Screen Resumes and Match Candidates with Gpt-4o, Google Sheets and Email

The workflow JSON

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{
  "id": "RIl15VENoTnyquUt",
  "name": "Intelligent resume screening and candidate matching with confidence routing",
  "tags": [],
  "nodes": [
    {
      "id": "922861ac-44e2-444e-b67a-a699ef833894",
      "name": "Receive Resume & Job Data",
      "type": "n8n-nodes-base.webhook",
      "position": [
        256,
        400
      ],
      "parameters": {
        "path": "recruitment-analysis",
        "options": {},
        "httpMethod": "POST",
        "responseMode": "lastNode"
      },
      "typeVersion": 2.1
    },
    {
      "id": "5a82b5f7-ab33-48c7-8905-4f5aa0231ab2",
      "name": "Matching Agent (Orchestrator)",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        1192,
        400
      ],
      "parameters": {
        "text": "={{ $json.body }}",
        "options": {
          "systemMessage": "You are the Matching Agent orchestrator for a recruitment system. Your role is to coordinate specialized sub-agents (Resume Parser, Skill Analysis, Experience Assessment, Cultural Fit) to evaluate candidates against job requirements. You MUST provide detailed ranking explanations but NEVER make autonomous hiring decisions. Always present findings as recommendations requiring human review. Analyze the candidate data and job requirements, delegate tasks to your specialized agents, then synthesize their findings into a comprehensive ranking explanation with confidence scores and reasoning."
        },
        "promptType": "define",
        "hasOutputParser": true
      },
      "typeVersion": 3.1
    },
    {
      "id": "a715144c-8201-41bb-b0f1-e4f598b98489",
      "name": "Matching Agent Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        480,
        736
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "id",
          "value": "gpt-4o"
        },
        "options": {
          "temperature": 0.2
        },
        "builtInTools": {}
      },
      "credentials": {
        "openAiApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "2b608072-1da6-41cd-a72a-8fd5f3aa3339",
      "name": "Ranking Output Parser",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        1952,
        736
      ],
      "parameters": {
        "schemaType": "manual",
        "inputSchema": "{\n\t\"type\": \"object\",\n\t\"properties\": {\n\t\t\"candidateId\": {\n\t\t\t\"type\": \"string\",\n\t\t\t\"description\": \"Unique identifier for the candidate\"\n\t\t},\n\t\t\"overallScore\": {\n\t\t\t\"type\": \"number\",\n\t\t\t\"minimum\": 0,\n\t\t\t\"maximum\": 100,\n\t\t\t\"description\": \"Overall matching score from 0 to 100\"\n\t\t},\n\t\t\"confidenceLevel\": {\n\t\t\t\"type\": \"string\",\n\t\t\t\"enum\": [\"high\", \"medium\", \"low\"],\n\t\t\t\"description\": \"Confidence level of the match assessment\"\n\t\t},\n\t\t\"skillMatchScore\": {\n\t\t\t\"type\": \"number\",\n\t\t\t\"description\": \"Score representing how well candidate skills match job requirements\"\n\t\t},\n\t\t\"experienceScore\": {\n\t\t\t\"type\": \"number\",\n\t\t\t\"description\": \"Score representing candidate experience level\"\n\t\t},\n\t\t\"culturalFitScore\": {\n\t\t\t\"type\": \"number\",\n\t\t\t\"description\": \"Score representing cultural fit assessment\"\n\t\t},\n\t\t\"strengths\": {\n\t\t\t\"type\": \"array\",\n\t\t\t\"items\": {\n\t\t\t\t\"type\": \"string\"\n\t\t\t},\n\t\t\t\"description\": \"List of candidate strengths\"\n\t\t},\n\t\t\"concerns\": {\n\t\t\t\"type\": \"array\",\n\t\t\t\"items\": {\n\t\t\t\t\"type\": \"string\"\n\t\t\t},\n\t\t\t\"description\": \"List of concerns or potential issues\"\n\t\t},\n\t\t\"recommendation\": {\n\t\t\t\"type\": \"string\",\n\t\t\t\"description\": \"Overall recommendation for the candidate\"\n\t\t},\n\t\t\"requiresHumanReview\": {\n\t\t\t\"type\": \"boolean\",\n\t\t\t\"default\": true,\n\t\t\t\"description\": \"Flag indicating if human review is required\"\n\t\t},\n\t\t\"detailedExplanation\": {\n\t\t\t\"type\": \"string\",\n\t\t\t\"description\": \"Detailed explanation of the ranking decision\"\n\t\t}\n\t},\n\t\"required\": [\"candidateId\", \"overallScore\", \"confidenceLevel\", \"skillMatchScore\", \"experienceScore\", \"culturalFitScore\", \"strengths\", \"concerns\", \"recommendation\", \"requiresHumanReview\", \"detailedExplanation\"]\n}"
      },
      "typeVersion": 1.3
    },
    {
      "id": "34265133-efe2-41ce-b80f-7309edb49e3a",
      "name": "Resume Parser Agent",
      "type": "@n8n/n8n-nodes-langchain.agentTool",
      "position": [
        608,
        736
      ],
      "parameters": {
        "text": "={{ $fromAI('resumeData', 'The resume content to parse and structure') }}",
        "options": {
          "systemMessage": "You are a Resume Parser Agent. Extract structured information from resume text including: personal details, education history, work experience, technical skills, soft skills, certifications, and achievements. Validate data completeness and flag missing critical fields. Return structured JSON metadata."
