a maximum of 3 job descriptions that are a best match to the candidate's CV and profile.\n\n\n## YOUR RESPONSE FORMAT\nReturn your response in this exact JSON format:\n\nFor email match:\n{\n \"match_type\": \"email_match\",\n \"email_match\": {\n \"jd_filename\": \"Marketing Director JD\",\n \"jd_file_id\": \"1xxxxxxxxxxxxxxxxxxxxxx\",\n \"confidence\": \"high\"\n }\n}\n\nFor CV match (up to a maximum of 3 best-match JDs):\n{\n \"match_type\": \"cv_match\", \n \"cv_match\": [\n {\n \"jd_filename\": \"Marketing Director JD\",\n \"jd_file_id\": \"2xxxxxxxxxxxxxxxxxxxxxx\"\n },\n {\n \"jd_filename\": \"COO_JD.pdf\", \n \"jd_file_id\": \"3xxxxxxxxxxxxxxxxxxxxxx\"\n },\n {\n \"jd_filename\": \"Sales Enablement Lead - job description.pdf\",\n \"jd_file_id\": \"4xxxxxxxxxxxxxxxxxxxxxx\"\n }\n ]\n}",
"options": {
"systemMessage": "=You are an expert HR tech recruiter. Your task is to match a candidate with the most appropriate job description from as list of job descriptions I provide to you. \n\nAs a priority, you should first try to match a single JD based on the candidate's email (subject line and message) where possible. \n\nIf the contents of their email do not clearly state a JD or specific role, then you should match up to 3 JDs based on the content of their CV as a fallback method - but you do not have to match always 3: it can be less JDs if there is just 1 or 2 JDs that are only the best fit.\n\n## COMPANY DESCRIPTION\nOur company specialises in providing AI and Automation workflow solutions for small businesses, and we use tools such as n8n, Zapier, OpenAI, Claude Code, Airtable, and more."
},
"promptType": "define",
"hasOutputParser": true
},
"executeOnce": false,
"typeVersion": 2.2
},
{
"id": "99f8d6a6-3820-4744-8011-e15f66a4b133",
"name": "스티키 노트2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2432,
112
],
"parameters": {
"color": 5,
"width": 1472,
"height": 320,
"content": "## Get CV via PDF Format"
},
"typeVersion": 1
},
{
"id": "56eb1d87-b1cd-469a-ac19-8b71fe8735c6",
"name": "스티키 노트3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2432,
-384
],
"parameters": {
"color": 6,
"width": 1472,
"height": 464,
"content": "## Get CV via Word/Doc/Docx Format"
},
"typeVersion": 1
},
{
"id": "5ce6b66d-301e-45b0-a8a8-b2fdee67d833",
"name": "스티키 노트",
"type": "n8n-nodes-base.stickyNote",
"position": [
-928,
-384
],
"parameters": {
"color": 2,
"width": 1712,
"height": 624,
"content": "## Job Description (Vacany) Matching with Candidate's CV"
},
"typeVersion": 1
},
{
"id": "abd2b59f-381e-4f73-9fea-561466127101",
"name": "스티키 노트1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-928,
272
],
"parameters": {
"width": 1392,
"height": 560,
"content": "## 3. CV Analysis and Feedback"
},
"typeVersion": 1
},
{
"id": "8e857a30-d21e-4753-b8f0-ecd834f5b195",
"name": "상세 JD 매칭 에이전트",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
944,
-48
],
"parameters": {
"text": "=Compare this candidate's full CV against the detailed job descriptions provided. There will be a maximum of 3 separate job descriptions that were previously identified as being a best match for this candidate's profile.\n\nSelect the SINGLE best JD match by simply responding with the file name exactly like the input of each file name provided to you.\n\n\n## Candidate's CV\n{{ $('Standardize').item.json.cv_text }}\n\n\n## Pre-matched Job Descriptions\n\nJob Description 1: {{ $('Loop Over Items').all()[0].json.jd_filename }}\n{{ $('Loop Over Items').all()[0].json.text }}\n\n\n{% if $('Loop Over Items').all()[1] %}\nJob Description 2: {{ $('Loop Over Items').all()[1].json.jd_filename }}\n{{ $('Loop Over Items').all()[1].json.text }}\n{% endif %}\n\n\n{% if $('Loop Over Items').all()[2] %}\nJob Description 3: {{ $('Loop Over Items').all()[2].json.jd_filename }}\n{{ $('Loop Over Items').all()[2].json.text }}\n{% endif %}\n\n\n--\n\nSelect the best match and respond in this JSON format:\n{\n \"selected_jd\": {\n \"jd_filename\": \"exact_filename_here\"\n }\n}",
"options": {
"systemMessage": "=You are an expert HR tech recruiter. Your task is to match a candidate's profile based on their CV provided, with the best-fit job description provided from a maximum of 3 job descriptions.\n\n## COMPANY DESCRIPTION\nOur company specialises in providing AI and Automation workflow solutions for small businesses, and we use tools such as n8n, Zapier, OpenAI, Claude Code, Airtable, and more."
