{"workflow":{"id":13876,"name":"Screen CVs with OpenAI and PostgreSQL using chained prompts","views":2,"recentViews":0,"totalViews":2,"createdAt":"2026-03-04T23:31:34.839Z","description":"## How it works\n\n- Webhook receives a job ID and list of candidate IDs from your database\n- If the job has no template yet, **Prompt 0** reads the job description and automatically extracts mandatory requirements, differentials, behavioral competencies and sets the weight of each criterion\n- For each candidate, 3 prompts run sequentially with accumulated context:\n  - **Prompt 1** scores the candidate (0–100) against the job template using calibration anchors to avoid score inflation, plus a breakdown score per criterion\n  - **Prompt 2** receives the score as context and identifies strengths with concrete resume evidence, separating critical gaps (missing mandatory requirements) from secondary gaps (missing differentials)\n  - **Prompt 3** receives the gaps as context and generates personalized interview questions for that specific candidate — not generic HR templates\n- Results are saved directly to PostgreSQL after each candidate\n- When all candidates are processed, **Prompt 4** automatically generates an executive summary of the entire pool with recommendations on who to interview\n\n## Set up steps\n\n- Add your **OpenAI credentials** to all AI nodes (~2 min)\n- Add your **PostgreSQL credentials** to all Postgres nodes (~2 min)\n- Create the required tables using the SQL schema provided in the workflow sticky note (~5 min)\n- Trigger via `POST /webhook/cv-analyze` with `{ \"job_id\": 1, \"candidate_ids\": [1, 2, 3] }`","workflow":{"id":"FoMizxWit6hOJVzp","meta":{"instanceId":"58e8d6f88fe0fa710ea6cd8b8dc92c4f15ade950ad61feb0b84deb700c9e81cf","templateCredsSetupCompleted":true},"name":"AI CV Screening with Chained Prompts","tags":[],"nodes":[{"id":"0cb0572a-b6e0-49e4-a9f2-c7249f9df88a","name":"Overview","type":"n8n-nodes-base.stickyNote","position":[-272,64],"parameters":{"color":3,"width":500,"height":832,"content":"## 🤖 AI CV Screening with Chained Prompts\n\nAutomatically screen resumes using 4 sequential AI prompts, each building on the previous one's output. Results are saved directly to PostgreSQL — no external backend required.\n\n### How it works\n1. **Webhook** receives a job ID + list of candidate IDs. Candidates and job must already exist in the database.\n2. **Prompt 0** extracts a structured job template (requirements, differentials, behavioral competencies and weights) from the job description — runs only if `gabarito` is null in the jobs table.\n3. **Prompts 1–3** run for each candidate in a loop:\n   - **Prompt 1** scores the candidate (0–100) against the job template with calibration anchors to avoid score inflation, plus per-criteria scores\n   - **Prompt 2** uses the score as context to identify concrete strengths (with CV evidence) and critical vs secondary gaps\n   - **Prompt 3** uses the gaps as context to generate personalized interview questions for that specific candidate\n4. Results are saved directly to the `analyses` table and candidate status updated to `processed`\n5. **Prompt 4** runs automatically when all candidates are processed — generates an executive summary saved to `job_summaries`\n\n### Setup\n- Add your **OpenAI API credentials** to all AI nodes\n- Add your **PostgreSQL credentials** to all Postgres nodes\n- Create the