Qualitätsaudit von Interviews

• •Slack Bot Token oder Webhook URL
• •Google Sheets API-Anmeldedaten
• •OpenAI API Key

{
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  "meta": {
    "instanceId": "8443f10082278c46aa5cf3acf8ff0f70061a2c58bce76efac814b16290845177",
    "templateCredsSetupCompleted": true
  },
  "name": "Interview Quality Audit",
  "tags": [],
  "nodes": [
    {
      "id": "9e228e13-31c4-4f40-8bc1-83ffc0c0df21",
      "name": "Bei Klick auf 'Workflow ausführen'",
      "type": "n8n-nodes-base.manualTrigger",
      "position": [
        -176,
        16
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "4656de9f-4ad6-4b48-a8b0-6802cd1e88ca",
      "name": "Notiz",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        576,
        160
      ],
      "parameters": {
        "height": 384,
        "content": " ✅ Validate AI Response\nType: Conditional Logic\nPurpose: Quality control checkpoint\nValidation:\n\nResponse text is not undefined\nContains valid data structure\n\nRouting:\n\n✅ Valid → Continue to JSON parsing\n❌ Invalid → Log to error sheet\n\nFunction: Prevents malformed responses from corrupting pipeline"
      },
      "typeVersion": 1
    },
    {
      "id": "ed2e275a-ea76-4d78-b9da-2149ec9f5b50",
      "name": "Notiz1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        224,
        352
      ],
      "parameters": {
        "height": 320,
        "content": "🤖 AI Quality Evaluator (GPT-4o)\nType: AI Language Model\nPurpose: Cognitive engine for feedback analysis\nConfiguration:\n\nModel: gpt-4o-mini\nPlatform: Azure OpenAI\nIntegration: LangChain\n\nConnects to Azure GPT-4o-mini\nPowers NLP evaluation\nGenerates structured JSON outputs"
      },
      "typeVersion": 1
    },
    {
      "id": "97cdbdb7-5306-4910-8e93-927940ed699d",
      "name": "Notiz2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        224,
        -560
      ],
      "parameters": {
        "width": 336,
        "height": 544,
        "content": "🔍 Analyze Feedback Quality\nType: LangChain LLM Chain\nPurpose: Core AI evaluation engine\nEvaluation Dimensions (1-5 Scale):\n\nSpecificity (35%) - Concrete details\nStructure/STAR (15%) - Situation-Task-Action-Result\nBias-Free Language (15%) - No gender/appearance bias\nActionability (10%) - Decision usefulness\nDepth (25%) - Multiple competencies\n\nScoring:\n\n5: Excellent - Clear STAR with evidence\n3: Adequate - Some info but vague\n1: Unusable - Purely subjective\n\nSpecial Rules:\n\nFeedback <30 words → Scores ≤2\nExtracts vague phrases (\"great guy\", \"nice energy\")\n\nOutput: JSON with scores and vague_phrases array"
      },
      "typeVersion": 1
    },
    {
      "id": "6681d513-9520-4c78-8595-cbb27bdfff9c",
      "name": "Notiz3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -64,
        192
      ],
      "parameters": {
        "height": 368,
        "content": "📋 Fetch Interview Feedback\nType: Google Sheets Read\nPurpose: Retrieve raw feedback data\nConfiguration:\n\nDocument: Interviewer Brief Pack\nSheet: Raw_Feedback\nOperation: Read all rows\n\nData Retrieved:\nTimestamp, Candidate_ID, Role, Stage, Interviewer_Email, Feedback_Text, row_number\nOutput: All feedback records passed to AI evaluation"
      },
      "typeVersion": 1
    },
    {
      "id": "f3e8a974-86fa-4a81-a5bd-3cbebf6302b4",
      "name": "Notiz4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        928,
        -224
      ],
      "parameters": {
        "width": 288,
        "height": 208,
        "content": "🔄 Parse AI JSON Output\nType: JavaScript Code\nPurpose: Convert string to structured data\nFunction:\n\nSafely parses AI text to JSON object\nTry-catch error handling\nThrows descriptive errors for debugging"
      },
      "typeVersion": 1
    },
    {
      "id": "78deb6fe-c12f-4281-974d-19a9155f0f0e",
      "name": "Notiz5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1664,
        448
      ],
      "parameters": {
        "width": 320,
        "height": 224,
        "content": "🎯 Check if Training Needed\nType: Conditional Logic\nPurpose: Identify low performers\nCondition: Score < 50\nRouting:\n\n✅ <50 → Send training resources\n❌ ≥50 → No additional action\n\nFunction: Triggers enhanced coaching for scores below threshold"