},
"promptType": "define",
"hasOutputParser": true
},
"executeOnce": false,
"typeVersion": 2.2
},
{
"id": "98c0d280-0aca-48e8-aaaa-86be4e2bde45",
"name": "항목 루프",
"type": "n8n-nodes-base.splitInBatches",
"position": [
128,
-144
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "e8a4e7f4-b26a-4853-a23e-2710ce09cc51",
"name": "선택된 JD1 다운로드",
"type": "n8n-nodes-base.googleDrive",
"position": [
304,
0
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.jd_file_id }}"
},
"options": {
"googleFileConversion": {
"conversion": {
"docsToFormat": "application/pdf"
}
}
},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "IjoB5flCkLlcfjdH",
"name": "Google Drive account"
}
},
"typeVersion": 3
},
{
"id": "1e55c3ab-4dc4-42fa-990b-26600f5f4d9e",
"name": "파일에서 추출1",
"type": "n8n-nodes-base.extractFromFile",
"position": [
464,
0
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
},
{
"id": "eaea53b0-fd3e-4d16-bfd9-d6d7c93f3e0e",
"name": "Gemini 2.5 Flash",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
-736,
-80
],
"parameters": {
"options": {}
},
"credentials": {
"googlePalmApi": {
"id": "etClcv7ej0yswPTF",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "f0311480-1820-4229-89d6-87634e1aae32",
"name": "JD 파일 액세스",
"type": "n8n-nodes-base.googleDriveTool",
"position": [
-576,
-80
],
"parameters": {
"filter": {
"folderId": {
"__rl": true,
"mode": "list",
"value": "1UWI0TanlIGOec_d3S2HJzDymT-51BxHm",
"cachedResultUrl": "https://drive.google.com/drive/folders/1UWI0TanlIGOec_d3S2HJzDymT-51BxHm",
"cachedResultName": "Job Descriptions"
}
},
"options": {},
"resource": "fileFolder"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "IjoB5flCkLlcfjdH",
"name": "Google Drive account"
}
},
"typeVersion": 3
},
{
"id": "74d68403-76d4-4970-bd17-8bdeab243e8f",
"name": "다중 JD용 변환",
"type": "n8n-nodes-base.code",
"position": [
-96,
-176
],
"parameters": {
"jsCode": "// Transform the AI agent output for looping\nif ($json.output.match_type === \"cv_match\") {\n // Return each JD as a separate item for the loop\n return $json.output.cv_match.map(jd => ({\n jd_filename: jd.jd_filename,\n jd_file_id: jd.jd_file_id\n }));\n} else {\n // For email match, return single item\n return [{\n jd_filename: $json.output.email_match.jd_filename,\n jd_file_id: $json.output.email_match.jd_file_id\n }];\n}"
},
"typeVersion": 2
},
{
"id": "203a733c-9916-420e-91b1-e8704f666ed5",
"name": "JD 매칭 w/이메일?",
"type": "n8n-nodes-base.if",
"position": [
-304,
-272
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "dde6fee9-e053-49de-aacf-b63f33fabec3",
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.output.match_type }}",
"rightValue": "email_match"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "19dcdeaa-edf5-4a8d-8060-ebcd6cb32c8b",
"name": "스티키 노트6",
"type": "n8n-nodes-base.stickyNote",
"position": [
496,
-384
],
"parameters": {
"color": 2,
"width": 1296,
"height": 1216,
"content": ""
},
"typeVersion": 1
},
{
"id": "423054a7-88f1-410e-bc0e-c2801f706acf",
"name": "설정",
"type": "n8n-nodes-base.set",
"position": [
624,
0
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "c1376530-e0d7-4ce5-abb7-bd5839a34ecb",
"name": "jd_filename",
"type": "string",
"value": "={{ $('Loop Over Items').item.json.jd_filename }}"
},
{
"id": "85efbcf5-b293-4058-aef6-8a6dfbed1c0a",
"name": "jd_file_id",
"type": "string",
"value": "={{ $('Loop Over Items').item.json.jd_file_id }}"
},
{
"id": "63d88a73-7144-458f-812b-f53ccd8fda49",
"name": "text",
"type": "string",
"value": "={{ $json.text }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "4b7440a0-2382-4afb-ab4e-ca58146b1d56",
"name": "선택된 JD 형식으로 설정",
"type": "n8n-nodes-base.set",
"position": [
1552,
448
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "271dc7a2-e8c3-40f7-9e7c-04c4463490f0",
"name": "selected_jd_filename",
"type": "string",
"value": "={{ $('JD Match w/Email?').item.json.output.email_match.jd_filename }}"
},
{
"id": "2e987315-7b9b-47ca-a2ca-400978944b7a",
"name": "selected_jd_file_id",
"type": "string",
"value": "={{ $('JD Match w/Email?').item.json.output.email_match.jd_file_id }}"
},
{
"id": "f9d90d18-8193-46d0-a9b2-a3fe4edcd7e5",
"name": "selected_jd_text",
"type": "string",
"value": "={{ $json.text }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "e376226f-f68d-41f6-b38c-3268f5c4b970",
"name": "선택된 JD 이름과 전체 텍스트 매칭",
"type": "n8n-nodes-base.code",
"position": [
1296,
208
],
"parameters": {