required tables using the SQL schema in the sticky note below\n- Trigger via `POST /webhook/cv-analyze` with `{ \"job_id\": 1, \"candidate_ids\": [1, 2, 3] }`\n\n### Customization\n- Swap `gpt-4.1-mini` for a more powerful model for higher accuracy\n- Adjust scoring anchors in Prompt 1 to match your hiring standards\n- Add more criteria to the job template in Prompt 0"},"typeVersion":1},{"id":"5ef87983-e5bb-49f1-9ea7-7842acb476d2","name":"Database Schema","type":"n8n-nodes-base.stickyNote","position":[800,672],"parameters":{"width":708,"height":880,"content":"## 🗄️ Required Database Schema\n\n```sql\nCREATE TABLE jobs (\n  id SERIAL PRIMARY KEY,\n  title VARCHAR(255) NOT NULL,\n  description TEXT NOT NULL,\n  gabarito JSONB,\n  status VARCHAR(20) DEFAULT 'draft',\n  created_at TIMESTAMP DEFAULT NOW()\n);\n\nCREATE TABLE candidates (\n  id SERIAL PRIMARY KEY,\n  job_id INTEGER REFERENCES jobs(id) ON DELETE CASCADE,\n  name VARCHAR(255),\n  filename VARCHAR(255),\n  cv_text TEXT,\n  status VARCHAR(20) DEFAULT 'pending',\n  created_at TIMESTAMP DEFAULT NOW()\n);\n\nCREATE TABLE analyses (\n  id SERIAL PRIMARY KEY,\n  candidate_id INTEGER REFERENCES candidates(id) ON DELETE CASCADE,\n  job_id INTEGER REFERENCES jobs(id) ON DELETE CASCADE,\n  score INTEGER,\n  nivel_aderencia VARCHAR(20),\n  justificativa_score TEXT,\n  pontos_fortes JSONB,\n  gaps_criticos JSONB,\n  gaps_secundarios JSONB,\n  perguntas_entrevista JSONB,\n  score_criterios JSONB,\n  created_at TIMESTAMP DEFAULT NOW(),\n  CONSTRAINT analyses_candidate_unique UNIQUE (candidate_id)\n);\n\nCREATE TABLE job_summaries (\n  id SERIAL PRIMARY KEY,\n  job_id INTEGER REFERENCES jobs(id) ON DELETE CASCADE,\n  total_analisados INTEGER,\n  recomendados JSONB,\n  destaque TEXT,\n  gap_comum TEXT,\n  resumo TEXT,\n  created_at TIMESTAMP DEFAULT NOW(),\n  CONSTRAINT job_summaries_job_id_key UNIQUE (job_id)\n);\n```"},"typeVersion":1},{"id":"e04df0d8-215c-4c7c-9c76-e49cd768cbdf","name":"Input Format","type":"n8n-nodes-base.stickyNote","position":[304,64],"parameters":{"width":340,"height":232,"content":"## 📥 Webhook Input\nSend a POST request:\n```json\n{\n  \"job_id\": 1,\n  \"candidate_ids\": [1, 2, 3]\n}\n```\nCandidates and job must already exist in the database with `cv_text` populated."},"typeVersion":1},{"id":"e3297343-eac2-4658-a1a7-19d66f2df74f","name":"Prompt 0 Section","type":"n8n-nodes-base.stickyNote","position":[832,176],"parameters":{"width":700,"height":140,"content":"## 🧠 Prompt 0 — Job Template Extraction\nRuns only when `gabarito` is null in the jobs table.\nExtracts structured requirements and sets weights automatically."},"typeVersion":1},{"id":"68d2ff58-4421-422b-8a6b-008b40527bde","name":"Candidate Loop Section","type":"n8n-nodes-base.stickyNote","position":[2112,64],"parameters":{"width":1700,"height":140,"content":"## 🔄 Candidate Loop\nProcesses one candidate at a time.\nPrompts 1 → 2 → 3 run sequentially,\neach using the previous output as context.\nResults saved to Postgres after each candidate."},"typeVersion":1},{"id":"512b3240-cb2c-4d8a-9ad7-be87df671b62","name":"Summary Section","type":"n8n-nodes-base.stickyNote","position":[4432,64],"parameters":{"width":1100,"height":140,"content":"## 📊 Executive Summary — Prompt 4\nRuns once when all candidates are processed (pending = 0).\nSaves pool-level recommendation to job_summaries table."