      },
      "typeVersion": 1
    },
    {
      "id": "97e18f0a-359c-4bf3-8ff8-bfe51a193e7d",
      "name": "Notiz6",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2064,
        464
      ],
      "parameters": {
        "width": 400,
        "height": 304,
        "content": "📚 Send Training Recommendations\nType: Slack Notification\nPurpose: Deliver coaching resources\nMessage Content:\n\nScore, Flags, Specific vague phrases\nImprovement guidance (STAR method)\n📘 STAR Method Guide link\n🎥 Bias-Free Interviewing Video link\n\nFunction:\n\nDetailed coaching for scores <50\nLinks to training resources\nSupportive, growth-focused tone"
      },
      "typeVersion": 1
    },
    {
      "id": "b88c8198-7941-41a6-8b7f-406f62371288",
      "name": "Notiz7",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        880,
        464
      ],
      "parameters": {
        "height": 320,
        "content": "🚨 Log AI Errors\nType: Google Sheets Append\nPurpose: Error tracking\nConfiguration:\n\nSheet: error log sheet\nOperation: APPEND\n\nFunction:\n\nCaptures AI failures\nCreates error audit trail\nEnables debugging and monitoring\nTracks system reliability\n\n"
      },
      "typeVersion": 1
    },
    {
      "id": "a98fbf8e-388a-40fe-a7a7-5e54866da422",
      "name": "Notiz9",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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        -144
      ],
      "parameters": {
        "width": 336,
        "height": 304,
        "content": "💬 Send Feedback Summary\nType: Slack Notification\nPurpose: Deliver quality report\nMessage Structure:\n\nRole, Stage, Score (X/100), Flags\nHigh Quality: Congratulations message\nNeeds Improvement: Vague phrases + STAR tips\n\nFunction:\n\nImmediate feedback to interviewer\nSpecific, actionable guidance\nEncourages continuous improvement"
      },
      "typeVersion": 1
    },
    {
      "id": "989baf43-960a-4393-9640-02b4454289d3",
      "name": "Notiz10",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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        -592
      ],
      "parameters": {
        "width": 352,
        "height": 336,
        "content": "💾 Save Scores to Spreadsheet\nType: Google Sheets Update\nPurpose: Persist quality metrics\nConfiguration:\n\nSheet: Raw_Feedback\nOperation: UPDATE (match by row_number)\n\nFields Updated:\n\nScore (0-100)\nFlags (quality issues)\nLLM_JSON (complete AI analysis)\n\nFunction: Updates original rows, creates audit trail for trend analysis"
      },
      "typeVersion": 1
    },
    {
      "id": "c7fbcc25-cdde-4b02-b4ec-afdae31fff2e",
      "name": "Notiz11",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      ],
      "parameters": {
        "width": 288,
        "height": 480,
        "content": "🧮 Calculate Weighted Quality Score\nType: JavaScript Code\nPurpose: Compute final score and flags\nWeights:\nSpecificity 35% | Depth 25% | Structure 15% | Bias-Free 15% | Actionability 10%\nFlags:\n\n\"low_detail\": specificity < 3 OR depth < 3\n\"bias\": bias_free_language < 3\n\nFunction:\n\nCalculates 0-100 score\nGenerates quality flags\nFormats vague phrases for Slack\nPreserves context (Role, Stage, row_number)\n\nOutput: Score, Flags, LLM_JSON, VaguePhrasesFormatted"
      },
      "typeVersion": 1
    },
    {
      "id": "69339a87-95fa-4827-a768-a6a9aa1def9e",
      "name": "Roh-Feedbackdaten abrufen",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
        32,
        16
      ],
      "parameters": {
        "options": {},
        "sheetName": {
          "__rl": true,
          "mode": "list",
          "value": 315277036,
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Uldk_4BxWbdZTDZxFUeohIfeBmGHHqVEl9Ogb0l6R8Y/edit#gid=315277036",
          "cachedResultName": "Raw_Feedback"
        },
        "documentId": {
          "__rl": true,
          "mode": "list",
          "value": "1Uldk_4BxWbdZTDZxFUeohIfeBmGHHqVEl9Ogb0l6R8Y",
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Uldk_4BxWbdZTDZxFUeohIfeBmGHHqVEl9Ogb0l6R8Y/edit?usp=drivesdk",
          "cachedResultName": "Interviewer Brief Pack "
        }
      },
      "credentials": {
        "googleSheetsOAuth2Api": {