        },
        "hasOutputParser": true,
        "toolDescription": "Extracts and structures resume data including contact info, education, work history, skills, and certifications. Returns validated metadata in standardized JSON format."
      },
      "typeVersion": 3
    },
    {
      "id": "f14c202b-5776-4540-832e-bc0e886eae03",
      "name": "Resume Parser Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        688,
        944
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "id",
          "value": "gpt-4o"
        },
        "options": {
          "temperature": 0.1
        },
        "builtInTools": {}
      },
      "credentials": {
        "openAiApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "591d3ac8-b01a-44a4-bb2e-c4729528c631",
      "name": "Skill Analysis Agent",
      "type": "@n8n/n8n-nodes-langchain.agentTool",
      "position": [
        896,
        736
      ],
      "parameters": {
        "text": "={{ $fromAI('skillData', 'Candidate skills and job requirements to analyze') }}",
        "options": {
          "systemMessage": "You are a Skill Analysis Agent specializing in technical and professional skill validation. Compare candidate skills against job requirements. Assess proficiency levels, identify skill gaps, recognize transferable skills, and calculate match scores. Provide detailed explanations for each assessment. Flag any skill mismatches or concerns."
        },
        "hasOutputParser": true,
        "toolDescription": "Validates candidate skills against job requirements. Analyzes technical proficiency, skill gaps, transferable skills, and provides match scoring with detailed explanations."
      },
      "typeVersion": 3
    },
    {
      "id": "7f0c6241-1bc2-43e7-8a8d-d7bb1f96c634",
      "name": "Skill Analysis Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        976,
        944
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "id",
          "value": "gpt-4o"
        },
        "options": {
          "temperature": 0.2
        },
        "builtInTools": {}
      },
      "credentials": {
        "openAiApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "e9941ce5-c2d1-41bf-b023-9627e52a96f5",
      "name": "Experience Assessment Agent",
      "type": "@n8n/n8n-nodes-langchain.agentTool",
      "position": [
        1184,
        736
      ],
      "parameters": {
        "text": "={{ $fromAI('experienceData', 'Candidate work history and job experience requirements to evaluate') }}",
        "options": {
          "systemMessage": "You are an Experience Assessment Agent specializing in work history evaluation. Analyze candidate work experience against job requirements. Assess: years of relevant experience, career progression trajectory, role responsibilities alignment, industry experience, leadership roles, and achievement impact. Calculate experience match scores and provide detailed explanations for your assessments."
        },
        "hasOutputParser": true,
        "toolDescription": "Evaluates work history relevance, career progression, role responsibilities, and industry experience alignment with job requirements. Provides scoring and detailed reasoning."