},"typeVersion":1},{"id":"7b48fa44-ea08-4c46-b89e-388d3ae9ee0e","name":"Receive CVs","type":"n8n-nodes-base.webhook","position":[304,360],"webhookId":"3a70a559-3905-44d1-8837-f983b30f8354","parameters":{"path":"cv-analyze","options":{},"httpMethod":"POST"},"typeVersion":2.1},{"id":"6dbd0cb2-dba2-46dd-a355-eb8bb474e68b","name":"Fetch Job and Candidates","type":"n8n-nodes-base.postgres","position":[528,360],"parameters":{"query":"SELECT j.id AS job_id, j.title, j.description, j.gabarito, array_agg(c.id) AS candidate_ids, json_agg(json_build_object('id', c.id, 'name', c.name, 'cv_text', c.cv_text)) AS candidates FROM jobs j JOIN candidates c ON c.job_id = j.id WHERE j.id = {{ $json.body.job_id }} AND c.id = ANY(ARRAY[{{ $json.body.candidate_ids.join(',') }}]::int[]) GROUP BY j.id, j.title, j.description, j.gabarito","options":{},"operation":"executeQuery"},"credentials":{"postgres":{"id":"SokBhjvxZoBINTiO","name":"Prompt"}},"typeVersion":2.5},{"id":"4cfed364-6856-4b72-a781-dbcc5e784879","name":"Job Template exists?","type":"n8n-nodes-base.if","position":[752,360],"parameters":{"options":{},"conditions":{"options":{"version":2,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"id":"cond-gabarito","operator":{"type":"object","operation":"notEmpty","singleValue":true},"leftValue":"={{ $json.gabarito }}","rightValue":""}]}},"typeVersion":2.2},{"id":"e0574622-0404-4739-8f86-64e9f2061a5a","name":"Prompt 0 — Extract Job Template","type":"@n8n/n8n-nodes-langchain.openAi","position":[976,432],"parameters":{"modelId":{"__rl":true,"mode":"list","value":"gpt-4.1-mini","cachedResultName":"GPT-4.1-MINI"},"options":{},"responses":{"values":[{"role":"system","content":"You are an HR and recruitment specialist. Analyze the job description and extract information in a structured way. Return ONLY valid JSON, no additional text, no markdown, no backticks."},{"content":"=JOB DESCRIPTION:\n{{ $('Fetch Job and Candidates').item.json.description }}\n\nReturn ONLY valid JSON in this format:\n{\n  \"mandatory_requirements\": [\"list of mandatory requirements\"],\n  \"differential_requirements\": [\"list of differentials\"],\n  \"behavioral_competencies\": [\"list of behavioral competencies\"],\n  \"seniority_level\": \"junior|mid|senior|specialist\",\n  \"area\": \"area of expertise\",\n  \"weights\": {\n    \"mandatory_requirements\": 0.5,\n    \"differential_requirements\": 0.2,\n    \"behavioral_competencies\": 0.2,\n    \"experience\": 0.1\n  }\n}\n\nWeights must sum to 1.0 and reflect the importance of each criterion for THIS specific job."}]},"builtInTools":{}},"credentials":{"openAiApi":{"id":"lEM4Vz2E6WMNTSrX","name":"OpenAi account"}},"typeVersion":2.1},{"id":"1f095e0e-bb1e-4eb7-b2bf-593e775928bb","name":"Save Job Template","type":"n8n-nodes-base.postgres","position":[1328,432],"parameters":{"query":"UPDATE jobs SET gabarito = '{{ $json.output[0].content[0].text }}'::jsonb WHERE id = {{ $('Fetch Job and Candidates').item.json.job_id }} RETURNING gabarito","options":{},"operation":"executeQuery"},"credentials":{"postgres":{"id":"SokBhjvxZoBINTiO","name":"Prompt"}},"typeVersion":2.5},{"id":"aed6cd32-e56a-482a-85ec-3a3ccde8e35f","name":"Prepare Candidates","type":"n8n-nodes-base.code","position":[1552,360],"parameters":{"jsCode":"const jobData = $('Fetch Job and Candidates').first().json;\n\nlet gabarito = jobData.gabarito;\n\nif (!gabarito) {\n  const raw = $('Prompt 0 — Extract Job Template').first().json.output[0].content[0].text;\n  