          "id": "kpPEOLCGn963qpoh",
          "name": "automations@techdome.ai"
        }
      },
      "typeVersion": 4.6,
      "alwaysOutputData": false
    },
    {
      "id": "99814f2b-ab97-49bf-8eec-43083f731dad",
      "name": "AI-Qualitätsbewerter (GPT-4o1)",
      "type": "@n8n/n8n-nodes-langchain.lmChatAzureOpenAi",
      "position": [
        272,
        192
      ],
      "parameters": {
        "model": "gpt-4o-mini",
        "options": {}
      },
      "credentials": {
        "azureOpenAiApi": {
          "id": "C3WzT18XqF8OdVM6",
          "name": "Azure Open AI account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "6517c215-19c5-4644-97f6-26d650c65540",
      "name": "Feedbackqualität analysieren",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        288,
        16
      ],
      "parameters": {
        "text": "=You are an Interview Feedback Quality Auditor.\n\nYour task is to evaluate interviewer feedback notes and score them across 5 dimensions:\n- specificity (1–5)\n- structure_STAR (1–5)\n- bias_free_language (1–5)\n- actionability (1–5)\n- depth (1–5)\n\n⚖️ Scoring Guidelines:\n- 5 = Excellent: Clear, detailed, STAR format (Situation, Task, Action, Result) explicitly used or strongly implied, with evidence/examples.\n- 4 = Good: Mostly structured, some detail, minor gaps, still useful for decisions.\n- 3 = Adequate: Some relevant info but mixed with vagueness, missing STAR elements.\n- 2 = Poor: Mostly vague or generic, no clear evidence, over-reliant on subjective phrasing.\n- 1 = Unusable: Purely subjective (“great guy”, “nice energy”), no actionable details.\n\nBias-free language: Score low if feedback references gender, looks, personality, or irrelevant traits.\n\nActionability: Score higher if the feedback directly helps in making a decision (e.g., “passed all test cases under time constraint” vs “seems smart”).\n\nDepth: Score higher if multiple competencies or dimensions are covered, lower if only 1 vague point.\n\n🚨 Additional Rules:\n- If text <30 words OR contains mostly emojis/placeholders → set ALL scores ≤2 and add `\"too_short\"` to vague_phrases.\n- Extract vague phrases (e.g., “good energy”, “smart guy”, “should be fine”) into `\"vague_phrases\"` array.\n\nReturn ONLY valid JSON in this schema:\n{\n  \"specificity\": <1–5>,\n  \"structure\": <1–5>,\n  \"bias_free_language\": <1–5>,\n  \"actionability\": <1–5>,\n  \"depth\": <1–5>,\n  \"vague_phrases\": [ ... ]\n}\n",
        "batching": {},
        "messages": {
          "messageValues": [
            {
              "message": "You are an Interview Feedback Quality Auditor.   Evaluate interview feedback for specificity, structure (STAR), bias-free language, actionability, and depth.   Be strict but fair. Always return **only valid JSON** that follows the given schema.Return ONLY valid JSON, no explanations, no markdown, no quotes wrapping the whole object.\n"
            },
            {
              "type": "HumanMessagePromptTemplate",
              "message": "=Role: {{$json[\"Role\"]}}   Stage: {{$json[\"Stage\"]}}   Feedback: {{$json[\"Feedback_Text\"]}}"
            }
          ]
        },
        "promptType": "define"
      },
      "typeVersion": 1.7
    },
    {
      "id": "0612aeb7-a3a2-4215-9910-4cd077e06586",
      "name": "AI-Antwort validieren",
      "type": "n8n-nodes-base.if",
      "position": [
        640,
        16
      ],
      "parameters": {
        "options": {},
        "conditions": {
          "options": {
            "version": 2,
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
          },
          "combinator": "and",
          "conditions": [
            {
              "id": "4901c65c-6aaf-4efe-a133-1fedfedc0bca",
              "operator": {
                "type": "string",
                "operation": "notEquals"
              },
              "leftValue": "={{ $json.text }}",
              "rightValue": "undefined "
            }
          ]
        }
      },
      "typeVersion": 2.2
    },
    {
      "id": "f89f5698-5d3c-4771-8b0b-511b32b9fc33",
      "name": "AI-JSON-Ausgabe parsen",
      "type": "n8n-nodes-base.code",
      "position": [
        1024,
        0
      ],
      "parameters": {