      },
      "typeVersion": 3
    },
    {
      "id": "16842a64-7455-4973-b7ab-ca0be9ffc796",
      "name": "Experience Assessment Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        1264,
        944
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "id",
          "value": "gpt-4o"
        },
        "options": {
          "temperature": 0.2
        },
        "builtInTools": {}
      },
      "credentials": {
        "openAiApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "40de05bb-77e4-4190-aef5-02b90a220c77",
      "name": "Cultural Fit Agent",
      "type": "@n8n/n8n-nodes-langchain.agentTool",
      "position": [
        1472,
        736
      ],
      "parameters": {
        "text": "={{ $fromAI('culturalData', 'Candidate soft skills and company culture requirements to analyze') }}",
        "options": {
          "systemMessage": "You are a Cultural Fit Agent specializing in soft skills and values alignment analysis. Evaluate candidate cultural compatibility based on: communication style, collaboration indicators, adaptability, problem-solving approach, leadership style, and values expressed in resume. Compare against company culture requirements. Provide cultural fit scores with detailed reasoning. Note: This is advisory only - final cultural fit must be assessed through human interviews."
        },
        "hasOutputParser": true,
        "toolDescription": "Analyzes soft skills, communication style, values alignment, team collaboration indicators, and cultural compatibility with company values. Provides fit assessment with explanations."
      },
      "typeVersion": 3
    },
    {
      "id": "724038b5-4478-4575-bd7e-13fe46e23eeb",
      "name": "Cultural Fit Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        1552,
        944
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "id",
          "value": "gpt-4o"
        },
        "options": {
          "temperature": 0.3
        },
        "builtInTools": {}
      },
      "credentials": {
        "openAiApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "f74c3a7b-6990-4b0e-bad8-1f31a191a053",
      "name": "Validation Logic Tool",
      "type": "@n8n/n8n-nodes-langchain.toolCode",
      "position": [
        1760,
        736
      ],
      "parameters": {
        "jsCode": "// Validation Logic Tool - Resume Metadata Validator\n// Validates resume metadata completeness and quality\n\nconst resumeMetadata = query;\n\n// Initialize validation result\nconst validationResult = {\n  isValid: true,\n  missingFields: [],\n  qualityScore: 100,\n  warnings: []\n};\n\n// Check required fields\nconst requiredFields = ['name', 'email', 'skills', 'experience'];\n\nfor (const field of requiredFields) {\n  if (!resumeMetadata[field] || \n      (Array.isArray(resumeMetadata[field]) && resumeMetadata[field].length === 0) ||\n      (typeof resumeMetadata[field] === 'string' && resumeMetadata[field].trim() === '')) {\n    validationResult.missingFields.push(field);\n    validationResult.isValid = false;\n    validationResult.qualityScore -= 25;\n  }\n}\n\n// Validate email format\nif (resumeMetadata.email) {\n  const emailRegex = /^[^\\s@]+@[^\\s@]+\\.[^\\s@]+$/;\n  if (!emailRegex.test(resumeMetadata.email)) {\n    validationResult.warnings.push('Invalid email format');\n    validationResult.qualityScore -= 10;\n  }\n} else {\n  validationResult.warnings.push('Email is missing');\n}\n\n// Validate minimum experience years\nif (resumeMetadata.experience !== undefined) {\n  const experienceYears = Array.isArray(resumeMetadata.experience) \n    ? resumeMetadata.experience.length \n    : (typeof resumeMetadata.experience === 'number' ? resumeMetadata.experience : 0);\n  \n  if (experienceYears < 0) {\n    validationResult.warnings.push('Experience years cannot be negative');\n    validationResult.qualityScore -= 15;\n  }\n} else {\n  validationResult.warnings.push('Experience data is missing');\n}\n\n// Check skill count\nif (resumeMetadata.skills) {\n  const skillCount = Array.isArray(resumeMetadata.skills) \n    ? resumeMetadata.skills.length \n    : 0;\n  \n  if (skillCount === 0) {\n    validationResult.warnings.push('No skills listed');\n    validationResult.qualityScore -= 20;\n  } else if (skillCount < 3) {\n    validationResult.warnings.push('Very few skills listed (less than 3)');\n    validationResult.qualityScore -= 10;\n  }\n} else {\n  validationResult.warnings.push('Skills data is missing');\n}\n\n// Ensure quality score doesn't go below 0\nvalidationResult.qualityScore = Math.max(0, validationResult.qualityScore);\n\n// Return validation result as JSON string\nreturn JSON.stringify(validationResult, null, 2);",