gabarito = JSON.parse(raw);\n}\n\nconst candidates = typeof jobData.candidates === 'string'\n  ? JSON.parse(jobData.candidates)\n  : jobData.candidates;\n\nreturn candidates.map(c => ({\n  json: {\n    job_id:         jobData.job_id,\n    job_title:      jobData.title,\n    gabarito:       gabarito,\n    candidate_id:   c.id,\n    candidate_name: c.name,\n    cv_text:        c.cv_text\n  }\n}));"},"typeVersion":2},{"id":"d974a929-57f3-49de-821b-77a41d46724e","name":"Loop Candidates","type":"n8n-nodes-base.splitInBatches","position":[1776,360],"parameters":{"options":{}},"typeVersion":3},{"id":"414e3332-8857-4b7a-8f19-4083e60516a1","name":"Prompt 1 — Score","type":"@n8n/n8n-nodes-langchain.openAi","position":[2000,288],"parameters":{"modelId":{"__rl":true,"mode":"list","value":"gpt-4.1-mini","cachedResultName":"GPT-4.1-MINI"},"options":{},"responses":{"values":[{"role":"system","content":"You are a senior recruitment specialist with 15 years of experience. You evaluate resumes with technical rigor and impartiality. Never inflate scores — an average candidate should receive an average score. Return ONLY valid JSON, no additional text, no markdown, no backticks."},{"content":"=JOB TEMPLATE:\n{{ JSON.stringify($json.gabarito) }}\n\nCANDIDATE RESUME:\n{{ $json.cv_text }}\n\nSCORING RULES — follow strictly:\n- 90-100: Meets ALL mandatory requirements with proven, documented experience. Has most differentials.\n- 70-89: Meets most mandatory requirements. Minor gaps compensated by other strengths.\n- 50-69: Partially meets mandatory requirements. Relevant but non-eliminatory gaps.\n- 30-49: Critical gaps in mandatory requirements. Candidate would need significant development.\n- 0-29: Does not meet the minimum requirements for the role.\n\nINSTRUCTIONS:\n- Consider PROVEN experience, not just superficial keyword mentions.\n- Penalize absence of mandatory requirements proportionally to weights in the job template.\n- Value real projects and concrete results over theoretical knowledge.\n- Use the job template weights to calculate the final score AND per-criteria scores.\n\nReturn ONLY valid JSON:\n{\n  \"score\": number between 0 and 100,\n  \"adherence_level\": \"Low|Medium|High|Excellent\",\n  \"justification\": \"3 objective sentences explaining the score. Cite concrete evidence from the resume and specific gaps that justify the rating.\",\n  \"criteria_scores\": {\n    \"mandatory_requirements\": number between 0 and 100,\n    \"differential_requirements\": number between 0 and 100,\n    \"behavioral_competencies\": number between 0 and 100,\n    \"experience\": number between 0 and 100\n  }\n}"}]},"builtInTools":{}},"credentials":{"openAiApi":{"id":"lEM4Vz2E6WMNTSrX","name":"OpenAi account"}},"typeVersion":2.1},{"id":"ab0906db-3de1-4f4e-b749-a680b16d8cd9","name":"Prompt 2 — Gaps","type":"@n8n/n8n-nodes-langchain.openAi","position":[2352,288],"parameters":{"modelId":{"__rl":true,"mode":"list","value":"gpt-4.1-mini","cachedResultName":"GPT-4.1-MINI"},"options":{},"responses":{"values":[{"role":"system","content":"You are a senior recruitment specialist. Analyze resumes with surgical precision — cite concrete evidence, not generalities. Return ONLY valid JSON, no additional text, no markdown, no backticks."