"jsCode": "// Get the selected filename from the AI agent output\nconst selectedFilename = $json.output.selected_jd.jd_filename;\n\n// Get all the JD data from the loop output\nconst allJDs = $('Loop Over Items').all();\n\n// Find the matching JD by filename\nconst selectedJD = allJDs.find(jd => jd.json.jd_filename === selectedFilename);\n\nif (!selectedJD) {\n throw new Error(`No JD found with filename: ${selectedFilename}`);\n}\n\n// Return just the selected JD data\nreturn [{\n selected_jd_filename: selectedJD.json.jd_filename,\n selected_jd_file_id: selectedJD.json.jd_file_id,\n selected_jd_text: selectedJD.json.text\n}];"
},
"typeVersion": 2
},
{
"id": "642dac3e-e494-4f70-ae54-ea5dab98cea2",
"name": "Gemini 2.5 Pro-1",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
912,
160
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.5-pro"
},
"credentials": {
"googlePalmApi": {
"id": "etClcv7ej0yswPTF",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "450ddf83-b4ad-4834-9286-29b63c4552f6",
"name": "Gemini 2.5 Flash-1",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
-512,
672
],
"parameters": {
"options": {}
},
"credentials": {
"googlePalmApi": {
"id": "etClcv7ej0yswPTF",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "44889a9c-1185-4aaa-b14b-864a4599b2b7",
"name": "웹 링크 및 CV 텍스트 표준화 (PDF)",
"type": "n8n-nodes-base.set",
"position": [
-1168,
208
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "f7a58896-424f-46a5-8849-a75f89bf20b0",
"name": "cv_webviewlink",
"type": "string",
"value": "={{ $('Upload CV - PDF').item.json.webViewLink }}"
},
{
"id": "0e202a35-b3c6-4c2d-893e-ad986d00ef6c",
"name": "cv_text",
"type": "string",
"value": "={{ $json.text }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "8132c536-2de6-440c-8202-fc7cec6ceccb",
"name": "웹 링크 및 CV 텍스트 표준화 (GDoc)",
"type": "n8n-nodes-base.set",
"position": [
-1168,
-208
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "1c868154-561f-4897-8828-e12c66dba01b",
"name": "cv_webviewlink",
"type": "string",
"value": "={{ $('Get Web Link').item.json.webViewLink }}"
},
{
"id": "0a02aa8c-b464-44c1-a962-bbddcde33fa9",
"name": "cv_text",
"type": "string",
"value": "={{ $json.text }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "d8af1a95-21f4-4ee8-8de5-9a6387e2475f",
"name": "후보자 선별 확인 전송",
"type": "n8n-nodes-base.slack",
"position": [
32,
448
],
"webhookId": "4cf3a198-5cdd-495f-aad3-aa28f0441063",
"parameters": {
"select": "channel",
"blocksUi": "={\n\t\"blocks\": [\n\t\t{\n\t\t\t\"type\": \"section\",\n\t\t\t\"text\": {\n\t\t\t\t\"type\": \"mrkdwn\",\n\t\t\t\t\"text\": \"I've just completed the screening of a new candidate, who sent their CV moments ago. I've intelligently matched the candidate with the JD we have -- either through the candidate directly applying for this role, or selecting the best matched JD if they didn't apply for a specific role.\"\n\t\t\t}\n\t\t},\n\t\t{\n\t\t\t\"type\": \"section\",\n\t\t\t\"text\": {\n\t\t\t\t\"type\": \"mrkdwn\",\n\t\t\t\t\"text\": \"I've provided the following *Overall Fit* score and *Justification*:\"\n\t\t\t}\n\t\t},\n\t\t{\n\t\t\t\"type\": \"divider\"\n\t\t},\n\t\t{\n\t\t\t\"type\": \"table\",\n\t\t\t\"rows\": [\n\t\t\t\t[\n\t\t\t\t\t{\n\t\t\t\t\t\t\"type\": \"rich_text\",\n\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"type\": \"rich_text_section\",\n\t\t\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"type\": \"text\",\n\t\t\t\t\t\t\t\t\t\t\"text\": \"Name\",\n\t\t\t\t\t\t\t\t\t\t\"style\": {\n\t\t\t\t\t\t\t\t\t\t\t\"bold\": true\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t\"type\": \"rich_text\",\n\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"type\": \"rich_text_section\",\n\t\t\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"type\": \"text\",\n\t\t\t\t\t\t\t\t\t\t\"text\": \"CV\",\n\t\t\t\t\t\t\t\t\t\t\"style\": {\n\t\t\t\t\t\t\t\t\t\t\t\"bold\": true\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t\"type\": \"rich_text\",\n\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"type\": \"rich_text_section\",\n\t\t\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"type\": \"text\",\n\t\t\t\t\t\t\t\t\t\t\"text\": \"Job Role Matched\",\n\t\t\t\t\t\t\t\t\t\t\"style\": {\n\t\t\t\t\t\t\t\t\t\t\t\"bold\": true\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t\"type\": \"rich_text\",\n\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"type\": \"rich_text_section\",\n\t\t\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"type\": \"text\",\n\t\t\t\t\t\t\t\t\t\t\"text\": \"Overall