{ "name": "Resume AI Analyzer", "nodes": [ { "id": "0f5ac547-cb10-4e88-9421-b8d62be2c107", "name": "Gmail Trigger", "type": "n8n-nodes-base.gmailTrigger", "typeVersion": 1.2, "position": [ 0, 300 ], "parameters": { "pollTimes": { "item": [ { "mode": "everyMinute" } ] }, "simple": false, "filters": { "q": "has:attachment filename:pdf resume" }, "options": { "downloadAttachments": true, "dataPropertyAttachmentsPrefixName": "cv" } } }, { "id": "e4c4e751-9f0e-4c8d-b928-13165c9b2a58", "name": "Upload file", "type": "n8n-nodes-base.googleDrive", "typeVersion": 3, "position": [ 220, 300 ], "parameters": { "inputDataFieldName": "cv0", "name": "={{ $json.from.value[0].name }}", "driveId": { "__rl": true, "value": "My Drive", "mode": "id" }, "folderId": { "__rl": true, "value": "YOUR_DRIVE_FOLDER_ID", "mode": "id" }, "options": {} } }, { "id": "ef22d20f-453a-4c3f-950a-2127718ff7fd", "name": "Google Drive", "type": "n8n-nodes-base.googleDrive", "typeVersion": 3, "position": [ 440, 300 ], "parameters": { "operation": "download", "fileId": { "__rl": true, "value": "YOUR_FILE_ID", "mode": "id" }, "options": {} } }, { "id": "00abb4c7-46ea-43f+10000000000bd239b5", "name": "Extract from File", "type": "n8n-nodes-base.extractFromFile", "typeVersion": 1, "position": [ 660, 300 ], "parameters": { "operation": "pdf", "binaryPropertyName": "data", "options": { "keepSource": "json" } } }, { "id": "7e975e3f-ad10-46bd-867d-7e8fa079008f", "name": "Information Extractor", "type": "@n8n/n8n-nodes-langchain.informationExtractor", "typeVersion": 1.1, "position": [ 880, 300 ], "parameters": { "text": "={{ $json.text }}", "schemaType": "manual", "inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"name\": {\n \"type\": \"string\"\n },\n \"email\": {\n \"type\": \"string\",\n \"format\": \"email\"\n },\n \"phone_number\": {\n \"type\": \"string\",\n \"pattern\": \"^(\\\\+\\\\d{1,3}[- ]?)?\\\\d{10}$\"\n }\n },\n \"required\": [\"name\", \"email\", \"phone_number\"]\n}", "options": {} } }, { "id": "313ed47d-b272-4251-abaf-61e845c461ca", "name": "OpenAI Chat Model2", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "typeVersion": 1.2, "position": [ 1000, 560 ], "parameters": { "model": { "__rl": true, "value": "gpt-4o", "mode": "list", "cachedResultName": "gpt-4o" }, "options": {} } }, { "id": "eeb8fccf-927e-44b5-bf19-2a60360b212e", "name": "AI Agent", "type": "@n8n/n8n-nodes-langchain.agent", "typeVersion": 1.9, "position": [ 1200, 300 ], "parameters": { "promptType": "define", "text": "=Resume: {{ $('Extract from File').item.json.text }}", "options": { "systemMessage": "** Purpose **\nYou are a CV Evaluation and Summarization Agent. Your task is to extract key\ninformation from a CV and output it in a clean, structured format. You must also\nevaluate how well the candidate fits a specified job role and assign a relevance\nscore (1-10), followed by a short justification.\n\n** How It Works **\nWhen provided with a CV and job description, follow this process:\n\n1. Extract & Summarize Information from the CV\n\n- Educational Qualifications\nInclude degrees, institutions, and graduation years.\n\n- Job History\nList job titles, company names, employment dates, and 1 short sentence\nsummarizing key responsibilities or achievements.\n\n- Skill Set\nList technical, soft, or role-specific skills - separated by commas.\n\n2. Evaluate the Candidate for the Job Role\n\n- Compare the candidate's qualifications and experience with the job requirements.\n- Assign a score from 1-10 based on relevance:\n\n1-3: Weak match\n\n4-6: Moderate match\n\n7-8: Strong match\n\n9-10: Excellent match\n\n- Provide a brief justification for the score. Mention strengths and gaps\ndirectly related to the role - no assumptions or interpretations.