        "schemaType": "manual",
        "description": "Validates resume metadata completeness, checks required fields, calculates data quality scores, and flags incomplete or suspicious data.",
        "inputSchema": "{\n\t\"type\": \"object\",\n\t\"properties\": {\n\t\t\"resumeMetadata\": {\n\t\t\t\"type\": \"object\",\n\t\t\t\"description\": \"Resume metadata object to validate\",\n\t\t\t\"properties\": {\n\t\t\t\t\"name\": {\n\t\t\t\t\t\"type\": \"string\",\n\t\t\t\t\t\"description\": \"Candidate name\"\n\t\t\t\t},\n\t\t\t\t\"email\": {\n\t\t\t\t\t\"type\": \"string\",\n\t\t\t\t\t\"description\": \"Candidate email address\"\n\t\t\t\t},\n\t\t\t\t\"skills\": {\n\t\t\t\t\t\"type\": \"array\",\n\t\t\t\t\t\"description\": \"List of candidate skills\"\n\t\t\t\t},\n\t\t\t\t\"experience\": {\n\t\t\t\t\t\"description\": \"Candidate experience data\"\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t},\n\t\"required\": [\"resumeMetadata\"]\n}",
        "specifyInputSchema": true
      },
      "typeVersion": 1.3
    },
    {
      "id": "541003a8-cf59-4e97-8c00-ce98c49be327",
      "name": "Store Analysis Results",
      "type": "n8n-nodes-base.dataTable",
      "position": [
        2544,
        496
      ],
      "parameters": {
        "columns": {
          "value": null,
          "mappingMode": "autoMapInputData"
        },
        "options": {},
        "dataTableId": {
          "__rl": true,
          "mode": "id",
          "value": "<__PLACEHOLDER_VALUE__candidate_analysis_table__>"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "54e461a8-d5a3-40cf-acef-06755cf1493e",
      "name": "Prepare Analysis Data",
      "type": "n8n-nodes-base.set",
      "position": [
        2320,
        496
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "id-1",
              "name": "candidateId",
              "type": "string",
              "value": "={{ $json.candidateId }}"
            },
            {
              "id": "id-2",
              "name": "timestamp",
              "type": "string",
              "value": "={{ $now }}"
            },
            {
              "id": "id-3",
              "name": "overallScore",
              "type": "number",
              "value": "={{ $json.overallScore }}"
            },
            {
              "id": "id-4",
              "name": "confidenceLevel",
              "type": "string",
              "value": "={{ $json.confidenceLevel }}"
            },
            {
              "id": "id-5",
              "name": "skillMatchScore",
              "type": "number",
              "value": "={{ $json.skillMatchScore }}"
            },
            {
              "id": "id-6",
              "name": "experienceScore",
              "type": "number",
              "value": "={{ $json.experienceScore }}"
            },
            {
              "id": "id-7",
              "name": "culturalFitScore",
              "type": "number",
              "value": "={{ $json.culturalFitScore }}"
            },
            {
              "id": "id-8",
              "name": "recommendation",
              "type": "string",
              "value": "={{ $json.recommendation }}"
            },
            {
              "id": "id-9",
              "name": "detailedExplanation",
              "type": "string",
              "value": "={{ $json.detailedExplanation }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "e649cdcf-adc8-4b6a-b9bd-88769c701fb6",
      "name": "Check Confidence Level",
      "type": "n8n-nodes-base.if",
      "position": [
        2096,
        400
      ],
      "parameters": {
        "options": {},
        "conditions": {
          "options": {
            "leftValue": "",
            "caseSensitive": false,
            "typeValidation": "loose"
          },
          "combinator": "or",
          "conditions": [
            {