        "jsCode": "// OpenAI output comes as string in $json.text\nconst raw = $json[\"text\"];\n\n// Parse safely\nlet parsed;\ntry {\n  parsed = JSON.parse(raw);\n} catch (e) {\n  throw new Error(\"Invalid JSON returned by OpenAI: \" + raw);\n}\n\nreturn parsed;\n"
      },
      "typeVersion": 2
    },
    {
      "id": "1174cddb-9bf5-4582-a7ba-aed412336b7f",
      "name": "Gewichteten Qualitätsscore berechnen",
      "type": "n8n-nodes-base.code",
      "position": [
        1232,
        0
      ],
      "parameters": {
        "jsCode": "// Input = parsed JSON from AI\nconst data = $json;\n\n// Weights (can be adjusted or moved to Config sheet later)\nconst weights = {\n  specificity: 0.35,\n  structure: 0.15,\n  bias_free_language: 0.15,\n  actionability: 0.10,\n  depth: 0.25,\n};\n\n// ✅ Fallback for structure (AI might send structure or structure_star)\nconst structureValue = data.structure ?? data.structure_star ?? 0;\n\n// Calculate weighted score safely\nlet total = (\n  (data.specificity * weights.specificity) +\n  (structureValue * weights.structure) +\n  (data.bias_free_language * weights.bias_free_language) +\n  (data.actionability * weights.actionability) +\n  (data.depth * weights.depth)\n) / (\n  weights.specificity +\n  weights.structure +\n  weights.bias_free_language +\n  weights.actionability +\n  weights.depth\n);\n\n// Scale to 0–100\ntotal = Math.round(total * 20);\n\n// Flags\nconst flags = [];\nif ((data.specificity ?? 0) < 3 || (data.depth ?? 0) < 3) {\n  flags.push(\"low_detail\");\n}\nif ((data.bias_free_language ?? 0) < 3) {\n  flags.push(\"bias\");\n}\n\n// Format vague phrases if they exist\nlet vagueFormatted = \"\";\nif (Array.isArray(data.vague_phrases) && data.vague_phrases.length > 0) {\n  vagueFormatted = data.vague_phrases.map(p => `• ${p}`).join(\"\\n\");\n}\n\n// Return clean JSON\nreturn {\n  json: {\n    Score: total,\n    Flags: flags.join(\", \"),\n    LLM_JSON: JSON.stringify(data),\n    VaguePhrasesFormatted: vagueFormatted,   // for Slack message\n    row_number: $json.row_number,            // keep tracking the row\n    Role: $item(0).$node[\"Fetch Raw Feedback Data\"].json.Role,\n    Stage: $item(0).$node[\"Fetch Raw Feedback Data\"].json.Stage\n  }\n};\n"
      },
      "typeVersion": 2
    },
    {
      "id": "5d940fc3-8727-4dc4-9a58-133ab5180f08",
      "name": "Scores in Tabellenkalkulation speichern",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
        1600,
        -208
      ],
      "parameters": {
        "columns": {
          "value": {
            "Flags": "={{ $json.Flags }}",
            "Score": "={{ $json.Score }}",
            "LLM_JSON": "={{ $json.LLM_JSON }}",
            "row_number": "={{ $('Fetch Raw Feedback Data').item.json.row_number }}"
          },
          "schema": [
            {
              "id": "Timestamp",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Timestamp",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Candidate_ID",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Candidate_ID",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Role",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Role",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Stage",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Stage",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Interviewer_Email",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Interviewer_Email",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Feedback_Text",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Feedback_Text",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Score",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Score",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Flags",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Flags",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "LLM_JSON",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "LLM_JSON",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "row_number",
              "type": "number",
              "display": true,
              "removed": false,
              "readOnly": true,
              "required": false,
              "displayName": "row_number",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            }