\n\n** Output Format (follow EXACTLY - the Code node depends on it) **\n\n**Educational Qualifications:**\n\n\n**Job History:**\n\n\n**Skill Set:**\n\n\n**Evaluation**\n**Relevance Score:** X/10\n**Justification:** \n\nAlways write the score as a whole number followed by /10, for example: **Relevance Score:** 7/10\n\n** Job Role Being Hired For **\nJob Title: Junior AI Automation Engineer\nRequired: Bachelor's degree in CS, SE, IT or a related field.\nRequired skills: Python, REST APIs, JSON, prompt engineering, n8n or\n any low-code automation platform, Git.\nNice to have: LangChain, vector databases, SQL, cloud deployment (AWS/GCP).\nExperience: 0-2 years. Internships and personal projects count.\n\nScore the candidate ONLY against the requirements listed above." } } }, { "id": "06d6fcfb-5107-4361-a231-9cf2688d031c", "name": "Edit Fields", "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ 1520, 300 ], "parameters": { "assignments": { "assignments": [ { "id": "71f22941-66a5-44d0-939f-0e9e08e6481c", "name": "content", "value": "={{ $json.output }}", "type": "string" } ] }, "options": {} } }, { "id": "0838f4cb-dbc8-4dfe-a356-fb12e3c56ca6", "name": "Code", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 1740, 300 ], "parameters": { "mode": "runOnceForEachItem", "jsCode": "// ---------- 1. Read input text ----------\nconst inputData = $input.item.json.content;\n\n// ---------- 2. Helper: extract a block that starts with a bold header ----------\nconst grabBlock = (text, header) => {\n const re = new RegExp(`\\\\*{0,2}${header}\\\\*{0,2}\\\\s*([\\\\s\\\\S]*?)\\\\s*$`, 'i');\n const m = re.exec(text);\n return m ? m[1].trim() : null;\n};\n\n// ---------- 3. Helper: extract a section that sits between bold headers ----------\nconst grabSection = (text, label) => {\n /* takes \"**Label:** ...\" up to the next **Bold Header** or end */\n const re = new RegExp(\n `\\\\*{0,2}${label}\\\\*{0,2}\\\\s*([\\\\s\\\\S]*?)(?=\\\\n\\\\*{0,2}[A-Z][^\\\\n]*\\\\*{0,2}\\\\s*|$)`,\n 'i'\n );\n const m = re.exec(text);\n return m ? m[1].trim() : null;\n};\n\n// ---------- 4. Pull the three simple sections ----------\nconst educationalQualifications = grabSection(inputData, 'Educational Qualifications:');\nconst jobHistory = grabSection(inputData, 'Job History:');\nconst skillSet = grabSection(inputData, 'Skill Set:');\n\n// ---------- 5. Pull the whole \"Evaluation ...\" block ----------\nconst evaluationBlock = grabBlock(inputData, 'Evaluation');\n\n// ---------- 6. From that block, get score & justification ----------\nlet score = null;\nlet justification = null;\n\nif (evaluationBlock) {\n // score can be \"Relevance Score: 7/10\" or \"Score: 7/10\"\n const scoreMatch = /(Relevance\\s*)?Score:\\**\\s*(\\d{1,2})\\s*\\/\\s*10/i.exec(evaluationBlock);\n score = scoreMatch ? scoreMatch[2] : null;\n\n const justMatch = /\\*{0,2}Justification:\\*{0,2}\\s*([\\s\\S]*)/i.exec(evaluationBlock);\n if (justMatch) {\n justification = justMatch[1].trim();\n }\n}\n\n// ---------- 7. Return tidy JSON ----------\n// Mode is \"Run Once for Each Item\", so return ONE object (not an array).\nreturn {\n educationalQualifications,\n jobHistory,\n skillSet,\n score,\n justification\n};" } }, { "id": "87bbd5f0-45fd-4104-83f7-29c8ecf288eb", "name": "Append row in sheet", "type": "n8n-nodes-base.googleSheets", "typeVersion": 4.6, "position": [ 1960, 300 ], "parameters": { "operation": "append", "documentId": { "__rl": true, "value": "YOUR_GOOGLE_SHEET_ID", "mode": "id" }, "sheetName": { "__rl": true, "value": "Sheet1", "mode": "name" }, "columns": { "mappingMode": "defineBelow", "value": { "Name": "={{ $('Information Extractor').item.json.output.name }}", "Email": "={{ $('Information Extractor').item.json.output.email }}", "Number": "={{ $('Information Extractor').item.json.output.phone_number }}", "Skill Set": "={{ $json.skillSet }}", "Qualifications": "={{ $json.educationalQualifications }}", "Job History": "={{ $json.jobHistory }}", "Score": "={{ $json.score }}", "Justification": "={{ $json.justification }}" }, "matchingColumns": [], "schema": [ { "id": "Name", "displayName": "Name", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "Email", "displayName": "Email", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "Number", "displayName": "Number", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "Skill Set", "displayName": "Skill Set", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "Qualifications", "displayName": "Qualifications", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "Job History", "displayName": "Job History", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "Score", "displayName": "Score", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "Justification", "displayName": "Justification", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true } ], "attemptToConvertTypes": false, "convertFieldsToString": false }, "options": {} } }, { "id": "5663dab4-8a2e-4a48-ae4a-26179da29224", "name": "Score >= 4?", "type": "n8n-nodes-base.if", "typeVersion": 2.2, "position": [ 2180, 300 ], "parameters": { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "loose", "version": 2 }, "conditions": [ { "id": "9bbcc15f-445d-4685-9230-b0523f6ff7bc", "leftValue": "={{ $('Code').item.json.score }}", "rightValue": 4, "operator": { "type": "number", "operation": "gte" } } ], "combinator": "and" }, "looseTypeValidation": true, "options": {} } }, { "id": "1fc4f30e-c77e-4167-9a48-b4f115a5e0eb", "name": "Build Email Content", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 2420, 180 ], "parameters": { "mode": "runOnceForEachItem", "jsCode": "// =============================================================\n// BUILD EMAIL CONTENT\n// Runs AFTER \"Append row in sheet\" (true branch of the score guard).\n// Reads the parsed evaluation from the \"Code\" node and the\n// contact details from the \"Information Extractor\" node.\n// =============================================================\n\n// ---------- 1. Pull data from the earlier nodes ----------\nconst evalData = $('Code').item.json;\nconst extracted = $('Information Extractor').item.json.output || {};\nconst sender = $('Gmail Trigger').item.json.from?.value?.[0] || {};\n\n// ---------- 2. Decide who we are replying to ----------\n// Prefer the address printed on the CV, fall back to the address\n// the email was actually sent from.\nconst toEmail = extracted.email || sender.address;\nconst toName = extracted.name || sender.name || 'Candidate';\n\nif (!toEmail) {\n throw new Error('No recipient email address could be found.');\n}\n\n// ---------- 3. Normalise the score ----------\nconst parsed = Number(evalData.score);\nconst score = Number.isFinite(parsed) ? parsed : 0;\n\n// ---------- 4. Pick a verdict band ----------\nlet band, verdict;\nif (score >= 9) {\n band = 'Excellent match';\n verdict = 'Your profile is a very strong fit for this role. Our team will ' +\n 'contact you shortly to arrange an interview.';\n} else if (score >= 7) {\n band = 'Strong match';\n verdict = 'Your profile aligns well with this role and has been shortlisted ' +\n 'for further review.';\n} else if (score >= 4) {\n band = 'Moderate match';\n verdict = 'Your profile matches several of our requirements. We will keep ' +\n 'your CV on file for suitable openings.';\n} else {\n band = 'Limited match';\n verdict = 'Your profile does not closely match this particular role, but we ' +\n 'encourage you to apply for future openings.';\n}\n\n// ---------- 5. Helpers that keep the HTML safe ----------\nconst esc = (v) => String(v ?? '')\n .replace(/&/g, '&')\n .replace(//g, '>');\n\nconst nl2br = (v) => esc(v).replace(/\\n/g, '
');\n\n// ---------- 6. Build the subject line ----------\nconst subject = `Your Application Review - Score ${score}/10 (${band})`;\n\n// ---------- 7. Build the HTML body ----------\nconst htmlBody = `\n
\n\n