              "id": "id-1",
              "operator": {
                "type": "string",
                "operation": "equals"
              },
              "leftValue": "={{ $json.confidenceLevel }}",
              "rightValue": "high"
            },
            {
              "id": "id-2",
              "operator": {
                "type": "number",
                "operation": "gt"
              },
              "leftValue": "={{ $json.overallScore }}",
              "rightValue": "75"
            }
          ]
        }
      },
      "typeVersion": 2.3
    },
    {
      "id": "6af1d837-6801-4f30-ace2-2083b209d12e",
      "name": "Send High Confidence Report",
      "type": "n8n-nodes-base.emailSend",
      "position": [
        2768,
        496
      ],
      "parameters": {
        "html": "=<h2>High Confidence Candidate Analysis Report</h2>\n\n<div style=\"background-color: #f5f5f5; padding: 20px; border-radius: 8px; margin: 20px 0;\">\n  <h3 style=\"color: #2c5282; margin-top: 0;\">Candidate Overview</h3>\n  <p><strong>Candidate ID:</strong> {{ $json.output.candidateId }}</p>\n  <p><strong>Overall Score:</strong> {{ $json.output.overallScore }}/100</p>\n  <p><strong>Confidence Level:</strong> {{ $json.output.confidenceLevel }}</p>\n</div>\n\n<div style=\"background-color: #ffffff; padding: 20px; border: 1px solid #e2e8f0; border-radius: 8px; margin: 20px 0;\">\n  <h3 style=\"color: #2c5282;\">Detailed Scores</h3>\n  <ul style=\"list-style-type: none; padding-left: 0;\">\n    <li style=\"padding: 8px 0; border-bottom: 1px solid #e2e8f0;\"><strong>Skill Match Score:</strong> {{ $json.output.skillMatchScore }}</li>\n    <li style=\"padding: 8px 0; border-bottom: 1px solid #e2e8f0;\"><strong>Experience Score:</strong> {{ $json.output.experienceScore }}</li>\n    <li style=\"padding: 8px 0;\"><strong>Cultural Fit Score:</strong> {{ $json.output.culturalFitScore }}</li>\n  </ul>\n</div>\n\n<div style=\"background-color: #f0fff4; padding: 20px; border-left: 4px solid #48bb78; border-radius: 8px; margin: 20px 0;\">\n  <h3 style=\"color: #2c5282; margin-top: 0;\">Strengths</h3>\n  <ul>\n    {{ $json.output.strengths.map(strength => '<li>' + strength + '</li>').join('') }}\n  </ul>\n</div>\n\n<div style=\"background-color: #fffaf0; padding: 20px; border-left: 4px solid #ed8936; border-radius: 8px; margin: 20px 0;\">\n  <h3 style=\"color: #2c5282; margin-top: 0;\">Recommendation</h3>\n  <p>{{ $json.output.recommendation }}</p>\n</div>\n\n<div style=\"background-color: #ffffff; padding: 20px; border: 1px solid #e2e8f0; border-radius: 8px; margin: 20px 0;\">\n  <h3 style=\"color: #2c5282; margin-top: 0;\">Detailed Explanation</h3>\n  <p style=\"line-height: 1.6;\">{{ $json.output.detailedExplanation }}</p>\n</div>\n\n<div style=\"margin-top: 30px; padding-top: 20px; border-top: 2px solid #e2e8f0; color: #718096; font-size: 12px;\">\n  <p>This is an automated analysis report. Please review the candidate details and proceed with the next steps in the recruitment process.</p>\n</div>",
        "options": {
          "appendAttribution": true
        },
        "subject": "=High Confidence Candidate Analysis - {{ $json.output.candidateId }}",
        "toEmail": "<__PLACEHOLDER_VALUE__hiring_manager_email__>",
        "fromEmail": "<__PLACEHOLDER_VALUE__system_email__>"
      },
      "typeVersion": 2.1
    },
    {
      "id": "890d8787-abbb-46e4-bade-3a245a425edc",
      "name": "Send Review Required Alert",
      "type": "n8n-nodes-base.emailSend",
      "position": [
        2768,
        304
      ],
      "parameters": {
        "html": "=<h2 style=\"color: #ff6d5a;\">\u26a0\ufe0f MANUAL REVIEW REQUIRED</h2>\n\n<p>A candidate analysis has been completed with <strong>low confidence</strong> and requires immediate human review before proceeding.