Fit\",\n\t\t\t\t\t\t\t\t\t\t\"style\": {\n\t\t\t\t\t\t\t\t\t\t\t\"bold\": true\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t}\n\t\t\t\t],\n\t\t\t\t[\n\t\t\t\t\t{\n\t\t\t\t\t\t\"type\": \"rich_text\",\n\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"type\": \"rich_text_section\",\n\t\t\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"type\": \"text\",\n\t\t\t\t\t\t\t\t\t\t\"text\": \"{{ $json['First Name'] }} {{ $json['Last Name'] }}\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t\"type\": \"rich_text\",\n\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"type\": \"rich_text_section\",\n\t\t\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"type\": \"link\",\n\t\t\t\t\t\t\t\t\t\t\"url\": \"{{ $json.CV }}\",\n\t\t\t\t\t\t\t\t\t\t\"text\": \"CV - {{ $json['First Name'] }} {{ $json['Last Name'] }}\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t\"type\": \"rich_text\",\n\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"type\": \"rich_text_section\",\n\t\t\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"type\": \"text\",\n\t\t\t\t\t\t\t\t\t\t\"text\": \"{{ $json['JD Match'] }}\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t\"type\": \"rich_text\",\n\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"type\": \"rich_text_section\",\n\t\t\t\t\t\t\t\t\"elements\": [\n\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"type\": \"text\",\n\t\t\t\t\t\t\t\t\t\t\"text\": \"{{ $json['Overall Fit'] }}/10\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t}\n\t\t\t\t]\n\t\t\t]\n\t\t},\n\t\t{\n\t\t\t\"type\": \"divider\"\n\t\t},\n\t\t{\n\t\t\t\"type\": \"context\",\n\t\t\t\"elements\": [\n\t\t\t\t{\n\t\t\t\t\t\"type\": \"mrkdwn\",\n\t\t\t\t\t\"text\": \"_Submission ID: {{ $json['Submission ID'] }}_\"\n\t\t\t\t}\n\t\t\t]\n\t\t},\n\t\t{\n\t\t\t\"type\": \"section\",\n\t\t\t\"text\": {\n\t\t\t\t\"type\": \"mrkdwn\",\n\t\t\t\t\"text\": \"<https://docs.google.com/spreadsheets/d/YOUR_GOOGLE_SHEET_ID/edit?usp=sharing|View the full analysis in the AI Candidate Screening sheet>\"\n\t\t\t}\n\t\t},\n\t\t{\n\t\t\t\"type\": \"actions\",\n\t\t\t\"elements\": [\n\t\t\t\t{\n\t\t\t\t\t\"type\": \"button\",\n\t\t\t\t\t\"text\": {\n\t\t\t\t\t\t\"type\": \"plain_text\",\n\t\t\t\t\t\t\"text\": \"Proceed w/Candidate\",\n\t\t\t\t\t\t\"emoji\": true\n\t\t\t\t\t},\n\t\t\t\t\t\"value\": \"{\\\"action\\\":\\\"proceed_candidate\\\",\\\"submission_id\\\":\\\"{{ $json['Submission ID'] }}\\\",\\\"candidate_name\\\":\\\"{{ $json['First Name'] }} {{ $json['Last Name'] }}\\\",\\\"email\\\":\\\"{{ $json.Email }}\\\",\\\"cv_url\\\":\\\"{{ $json.CV }}\\\",\\\"jd_match\\\":\\\"{{ $json['JD Match'] }}\\\",\\\"overall_fit\\\":\\\"{{ $json['Overall Fit'] }}\\\",\\\"first_name\\\":\\\"{{ $json['First Name'] }}\\\",\\\"last_name\\\":\\\"{{ $json['Last Name'] }}\\\"}\",\n\t\t\t\t\t\"action_id\": \"proceed_action\",\n\t\t\t\t\t\"style\": \"primary\",\n\t\t\t\t\t\"confirm\": {\n\t\t\t\t\t\t\"title\": {\n\t\t\t\t\t\t\t\"type\": \"plain_text\",\n\t\t\t\t\t\t\t\"text\": \"Confirm Proceed\"\n\t\t\t\t\t\t},\n\t\t\t\t\t\t\"text\": {\n\t\t\t\t\t\t\t\"type\": \"mrkdwn\",\n\t\t\t\t\t\t\t\"text\": \"Are you sure you want to **proceed** with this candidate?\\n\\nThis will:\\n• Move them to the next interview stage\\n• Update the screening spreadsheet\\n• Notify the hiring team\"\n\t\t\t\t\t\t},\n\t\t\t\t\t\t\"confirm\": {\n\t\t\t\t\t\t\t\"type\": \"plain_text\",\n\t\t\t\t\t\t\t\"text\": \"Yes, Proceed\"\n\t\t\t\t\t\t},\n\t\t\t\t\t\t\"deny\": {\n\t\t\t\t\t\t\t\"type\": \"plain_text\",\n\t\t\t\t\t\t\t\"text\": \"Cancel\"\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"type\": \"button\",\n\t\t\t\t\t\"text\": {\n\t\t\t\t\t\t\"type\": \"plain_text\",\n\t\t\t\t\t\t\"text\": \"Reject Candidate\",\n\t\t\t\t\t\t\"emoji\": true\n\t\t\t\t\t},\n\t\t\t\t\t\"value\": \"{\\\"action\\\":\\\"reject_candidate\\\",\\\"submission_id\\\":\\\"{{ $json['Submission ID'] }}\\\",\\\"candidate_name\\\":\\\"{{ $json['First Name'] }} {{ $json['Last Name'] }}\\\",\\\"email\\\":\\\"{{ $json.Email }}\\\",\\\"cv_url\\\":\\\"{{ $json.CV }}\\\",\\\"jd_match\\\":\\\"{{ $json['JD Match'] }}\\\",\\\"overall_fit\\\":\\\"{{ $json['Overall Fit'] }}\\\",\\\"first_name\\\":\\\"{{ $json['First Name'] }}\\\",\\\"last_name\\\":\\\"{{ $json['Last Name'] }}\\\"}\",\n\t\t\t\t\t\"action_id\": \"reject_action\",\n\t\t\t\t\t\"style\": \"danger\",\n\t\t\t\t\t\"confirm\": {\n\t\t\t\t\t\t\"title\": {\n\t\t\t\t\t\t\t\"type\": \"plain_text\",\n\t\t\t\t\t\t\t\"text\": \"Confirm Rejection\"\n\t\t\t\t\t\t},\n\t\t\t\t\t\t\"text\": {\n\t\t\t\t\t\t\t\"type\": \"mrkdwn\",\n\t\t\t\t\t\t\t\"text\": \"Are you sure you want to **reject** this candidate?