},{"content":"=JOB TEMPLATE:\n{{ JSON.stringify($('Loop Candidates').item.json.gabarito) }}\n\nCANDIDATE RESUME:\n{{ $('Loop Candidates').item.json.cv_text }}\n\nSCORE RESULT:\n{{ $('Prompt 1 — Score').item.json.output[0].content[0].text }}\n\nINSTRUCTIONS:\n- For each strength: cite the EXACT evidence from the resume (project, company, technology mentioned).\n- For critical gaps: list only MANDATORY requirements from the template that are absent or insufficiently proven.\n- For secondary gaps: list absent differentials and unevidenced behavioral competencies.\n- Be specific — \"Experience with n8n in self-hosted production\" is better than \"Automation experience\".\n- Do not repeat information between strengths and gaps.\n\nReturn ONLY valid JSON:\n{\n  \"strengths\": [\"strength with resume evidence — max 8 items\"],\n  \"critical_gaps\": [\"absent or insufficient mandatory requirement — be specific\"],\n  \"secondary_gaps\": [\"absent differential or behavioral competency\"]\n}"}]},"builtInTools":{}},"credentials":{"openAiApi":{"id":"lEM4Vz2E6WMNTSrX","name":"OpenAi account"}},"typeVersion":2.1},{"id":"bccbda40-5c02-490b-b2bd-ee1a98ad1e9e","name":"Prompt 3 — Interview Questions","type":"@n8n/n8n-nodes-langchain.openAi","position":[2704,288],"parameters":{"modelId":{"__rl":true,"mode":"list","value":"gpt-4.1-mini","cachedResultName":"GPT-4.1-MINI"},"options":{},"responses":{"values":[{"role":"system","content":"You are a recruitment specialist with experience in technical and behavioral competency-based interviews. Generate questions that reveal the real candidate, not the prepared one. Return ONLY valid JSON, no additional text, no markdown, no backticks."},{"content":"=JOB TEMPLATE:\n{{ JSON.stringify($('Loop Candidates').item.json.gabarito) }}\n\nCANDIDATE PROFILE:\n- Name: {{ $('Loop Candidates').item.json.candidate_name }}\n- Score: {{ $('Prompt 1 — Score').item.json.output[0].content[0].text }}\n- Full analysis: {{ $('Prompt 2 — Gaps').item.json.output[0].content[0].text }}\n\nINSTRUCTIONS:\n- Generate PERSONALIZED questions for this candidate — not generic HR questions.\n- Prefer situational questions: \"Tell me about a time when...\" or \"How would you handle...\"\n- Avoid yes/no questions.\n- For critical gaps: investigate non-confrontationally — \"What is your experience with X?\" instead of \"Do you know X?\".\n- For strengths: dig deeper with requests for concrete examples and measurable results.\n- Vary types: at least 2 technical, 2 behavioral, 1 situational.\n- Each question must have a clear and distinct objective.\n\nReturn ONLY valid JSON:\n{\n  \"interview_questions\": [\n    {\n      \"question\": \"full question text\",\n      \"objective\": \"what this question specifically reveals about this candidate\",\n      \"type\": \"technical|behavioral|situational\"\n    }\n  ]\n}\n\nGenerate between 5 and 7 questions."}]},"builtInTools":{}},"credentials":{"openAiApi":{"id":"lEM4Vz2E6WMNTSrX","name":"OpenAi account"}},"typeVersion":2.1},{"id":"01fcae4e-6bcb-48a8-bbd0-8e137e9ef190","name":"Build Analysis Payload","type":"n8n-nodes-base.code","position":[3056,288],"parameters":{"jsCode":"const candidate = $('Loop Candidates').item.json;\n\nconst score_data = JSON.parse($('Prompt 1 — Score').item.json.output[0].content[0].text);\nconst gaps_data  = JSON.parse($('Prompt 2 — Gaps').item.json.output[0].content[0].text);\nconst pergs_data = JSON.parse($('Prompt 3 — Interview Questions').item.json.output[0].content[0].text);\n\nreturn [{\n  json: {\n    candidate_id:          