</p>\n\n<hr style=\"border: 1px solid #dbdfe7; margin: 20px 0;\">\n\n<h3>Candidate Overview</h3>\n<table style=\"width: 100%; border-collapse: collapse;\">\n  <tr>\n    <td style=\"padding: 8px; background-color: #f5f5f5; font-weight: bold; width: 40%;\">Candidate ID:</td>\n    <td style=\"padding: 8px;\">{{ $json.output.candidateId }}</td>\n  </tr>\n  <tr>\n    <td style=\"padding: 8px; background-color: #f5f5f5; font-weight: bold;\">Overall Score:</td>\n    <td style=\"padding: 8px; color: {{ $json.output.overallScore >= 75 ? '#28a745' : ($json.output.overallScore >= 50 ? '#ffc107' : '#dc3545') }}; font-weight: bold;\">{{ $json.output.overallScore }}/100</td>\n  </tr>\n  <tr>\n    <td style=\"padding: 8px; background-color: #f5f5f5; font-weight: bold;\">Confidence Level:</td>\n    <td style=\"padding: 8px; color: #dc3545; font-weight: bold;\">{{ $json.output.confidenceLevel.toUpperCase() }}</td>\n  </tr>\n  <tr>\n    <td style=\"padding: 8px; background-color: #f5f5f5; font-weight: bold;\">Skill Match Score:</td>\n    <td style=\"padding: 8px;\">{{ $json.output.skillMatchScore }}</td>\n  </tr>\n  <tr>\n    <td style=\"padding: 8px; background-color: #f5f5f5; font-weight: bold;\">Experience Score:</td>\n    <td style=\"padding: 8px;\">{{ $json.output.experienceScore }}</td>\n  </tr>\n  <tr>\n    <td style=\"padding: 8px; background-color: #f5f5f5; font-weight: bold;\">Cultural Fit Score:</td>\n    <td style=\"padding: 8px;\">{{ $json.output.culturalFitScore }}</td>\n  </tr>\n</table>\n\n<hr style=\"border: 1px solid #dbdfe7; margin: 20px 0;\">\n\n<h3 style=\"color: #dc3545;\">\u26a0\ufe0f Concerns Identified</h3>\n<ul style=\"background-color: #fff3cd; padding: 15px; border-left: 4px solid #ffc107;\">\n  {{ $json.output.concerns.map(concern => '<li>' + concern + '</li>').join('') }}\n</ul>\n\n<hr style=\"border: 1px solid #dbdfe7; margin: 20px 0;\">\n\n<h3>Detailed Analysis</h3>\n<div style=\"background-color: #f8f9fa; padding: 15px; border-radius: 5px; line-height: 1.6;\">\n  {{ $json.output.detailedExplanation }}\n</div>\n\n<hr style=\"border: 1px solid #dbdfe7; margin: 20px 0;\">\n\n<h3>AI Recommendation</h3>\n<div style=\"background-color: #e7f3ff; padding: 15px; border-left: 4px solid #2196F3;\">\n  {{ $json.output.recommendation }}\n</div>\n\n<hr style=\"border: 1px solid #dbdfe7; margin: 20px 0;\">\n\n<div style=\"background-color: #fff3cd; padding: 20px; border: 2px solid #ffc107; border-radius: 5px; margin-top: 30px;\">\n  <h3 style=\"color: #856404; margin-top: 0;\">\ud83d\udd0d IMPORTANT: Manual Review Required</h3>\n  <p style=\"color: #856404; font-weight: bold; margin-bottom: 0;\">\n    This analysis has been flagged for mandatory human review due to low confidence levels. \n    Please conduct a thorough manual assessment before making any hiring decisions. \n    The AI analysis should be used as a supplementary tool only.\n  </p>\n</div>\n\n<hr style=\"border: 1px solid #dbdfe7; margin: 20px 0;\">\n\n<p style=\"font-size: 12px; color: #6c757d; margin-top: 30px;\">\n  <em>This automated analysis was generated by the AI-Powered Recruitment System. All recommendations require human validation.</em>\n</p>",
        "options": {
          "appendAttribution": true
        },
        "subject": "=REVIEW REQUIRED - Low Confidence Candidate Analysis - {{ $json.output.candidateId }}",
        "toEmail": "<__PLACEHOLDER_VALUE__hiring_manager_email__>",
        "fromEmail": "<__PLACEHOLDER_VALUE__system_email__>"
      },
      "typeVersion": 2.1
    },
    {
      "id": "311cea88-9a9f-4698-9ad4-16748e8a19b3",
      "name": "Prepare Low Confidence Data",
      "type": "n8n-nodes-base.set",
      "position": [
        2320,
        304
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "id-1",
              "name": "candidateId",
              "type": "string",
              "value": "={{ $json.candidateId }}"
            },
            {
              "id": "id-2",
              "name": "timestamp",
              "type": "string",
              "value": "={{ $now }}"
            },
            {
              "id": "id-3",
              "name": "overallScore",
              "type": "number",
              "value": "={{ $json.overallScore }}"
            },
            {
              "id": "id-4",
              "name": "confidenceLevel",
              "type": "string",
              "value": "={{ $json.confidenceLevel }}"
            },
            {
              "id": "id-5",
              "name": "concerns",
              "type": "array",
              "value": "={{ $json.concerns }}"
            },
            {
              "id": "id-6",
              "name": "requiresHumanReview",
              "type": "boolean",
              "value": true