\\n\\nThis will:\\n• Mark them as rejected in the system\\n• Update the screening spreadsheet\\n• Send the candidate a friendly rejection email\\n• This action cannot be undone\"\n\t\t\t\t\t\t},\n\t\t\t\t\t\t\"confirm\": {\n\t\t\t\t\t\t\t\"type\": \"plain_text\",\n\t\t\t\t\t\t\t\"text\": \"Yes, Reject\"\n\t\t\t\t\t\t},\n\t\t\t\t\t\t\"deny\": {\n\t\t\t\t\t\t\t\"type\": \"plain_text\",\n\t\t\t\t\t\t\t\"text\": \"Cancel\"\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t]\n\t\t}\n\t]\n}\n",
"channelId": {
"__rl": true,
"mode": "list",
"value": "C09G7DR7X52",
"cachedResultName": "talent"
},
"messageType": "block",
"otherOptions": {
"includeLinkToWorkflow": false
},
"authentication": "oAuth2"
},
"credentials": {
"slackOAuth2Api": {
"id": "qXwPqfkNoVV9HIr1",
"name": "TidyCurve DJP"
}
},
"typeVersion": 2.3
},
{
"id": "5a0b4b2c-1c67-4fd1-b72d-d047ad727cb5",
"name": "채용 담당자 평가 에이전트",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-832,
448
],
"parameters": {
"text": "=Candidate's CV (Resume):\n{{ $('Standardize').first().json.cv_text }}\n",
"options": {
"systemMessage": "=# Overview\nYou are an expert sales and technical recruiter specializing in AI, automation, and software roles. You have been given a job description and a candidate resume. Your task is to analyze the resume in relation to the job description and provide a detailed screening report.\n\nFocus specifically on how well the candidate matches the core requirements and ideal profile outlined in the job description. Evaluate both technical skill alignment and business-context understanding. Use reasoning grounded in the actual content of the resume and job post – avoid making assumptions.\n\n## Output\nYour output should follow this exact format:\n\nJob Description Matched:\nA simple direct copy of {{ $json.selected_jd_filename }}\n\nCandidate Strengths:\nList the top strengths or relevant qualifications the candidate brings to the table. Be specific.\n\nCandidate Weaknesses:\nList areas where the candidate is lacking or mismatched based on the job description.\n\nRisk Factor:\n- Assign a risk score (Low / Medium / High)\n- Explain the worst-case scenario if this candidate is hired.\n\nReward Factor:\n- Assign a reward score (Low / Medium / High)\n- Describe the best-case scenario – what value could this candidate unlock?\n- Does the candidate appear to be a short-term or long-term fit?\n\nOverall Fit Rating (0–10):\nAssign a number between 0 (terrible match) and 10 (perfect match). Do not give decimals.\n\nJustification for Rating:\nExplain clearly why this candidate received that score. Reference specific resume content and how it aligns or doesn't with the job description.\n\n\n## Job Description\n\nFilename: {{ $json.selected_jd_filename }}\n\nJD content:\n{{ $json.selected_jd_text }}\n"
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 2.2
},
{
"id": "37f0cded-a0bd-4f8c-9512-54cd1312d171",
"name": "Gemini 2.5 Pro-2",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
-848,
672
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.5-pro"
},
"credentials": {
"googlePalmApi": {
"id": "etClcv7ej0yswPTF",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "6b175b49-903b-4682-b610-1bfaa97c9623",
"name": "스티키 노트7",
"type": "n8n-nodes-base.stickyNote",
"position": [
-3840,
-880
],
"parameters": {
"color": 7,
"width": 912,
"height": 1536,
"content": "# **First-Round Fast Track AI Recruiter Assistant**\n *CV → Match → Screen → Decide, all automated*\n\n This workflow automatically processes candidate CVs from email, intelligently matches them to job descriptions, performs\n AI-powered screening analysis, and sends actionable summaries to your team in Slack.\n\n **Good to know**\n - Handles both PDF and Word document CVs automatically\n - Two-stage JD matching: prioritizes role mentioned in candidate's email, falls back to CV analysis if needed\n - Uses Google Gemini API for AI screening (generous free tier and rate limits, typically enough to avoid paying for API requests, but check latest pricing at [Google AI Pricing](https://ai.google.dev/pricing))\n - All CVs stored in Google Drive with standardized naming (candidate name + date/time)\n - Complete audit trail logged in Google Sheets\n\n ## Who's it for\n Hiring teams and recruiters who want to automate first-round CV screening while maintaining quality. Perfect for companies \n receiving high volumes of applications across multiple roles, especially in tech, sales, or automation-focused positions.