Audit de la qualité des entretiens

• •Token Bot Slack ou URL Webhook
• •Informations d'identification Google Sheets API
• •Clé API OpenAI

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  "name": "Interview Quality Audit",
  "tags": [],
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        "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"
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        "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"
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        "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"
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        "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"
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        "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"
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      "position": [
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      "parameters": {
        "width": 320,
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        "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"
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      "parameters": {
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        "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"
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        "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"
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        "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"
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        "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"
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        "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"
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      "name": "Évaluateur de Qualité IA (GPT-4o1",
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        "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",
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            {
              "type": "HumanMessagePromptTemplate",
              "message": "=Role: {{$json[\"Role\"]}}   Stage: {{$json[\"Stage\"]}}   Feedback: {{$json[\"Feedback_Text\"]}}"
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      "type": "n8n-nodes-base.code",
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        "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"
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        "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"
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              "defaultMatch": false,
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