            },
            {
              "id": "id-7",
              "name": "reviewStatus",
              "type": "string",
              "value": "pending"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "c34cbcdb-d26d-4ff5-a724-dca6ffb41983",
      "name": "Store Low Confidence Cases",
      "type": "n8n-nodes-base.dataTable",
      "position": [
        2544,
        304
      ],
      "parameters": {
        "columns": {
          "value": null,
          "mappingMode": "autoMapInputData"
        },
        "options": {},
        "dataTableId": {
          "__rl": true,
          "mode": "id",
          "value": "<__PLACEHOLDER_VALUE__review_queue_table__>"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "85979882-7660-4be7-9108-543136b0df46",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1456,
        -80
      ],
      "parameters": {
        "color": 5,
        "width": 576,
        "height": 384,
        "content": "## Prerequisites\n- OpenAI API key (or compatible LLM)\n- Google Sheets with candidate tracking tabs pre-created\n- Email account credentials (SMTP or Gmail OAuth)\n## Use Cases\n- Recruiters automating high-volume resume screening against structured job descriptions\n## Customisation\n- Extend specialist agents with domain-specific scoring rubrics for technical or executive roles\n## Benefits\n- Four parallel specialist agents evaluate candidates across skills, experience, and cultural fit simultaneously"
      },
      "typeVersion": 1
    },
    {
      "id": "f431e002-3084-4b4b-a4f4-f982c8340fef",
      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        928,
        16
      ],
      "parameters": {
        "width": 464,
        "height": 288,
        "content": "## Setup Steps\n1. Import workflow; configure the POST webhook trigger URL for resume and job data ingestion.\n2. Add AI model credentials to the Matching Agent Orchestrator, Resume Parser Agent, Skill Analysis Agent, Experience Assessment Agent, and Cultural Fit Agent.\n3. Link Google Sheets credentials; set sheet IDs for Low Confidence Cases and Analysis Results tabs.\n4. Connect email credentials to the Send Review Required Alert and Send High Confidence Report nodes.\n5. Set confidence threshold values in the Check Confidence Level node."
      },
      "typeVersion": 1
    },
    {
      "id": "9230912c-12bc-4709-86aa-2543398ae4f1",
      "name": "Sticky Note2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        256,
        -48
      ],
      "parameters": {
        "width": 624,
        "height": 336,
        "content": "## How It Works\nThis workflow automates candidate screening and job matching for recruiters, HR operations teams, and talent acquisition leads. It eliminates the manual effort of parsing resumes, evaluating multi-dimensional candidate fit, and routing outcomes based on assessment confidence. Resume and job data are received via a POST webhook and passed directly to the Matching Agent Orchestrator, backed by a matching model and shared memory. The orchestrator coordinates four specialist agents in parallel: a Resume Parser Agent (structured extraction), a Skill Analysis Agent (competency mapping), an Experience Assessment Agent (seniority and relevance scoring), and a Cultural Fit Agent (organisational alignment evaluation). A Validation Logic Tool cross-checks outputs before a Ranking Output Parser produces a structured candidate ranking. Results are then checked against a confidence threshold \u2014 low-confidence cases trigger a review alert via email and are stored in Google Sheets for human follow-up, while high-confidence matches are prepared as analysis data, stored in Sheets, and distributed as a ranked report via email."
      },
      "typeVersion": 1
    },
    {
      "id": "9814e308-abf9-4996-ad04-4fb4a3ae87ea",
      "name": "Sticky Note3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2048,
        176
      ],
      "parameters": {
        "color": 7,
        "width": 400,
        "height": 608,
        "content": "## Check Confidence Level & Route Outcomes\n**Why** \u2014 Confidence-based routing separates high-quality matches from uncertain assessments, directing low-confidence cases to human review without discarding them."