\n\n ## How it works\n 1. Gmail monitors inbox for CVs with specific label and downloads attachments\n 2. Detects file type (PDF or Word) and converts/standardizes format for text extraction\n 3. AI agent matches candidate to best-fit job description by analyzing email context first (if candidate mentioned a role), or \n CV content as fallback (selects up to 3 potential JD matches)\n 4. If multiple JDs matched, second AI agent selects the single best fit\n 5. AI recruiter agent analyzes CV against selected JD and generates structured screening report (strengths, weaknesses,\n risk/reward factors, overall fit score 0-10 with justification)\n 6. Extracts candidate details (name, email) from CV text\n 7. Logs complete analysis to Google Sheets tracker\n 8. Sends formatted summary to Slack with Proceed/Reject action buttons for instant team decisions\n\n ## Requirements\n - Gmail account with API access\n - Google Drive account (OAuth2)\n - Google Sheets account (OAuth2)\n - Slack workspace with bot permissions\n - Google Gemini API key ([Get free key here](https://makersuite.google.com/app/apikey))\n - Google Drive folders: one for CVs, one for Job Descriptions (as PDFs or Google Docs)\n\n ## How to set up\n 1. Add credentials: Gmail OAuth2, Google Drive OAuth2, Google Sheets OAuth2, Slack OAuth2, Google Gemini API\n 2. Create Gmail label (e.g., \"CV-Screening\") for incoming candidate emails\n 3. In \"Receive CV via Email\" node: select your Gmail label for filtering\n 4. Create two Google Drive folders: \"Candidate CVs\" and \"Job Descriptions\"\n 5. In \"Upload CV - PDF\" and \"Stream Doc/Docx File\" nodes: update folder ID to your \"Candidate CVs\" folder\n 6. In \"Access JD Files\" node: update folder ID to your \"Job Descriptions\" folder\n 7. Create Google Sheet named \"AI Candidate Screening\" with columns matching the [sample AI Candidate Screening sheet](https://docs.google.com/spreadsheets/d/16HebkHqsM2ZE_IdJzQk1mDE3i2-HwsUqa5gEwXaF-7A/edit?usp=sharing) \n 8. In \"Append row in sheet\" node: select your Google Sheet\n 9. In \"Send Candidate Screening Confirmation\" node: select your Slack channel and enter your Google Sheet ID in the Blocks section\n 10. Activate workflow\n\n ## Customizing this workflow\n - **Change JD matching logic**: Edit \"JD Matching Agent\" node prompt to adjust how CVs are matched to roles (e.g., weight\n technical skills vs. experience).\n - **Change \"Company Description\" in AI prompts**: Insert your \"Company Description\" in System Message sections in \"JD Matching Agent\" and \"Detailed JD Matching Agent\" nodes\n - **Modify screening criteria**: Edit \"Recruiter Scoring Agent\" node system message to focus on specific qualities (culture \n fit, leadership, technical depth, etc.)\n - **Add more storage locations**: Add nodes to save CVs to other systems (Notion, Airtable, ATS platforms)\n - **Customize Slack message**: Edit \"Send Candidate Screening Confirmation\" node to change formatting, add more context, or \n include additional candidate data\n - **Auto-proceed logic**: Add IF node after screening to auto-proceed candidates with fit score above threshold (e.g., 8+/10) \n - **Add email responses**: Connect nodes to automatically email candidates (confirmation, rejection, interview invite)\n - **Add human-in-the-loop**: Sub-workflow triggered by Slack response or email response, to update Sheet with approve/reject status\n- **Add candidate email responses + interview scheduling**: For approved candidates, trigger email to candidate with Cal.com or Calendly link so they can book their interview"
},
"typeVersion": 1
},
{
"id": "3fc25e0b-d3a9-428b-8d75-a684a397301a",
"name": "스티키 노트4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2896,
464
],
"parameters": {
"color": 7,
"width": 544,
"height": 192,
"content": "## Acknowledgments\n This workflow was inspired by [Nate Herk's YouTube demonstration](https://www.youtube.com/watch?v=M0s6O8xtVUE) on building a resume analysis system. This implementation builds upon that foundation by adding dynamic job description matching (initial + detailed JD matching agents), Slack Block Kit integration with interactive buttons, updated Google Drive API methods for document handling, and enhanced candidate data capture in Google Sheets."