candidate.candidate_id,\n    job_id:                candidate.job_id,\n    score:                 score_data.score,\n    nivel_aderencia:       score_data.adherence_level,\n    justificativa_score:   score_data.justification,\n    score_criterios:       score_data.criteria_scores,\n    pontos_fortes:         gaps_data.strengths,\n    gaps_criticos:         gaps_data.critical_gaps,\n    gaps_secundarios:      gaps_data.secondary_gaps,\n    perguntas_entrevista:  pergs_data.interview_questions\n  }\n}];"},"typeVersion":2},{"id":"d9ba1c99-fb5d-46f4-8af1-62a9189b4c66","name":"Save Analysis","type":"n8n-nodes-base.postgres","position":[3280,288],"parameters":{"query":"INSERT INTO analyses (candidate_id, job_id, score, nivel_aderencia, justificativa_score, score_criterios, pontos_fortes, gaps_criticos, gaps_secundarios, perguntas_entrevista) VALUES ({{ $json.candidate_id }}, {{ $json.job_id }}, {{ $json.score }}, '{{ $json.nivel_aderencia }}', '{{ $json.justificativa_score.replace(/'/g, \"''\") }}', '{{ JSON.stringify($json.score_criterios) }}'::jsonb, '{{ JSON.stringify($json.pontos_fortes) }}'::jsonb, '{{ JSON.stringify($json.gaps_criticos) }}'::jsonb, '{{ JSON.stringify($json.gaps_secundarios) }}'::jsonb, '{{ JSON.stringify($json.perguntas_entrevista) }}'::jsonb) ON CONFLICT (candidate_id) DO UPDATE SET score = EXCLUDED.score, nivel_aderencia = EXCLUDED.nivel_aderencia, justificativa_score = EXCLUDED.justificativa_score, score_criterios = EXCLUDED.score_criterios, pontos_fortes = EXCLUDED.pontos_fortes, gaps_criticos = EXCLUDED.gaps_criticos, gaps_secundarios = EXCLUDED.gaps_secundarios, perguntas_entrevista = EXCLUDED.perguntas_entrevista","options":{},"operation":"executeQuery"},"credentials":{"postgres":{"id":"SokBhjvxZoBINTiO","name":"Prompt"}},"typeVersion":2.5},{"id":"a74272c7-8c2d-4b3b-a6ac-baa509eb8b12","name":"Update Candidate Status","type":"n8n-nodes-base.postgres","position":[3504,288],"parameters":{"query":"UPDATE candidates SET status = 'processed' WHERE id = {{ $('Build Analysis Payload').item.json.candidate_id }}","options":{},"operation":"executeQuery"},"credentials":{"postgres":{"id":"SokBhjvxZoBINTiO","name":"Prompt"}},"typeVersion":2.5},{"id":"25b0eaf5-2598-48d0-8be9-fb569d37ea03","name":"Check Pending Candidates","type":"n8n-nodes-base.postgres","position":[3728,288],"parameters":{"query":"SELECT COUNT(*) AS pending FROM candidates WHERE job_id = {{ $('Build Analysis Payload').item.json.job_id }} AND status = 'pending'","options":{},"operation":"executeQuery"},"credentials":{"postgres":{"id":"SokBhjvxZoBINTiO","name":"Prompt"}},"typeVersion":2.5},{"id":"3398f657-1294-4e5f-a006-6f8ed5c2e9bc","name":"All Candidates Processed?","type":"n8n-nodes-base.if","position":[3952,360],"parameters":{"options":{},"conditions":{"options":{"version":2,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"id":"cond-pending","operator":{"type":"number","operation":"equals"},"leftValue":"={{ $json.pending }}","rightValue":0}]}},"typeVersion":2.2},{"id":"397c9184-d234-47d8-bc3a-49087c7874e1","name":"Update Job Status","type":"n8n-nodes-base.postgres","position":[4176,360],"parameters":{"query":"UPDATE jobs SET status = 'done' WHERE id = {{ $('Build Analysis Payload').item.json.job_id }}","options":{},"operation":"executeQuery"},"credentials":{"postgres":{"id":"SokBhjvxZoBINTiO","name":"Prompt"}},"typeVersion":2.5},{"id":"bb43a8a2-8431-47dd-b436-2f71c96430a6","name":"Fetch