      },
      "typeVersion": 1
    },
    {
      "id": "a5d0614a-d8d1-4fea-b2cd-7139ffd3010c",
      "name": "Sticky Note4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        416,
        576
      ],
      "parameters": {
        "color": 7,
        "width": 1632,
        "height": 576,
        "content": "## Validation Logic Tool & Ranking Output Parser\n**Why** \u2014 Cross-validates multi-agent outputs and structures them into a ranked candidate list, ensuring scoring consistency before confidence checking."
      },
      "typeVersion": 1
    },
    {
      "id": "fb2c8c6f-d987-43ea-95b2-2ca286e8f79a",
      "name": "Sticky Note5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        144,
        320
      ],
      "parameters": {
        "color": 7,
        "width": 1424,
        "height": 240,
        "content": "## Receiving, Matching Agent Orchestrator & Specialist Agents\n**Why** \u2014 Coordinates Resume Parsing, Skill Analysis, Experience Assessment, and Cultural Fit agents in parallel using shared memory for comprehensive, multi-dimensional candidate evaluation."
      },
      "typeVersion": 1
    },
    {
      "id": "29fd6d78-36eb-4ffb-adcf-2d912f5aab11",
      "name": "Sticky Note6",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2464,
        160
      ],
      "parameters": {
        "color": 7,
        "width": 576,
        "height": 640,
        "content": "## Store Results & Send Reports\n**Why** \u2014 Both confidence paths store outputs in Google Sheets and distribute reports via email, maintaining a complete candidate record regardless of confidence outcome."
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "settings": {
    "binaryMode": "separate",
    "executionOrder": "v1"
  },
  "versionId": "b40afb64-e4ae-4d0e-bd0a-0528d4396a87",
  "connections": {
    "Cultural Fit Agent": {
      "ai_tool": [
        [
          {
            "node": "Matching Agent (Orchestrator)",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Cultural Fit Model": {
      "ai_languageModel": [
        [
          {
            "node": "Cultural Fit Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Resume Parser Agent": {
      "ai_tool": [
        [
          {
            "node": "Matching Agent (Orchestrator)",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Resume Parser Model": {
      "ai_languageModel": [
        [
          {
            "node": "Resume Parser Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Matching Agent Model": {
      "ai_languageModel": [
        [
          {
            "node": "Matching Agent (Orchestrator)",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Skill Analysis Agent": {
      "ai_tool": [
        [
          {
            "node": "Matching Agent (Orchestrator)",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Skill Analysis Model": {
      "ai_languageModel": [
        [
          {
            "node": "Skill Analysis Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Prepare Analysis Data": {
      "main": [
        [
          {
            "node": "Store Analysis Results",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Ranking Output Parser": {
      "ai_outputParser": [
        [
          {
            "node": "Matching Agent (Orchestrator)",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "Validation Logic Tool": {
      "ai_tool": [
        [
          {
            "node": "Matching Agent (Orchestrator)",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Check Confidence Level": {
      "main": [
        [
          {
            "node": "Prepare Analysis Data",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Prepare Low Confidence Data",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Store Analysis Results": {
      "main": [
        [
          {
            "node": "Send High Confidence Report",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Receive Resume & Job Data": {
      "main": [
        [
          {
            "node": "Matching Agent (Orchestrator)",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Store Low Confidence Cases": {
      "main": [
        [
          {
            "node": "Send Review Required Alert",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Experience Assessment Agent": {
      "ai_tool": [
        [
          {
            "node": "Matching Agent (Orchestrator)",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Experience Assessment Model": {
      "ai_languageModel": [
        [
          {
            "node": "Experience Assessment Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Prepare Low Confidence Data": {
      "main": [
        [
          {
            "node": "Store Low Confidence Cases",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Matching Agent (Orchestrator)": {
      "main": [
        [
          {
            "node": "Check Confidence Level",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}

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.

• openAiApi

About this workflow

This workflow automates candidate screening and job matching for recruiters, HR operations teams, and talent acquisition leads. It eliminates the manual effort of parsing resumes, evaluating multi-dimensional candidate fit, and routing outcomes based on assessment confidence.…

Source: https://n8n.io/workflows/14442/ — original creator credit. Request a take-down →

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