},
"typeVersion": 1
},
{
"id": "5d1d64c0-df2b-4b9d-b7af-46ae3619e5d9",
"name": "스티키 노트8",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2896,
-880
],
"parameters": {
"color": 7,
"width": 864,
"height": 464,
"content": " ## Quick Troubleshooting\n * **No CVs being processed**: Check Gmail label is correctly set in \"Receive CV via Email\" node and emails are being labeled \n * **Word documents failing**: Verify \"Stream Doc/Docx File\" node has correct parent folder ID and Google Drive credentials \n authorized\n * **JD matching returns no results**: Check \"Access JD Files\" node folder ID points to your Job Descriptions folder, and JD \n files are named clearly (e.g., \"Marketing Director JD.pdf\")\n * **JD matching is not relevant for my company**: Update the \"Company Description\" in the System Messages in the \"JD Matching Agent\" and \"Detailed JD Matching Agent\" nodes\n * **\"Can't find matching JD\"**: Ensure candidate's email mentions role name OR their CV clearly indicates relevant experience \n for available JDs\n * **Google Sheets errors**: Verify sheet name is \"AI Candidate Screening\" and column headers exactly match workflow\n expectations (Submission ID, Date, CV, First Name, etc.)\n * **Slack message not appearing**: Re-authorize Slack credentials and confirm channel ID in \"Send Candidate Screening\n Confirmation\" node\n * **Missing candidate name/email**: CV text must be readable - check PDF extraction quality or try converting complex CVs to \n simpler format\n * **401/403 API errors**: Re-authorize all OAuth2 credentials (Gmail, Google Drive, Google Sheets, Slack)\n * **AI analysis quality issues**: Edit system prompts in \"JD Matching Agent\" and \"Recruiter Scoring Agent\" nodes to refine \n screening criteria"
},
"typeVersion": 1
},
{
"id": "c3bcef58-069f-4193-b6d9-f3cafdb30677",
"name": "구조화된 출력 파서-1",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
-416,
-80
],
"parameters": {
"schemaType": "manual",
"inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"match_type\": {\n \"type\": \"string\",\n \"enum\": [\"email_match\", \"cv_match\"],\n \"description\": \"Whether the match was found in email or requires CV analysis\"\n },\n \"email_match\": {\n \"type\": \"object\",\n \"properties\": {\n \"jd_filename\": {\n \"type\": \"string\",\n \"description\": \"Exact filename of the matched JD (PDF or Google Doc)\"\n },\n \"jd_file_id\": {\n \"type\": \"string\",\n \"description\": \"Google Drive file ID for downloading the JD file\"\n },\n \"confidence\": {\n \"type\": \"string\",\n \"enum\": [\"high\", \"medium\", \"low\"],\n \"description\": \"Confidence level of the email-based match\"\n }\n },\n \"required\": [\"jd_filename\", \"jd_file_id\", \"confidence\"]\n },\n \"cv_match\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"jd_filename\": {\n \"type\": \"string\",\n \"description\": \"JD filename to download and analyze (PDF or Google Doc)\"\n },\n \"jd_file_id\": {\n \"type\": \"string\",\n \"description\": \"Google Drive file ID for downloading the JD file\"\n }\n },\n \"required\": [\"jd_filename\", \"jd_file_id\"]\n },\n \"minItems\": 1,\n \"maxItems\": 3,\n \"description\": \"Up to a maximum of 3 JD files for detailed analysis, but 1-2 JD files is also fine if you do not think there are 3 JD files relevant\"\n }\n },\n \"required\": [\"match_type\"],\n \"oneOf\": [\n {\n \"properties\": {\n \"match_type\": {\"const\": \"email_match\"}\n },\n \"required\": [\"email_match\"]\n },\n {\n \"properties\": {\n \"match_type\": {\"const\": \"cv_match\"} \n },\n \"required\": [\"cv_match\"]\n }\n ]\n}"
},
"typeVersion": 1.3
},
{
"id": "3977e0ec-5ba4-4a31-94c3-1efb5d3e2e2c",
"name": "구조화된 출력 파서-3",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
-672,
672
],
"parameters": {
"schemaType": "manual",