Full Pool","type":"n8n-nodes-base.postgres","position":[4400,360],"parameters":{"query":"SELECT c.name, a.score, a.nivel_aderencia, a.justificativa_score, a.pontos_fortes, a.gaps_criticos FROM candidates c JOIN analyses a ON a.candidate_id = c.id WHERE c.job_id = {{ $('Build Analysis Payload').item.json.job_id }} ORDER BY a.score DESC","options":{},"operation":"executeQuery"},"credentials":{"postgres":{"id":"SokBhjvxZoBINTiO","name":"Prompt"}},"typeVersion":2.5},{"id":"e4934c9f-1b20-4bbd-b7d8-2512c665f51a","name":"Fetch Job for Summary","type":"n8n-nodes-base.postgres","position":[4624,360],"parameters":{"query":"SELECT title, gabarito FROM jobs WHERE id = {{ $('Build Analysis Payload').item.json.job_id }}","options":{},"operation":"executeQuery"},"credentials":{"postgres":{"id":"SokBhjvxZoBINTiO","name":"Prompt"}},"typeVersion":2.5},{"id":"f8529e9e-1443-4b42-a6c7-c59af19f51c6","name":"Prompt 4 — Executive Summary","type":"@n8n/n8n-nodes-langchain.openAi","position":[4848,360],"parameters":{"modelId":{"__rl":true,"mode":"list","value":"gpt-4.1-mini","cachedResultName":"GPT-4.1-MINI"},"options":{},"responses":{"values":[{"role":"system","content":"You are a senior HR consultant with experience in executive selection. Analyze candidate pools with strategic vision and objective language. Return ONLY valid JSON, no additional text, no markdown, no backticks."},{"content":"=JOB: {{ $('Fetch Job for Summary').item.json.title }}\n\nJOB TEMPLATE:\n{{ JSON.stringify($('Fetch Job for Summary').item.json.gabarito) }}\n\nANALYZED CANDIDATES (ordered by score):\n{{ JSON.stringify($('Fetch Full Pool').all().map(i => i.json)) }}\n\nAnalyze the complete candidate pool and generate an executive summary for the HR team.\n\nINSTRUCTIONS:\n- Be direct and objective — HR needs to make a quick decision\n- Clearly identify who to recommend for interview (score >= 70)\n- Point out the most recurring gap in the pool — this may indicate an issue with the job description or the market\n- The summary should have at most 3 sentences — concise and executive\n- Do not repeat information already in individual candidate analyses\n\nReturn ONLY valid JSON:\n{\n  \"total_analyzed\": total number of candidates as integer,\n  \"recommended\": [\"Names of candidates with score >= 70\"],\n  \"destaque\": 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}\n}];"},"typeVersion":2},{"id":"221e40ba-5662-45ac-9200-787a460e9c8b","name":"Save Summary","type":"n8n-nodes-base.postgres","position":[5424,360],"parameters":{"query":"INSERT INTO job_summaries (job_id, total_analisados, recomendados, destaque, gap_comum, resumo) VALUES ({{ $json.job_id }}, {{ $json.total_analisados }}, '{{ JSON.stringify($json.recomendados) }}'::jsonb, '{{ $json.destaque.replace(/'/g, \"''\") }}', '{{ $json.gap_comum.replace(/'/g, \"''\") }}', '{{ $json.resumo.replace(/'/g, \"''\") }}') ON CONFLICT (job_id) DO UPDATE SET total_analisados = EXCLUDED.total_analisados, recomendados = EXCLUDED.recomendados, destaque = EXCLUDED.destaque, gap_comum = EXCLUDED.gap_comum, resumo = EXCLUDED.resumo","options":{},"operation":"executeQuery"},"credentials":{"postgres":{"id":"SokBhjvxZoBINTiO","name":"Prompt"}},"typeVersion":2.5}],"active":false,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"dc533c0c-4541-411b-a15d-81cd4c668502","connections":{"Receive 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