Application Review Summary

\n

\n Automated screening report - Junior AI Automation Engineer\n

\n\n

Hi ${esc(toName)},

\n\n

Thank you for sending us your CV. Our screening system has finished\n reviewing it. Here is a summary of the result.

\n\n
\n
\n ${score}/10\n
\n
${esc(band)}
\n
\n\n

${esc(verdict)}

\n\n

\n Why you received this score\n

\n

${nl2br(evalData.justification)}

\n\n

\n What we read from your CV\n

\n \n \n \n \n \n \n \n \n \n \n \n \n \n
Qualifications\n ${nl2br(evalData.educationalQualifications)}
Job History\n ${nl2br(evalData.jobHistory)}
Skill Set\n ${nl2br(evalData.skillSet)}
\n\n

Best regards,
\n The Recruitment Team

\n\n
\n

\n This message was generated automatically. If any detail above looks wrong,\n simply reply to this email and a human will review it.\n

\n
\n`;\n\n// ---------- 8. Hand everything to the Gmail node ----------\n// Mode is \"Run Once for Each Item\", so return ONE object.\nreturn { toEmail, toName, subject, htmlBody, score, band };" } }, { "id": "3180df67-f51a-44f3-83ff-ecdfc8d55d8c", "name": "Send Email", "type": "n8n-nodes-base.gmail", "typeVersion": 2.1, "position": [ 2640, 180 ], "parameters": { "sendTo": "={{ $json.toEmail }}", "subject": "={{ $json.subject }}", "emailType": "html", "message": "={{ $json.htmlBody }}", "options": { "appendAttribution": false, "threadId": "={{ $('Gmail Trigger').item.json.threadId }}" } } }, { "id": "69e130ab-3df6-4be8-9e50-beafd58ede3b", "name": "Build Thank-You Email", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ 2420, 440 ], "parameters": { "mode": "runOnceForEachItem", "jsCode": "// =============================================================\n// BUILD THANK-YOU EMAIL (false branch: score below 4)\n// Short, polite note - no detailed report.\n// =============================================================\nconst extracted = $('Information Extractor').item.json.output || {};\nconst sender = $('Gmail Trigger').item.json.from?.value?.[0] || {};\nconst toEmail = extracted.email || sender.address;\nconst toName = extracted.name || sender.name || 'Candidate';\nif (!toEmail) throw new Error('No recipient email address could be found.');\n\nconst esc = (v) => String(v ?? '').replace(/&/g, '&').replace(//g, '>');\n\nconst subject = 'Thank you for your application';\nconst htmlBody = `\n
\n

Hi ${esc(toName)},

\n

Thank you for applying for the Junior AI Automation Engineer role. After reviewing\n your CV, we will not be moving forward with your application for this position.

\n

We will keep your details on file and encourage you to apply for future openings.

\n

Best regards,
The Recruitment Team

\n
\n

\n This message was generated automatically. If you believe this decision is wrong,\n simply reply to this email and a human will review it.\n

\n
`;\nreturn { toEmail, toName, subject, htmlBody };" } }, { "id": "55a586f0-9be0-4c23-9990-dda2aba1aa71", "name": "Send Thank-You Email", "type": "n8n-nodes-base.gmail", "typeVersion": 2.1, "position": [ 2640, 440 ], "parameters": { "sendTo": "={{ $json.toEmail }}", "subject": "={{ $json.subject }}", "emailType": "html", "message": "={{ $json.htmlBody }}", "options": { "appendAttribution": false } } } ], "connections": { "Gmail Trigger": { "main": [ [ { "node": "Upload file", "type": "main", "index": 0 } ] ] }, "Upload file": { "main": [ [ { "node": "Google Drive", "type": "main", "index": 0 } ] ] }, "Google Drive": { "main": [ [ { "node": "Extract from File", "type": "main", "index": 0 } ] ] }, "Extract from File": { "main": [ [ { "node": "Information Extractor", "type": "main", "index": 0 } ] ] }, "Information Extractor": { "main": [ [ { "node": "AI Agent", "type": "main", "index": 0 } ] ] }, "AI Agent": { "main": [ [ { "node": "Edit Fields", "type": "main", "index": 0 } ] ] }, "Edit Fields": { "main": [ [ { "node": "Code", "type": "main", "index": 0 } ] ] }, "Code": { "main": [ [ { "node": "Append row in sheet", "type": "main", "index": 0 } ] ] }, "Append row in sheet": { "main": [ [ { "node": "Score >= 4?", "type": "main", "index": 0 } ] ] }, "Score >= 4?": { "main": [ [ { "node": "Build Email Content", "type": "main", "index": 0 } ], [ { "node": "Build Thank-You Email", "type": "main", "index": 0 } ] ] }, "Build Email Content": { "main": [ [ { "node": "Send Email", "type": "main", "index": 0 } ] ] }, "Build Thank-You Email": { "main": [ [ { "node": "Send Thank-You Email", "type": "main", "index": 0 } ] ] }, "OpenAI Chat Model2": { "ai_languageModel": [ [ { "node": "Information Extractor", "type": "ai_languageModel", "index": 0 }, { "node": "AI Agent", "type": "ai_languageModel", "index": 0 } ] ] } }, "settings": { "executionOrder": "v1" } }