"inputSchema": "{\n \"name\": \"resume_screening_evaluation\",\n \"description\": \"Analyzes a candidate's resume against a job description and outputs strengths, weaknesses, risk/reward assessment, and an overall fit score.\",\n \"type\": \"object\",\n \"properties\": {\n \"candidate_strengths\": {\n \"type\": \"array\",\n \"description\": \"A list of specific strengths or qualifications that match the job description.\",\n \"items\": {\n \"type\": \"string\"\n }\n },\n \"candidate_weaknesses\": {\n \"type\": \"array\",\n \"description\": \"A list of areas where the candidate falls short or lacks alignment with the job requirements.\",\n \"items\": {\n \"type\": \"string\"\n }\n },\n \"risk_factor\": {\n \"type\": \"object\",\n \"description\": \"An evaluation of the potential risks of hiring this candidate.\",\n \"properties\": {\n \"score\": {\n \"type\": \"string\",\n \"enum\": [\"Low\", \"Medium\", \"High\"],\n \"description\": \"The risk level of hiring this candidate.\"\n },\n \"explanation\": {\n \"type\": \"string\",\n \"description\": \"A brief explanation of the worst-case scenario if the candidate is hired.\"\n }\n },\n \"required\": [\"score\", \"explanation\"]\n },\n \"reward_factor\": {\n \"type\": \"object\",\n \"description\": \"An evaluation of the potential upside of hiring this candidate.\",\n \"properties\": {\n \"score\": {\n \"type\": \"string\",\n \"enum\": [\"Low\", \"Medium\", \"High\"],\n \"description\": \"The reward level of hiring this candidate.\"\n },\n \"explanation\": {\n \"type\": \"string\",\n \"description\": \"A description of the best-case scenario and whether the candidate is a short-term or long-term fit.\"\n }\n },\n \"required\": [\"score\", \"explanation\"]\n },\n \"selected_jd_filename\": {\n \"type\": \"string\",\n \"description\": \"The name of the job description file you are comparing the candidate's CV with.\"\n },\n \"overall_fit_rating\": {\n \"type\": \"integer\",\n \"description\": \"A rating from 0 to 10 indicating how well the candidate matches the job description.\",\n \"minimum\": 0,\n \"maximum\": 10\n },\n \"justification_for_rating\": {\n \"type\": \"string\",\n \"description\": \"A summary explaining why the candidate received the specific fit rating, referencing resume and job description alignment.\"\n }\n },\n \"required\": [\n \"candidate_strengths\",\n \"candidate_weaknesses\",\n \"risk_factor\",\n \"reward_factor\",\n \"overall_fit_rating\",\n \"justification_for_rating\",\n \"selected_jd_filename\"\n ]\n}"
},
"typeVersion": 1.3
},
{
"id": "dfadca1a-95df-4d34-ae7b-3f17192aad61",
"name": "구조화된 출력 파서-2",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
1104,
160
],
"parameters": {
"schemaType": "manual",
"inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"selected_jd\": {\n \"type\": \"object\",\n \"properties\": {\n \"jd_filename\": {\n \"type\": \"string\",\n \"description\": \"Exact filename of the selected job description\"\n }\n },\n \"required\": [\"jd_filename\"]\n }\n },\n \"required\": [\"selected_jd\"]\n}"
},
"typeVersion": 1.3
},
{
"id": "1f546e0e-264d-41bd-a7a4-f22607ecc53d",
"name": "스티키 노트5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2000,
-864
],
"parameters": {
"color": 7,
"width": 960,
"height": 448,
"content": "## Sample Outputs\n- [Google Sheets - AI Candidate Screening - sample](https://docs.google.com/spreadsheets/d/16HebkHqsM2ZE_IdJzQk1mDE3i2-HwsUqa5gEwXaF-7A/edit?usp=sharing)\n"
},
"typeVersion": 1
},
{
"id": "51a70bcd-6c2e-4f4f-b35b-7ced5501e505",
"name": "스티키 노트9",
"type": "n8n-nodes-base.stickyNote",
"position": [
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],
"parameters": {
"color": 7,
"width": 640,
"height": 400,
"content": ""
},
"typeVersion": 1
},
{
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"name": "스티키 노트10",
"type": "n8n-nodes-base.stickyNote",
"position": [
-432,
-848
],
"parameters": {
"color": 7,
"width": 800,
"height": 432,
"content": "- Slack confirmation message\n"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"callerPolicy": "workflowsFromSameOwner",
"errorWorkflow": "orVeCnK9KH8GxfmT",
"executionOrder": "v1"
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"versionId": "7d2443dc-a590-4148-ac8e-14b5643cde3d",
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}자주 묻는 질문
이 워크플로우를 어떻게 사용하나요?
위의 JSON 구성 코드를 복사하여 n8n 인스턴스에서 새 워크플로우를 생성하고 "JSON에서 가져오기"를 선택한 후, 구성을 붙여넣고 필요에 따라 인증 설정을 수정하세요.
이 워크플로우는 어떤 시나리오에 적합한가요?
고급
유료인가요?
이 워크플로우는 완전히 무료이며 직접 가져와 사용할 수 있습니다. 다만, 워크플로우에서 사용하는 타사 서비스(예: OpenAI API)는 사용자 직접 비용을 지불해야 할 수 있습니다.
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Dean Pike
@deanjpSaving 20+ hours weekly for growing companies by putting their client-facing and back-office operations on autopilot. As the Founder of TidyCurve, we build AI agents and workflow automations that replace critical repetitive work: from lead generation and customer support, to marketing, recruitment, and onboarding. We deploy scalable solutions in 4-8 weeks at a fraction of enterprise costs - backed by a 60-day 3x ROI guarantee.
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