          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [
            "row_number"
          ],
          "attemptToConvertTypes": false,
          "convertFieldsToString": false
        },
        "options": {},
        "operation": "update",
        "sheetName": {
          "__rl": true,
          "mode": "list",
          "value": 315277036,
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Uldk_4BxWbdZTDZxFUeohIfeBmGHHqVEl9Ogb0l6R8Y/edit#gid=315277036",
          "cachedResultName": "Raw_Feedback"
        },
        "documentId": {
          "__rl": true,
          "mode": "list",
          "value": "1Uldk_4BxWbdZTDZxFUeohIfeBmGHHqVEl9Ogb0l6R8Y",
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Uldk_4BxWbdZTDZxFUeohIfeBmGHHqVEl9Ogb0l6R8Y/edit?usp=drivesdk",
          "cachedResultName": "Interviewer Brief Pack "
        }
      },
      "credentials": {
        "googleSheetsOAuth2Api": {
          "id": "kpPEOLCGn963qpoh",
          "name": "automations@techdome.ai"
        }
      },
      "typeVersion": 4.6,
      "alwaysOutputData": true
    },
    {
      "id": "40df551b-4ca7-4268-a1fe-4ebb01fd304f",
      "name": "Feedback-Zusammenfassung an Interviewer senden",
      "type": "n8n-nodes-base.slack",
      "position": [
        1616,
        0
      ],
      "webhookId": "ddaa7632-9e35-4bd3-82d6-572d5cae84cc",
      "parameters": {
        "text": "=:mag: *Interview Feedback Audit*\n\n*Role:* {{ $json[\"Role\"] }}\n*Stage:* {{ $json[\"Stage\"] }}\n\n:bar_chart: *Score:* {{ $json[\"Score\"] }}/100  \n:warning: *Flags:* {{ $json[\"Flags\"] || \"none\" }}\n\n{{ $json[\"VaguePhrasesFormatted\"] ? \n    (\"_We noticed vague or incomplete feedback. Examples:_\\n\" + $json[\"VaguePhrasesFormatted\"] + \n    \"\\n\\n_To improve: try being more specific and evidence-based (e.g., STAR method)._\") \n    : \n    \"_✅ Great job! Your feedback was specific, structured, and bias-free._\" \n}}\n\nKeep it up — your detailed notes help us make fairer hiring decisions 🚀\n\n_Automated with this n8n workflow_\n",
        "user": {
          "__rl": true,
          "mode": "list",
          "value": "U09HMPVD466",
          "cachedResultName": "newscctv22"
        },
        "select": "user",
        "otherOptions": {}
      },
      "credentials": {
        "slackApi": {
          "id": "rNqvWj9TfChPVRYY",
          "name": "Slack account vivek"
        }
      },
      "typeVersion": 2.3
    },
    {
      "id": "de2e5d93-df58-41ef-a22f-e7a681308a64",
      "name": "Prüfen ob Schulung benötigt wird",
      "type": "n8n-nodes-base.if",
      "position": [
        1776,
        288
      ],
      "parameters": {
        "options": {},
        "conditions": {
          "options": {
            "version": 2,
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "loose"
          },
          "combinator": "and",
          "conditions": [
            {
              "id": "62f94225-d1b6-42a0-a3e9-7afceb9b937d",
              "operator": {
                "type": "number",
                "operation": "lt"
              },
              "leftValue": "={{$json[\"Score\"]}}",
              "rightValue": 50
            }
          ]
        },
        "looseTypeValidation": true
      },
      "typeVersion": 2.2
    },
    {
      "id": "49b80e52-27d3-455d-8082-e3e0f2365a69",
      "name": "Schulungsempfehlungen senden",
      "type": "n8n-nodes-base.slack",
      "position": [
        2064,
        272
      ],
      "webhookId": "ddaa7632-9e35-4bd3-82d6-572d5cae84cc",
      "parameters": {
        "text": "=:books: *Training Recommendation*\n\nYour interview feedback for **{{$json[\"Role\"]}} ({{$json[\"Stage\"]}})** was reviewed.\n\n📊 **Score:** {{$json[\"Score\"]}}/100  \n⚠️ **Flags:** {{$json[\"Flags\"] || \"none\"}}  \n\nWe noticed vague or incomplete feedback. Here are some examples:  \n{{  $json.VaguePhrasesFormatted }}\n\nTo improve: try using structured, evidence-based feedback (e.g., STAR method).  \n\n👉 Helpful resources:  \n📘 [STAR Method Guide](https://example.com/star-training)  \n🎥 [Bias-Free Interviewing Video](https://example.com/interview-bias)  \n\nYour detailed notes help us make fairer hiring decisions 🚀\n",
        "user": {
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