LinkedIn上の求人情報をDecodoとGPT-4o-miniでクロール＆分析
上級
これはHR, AI RAG分野の自動化ワークフローで、19個のノードを含みます。主にSet, Code, Merge, Function, GoogleSheetsなどのノードを使用。 LinkedIn、Decodo、GPT-4o-miniを使って人材のインサイトとデータドリブシングを抽出・分析
前提条件
- •Google Sheets API認証情報
- •OpenAI API Key
使用ノード (19)
カテゴリー
ワークフロープレビュー
ノード接続関係を可視化、ズームとパンをサポート
ワークフローをエクスポート
以下のJSON設定をn8nにインポートして、このワークフローを使用できます
{
"id": "FsM5tVDtDHt1mXCp",
"meta": {
"instanceId": "885b4fb4a6a9c2cb5621429a7b972df0d05bb724c20ac7dac7171b62f1c7ef40",
"templateCredsSetupCompleted": true
},
"name": "LinkedIn Talent Intelligence and Data Mining using Decodo with GPT-4o-mini",
"tags": [
{
"id": "ddPkw7Hg5dZhQu2w",
"name": "AI",
"createdAt": "2025-04-13T05:38:08.053Z",
"updatedAt": "2025-04-13T05:38:08.053Z"
},
{
"id": "ZOwtAMLepQaGW76t",
"name": "Building Blocks",
"createdAt": "2025-04-13T15:23:40.462Z",
"updatedAt": "2025-04-13T15:23:40.462Z"
},
{
"id": "Kujft2FOjmOVQAmJ",
"name": "Engineering",
"createdAt": "2025-04-09T01:31:00.558Z",
"updatedAt": "2025-04-09T01:31:00.558Z"
},
{
"id": "rKOa98eAi3IETrLu",
"name": "HR",
"createdAt": "2025-04-13T04:59:30.580Z",
"updatedAt": "2025-04-13T04:59:30.580Z"
}
],
"nodes": [
{
"id": "ebfaa6a4-3400-4867-bea6-4d88beee5661",
"name": "ワークフロー実行時",
"type": "n8n-nodes-base.manualTrigger",
"position": [
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128
],
"parameters": {},
"typeVersion": 1
},
{
"id": "ca339fd3-e09e-4a2b-a370-b903853769db",
"name": "付箋",
"type": "n8n-nodes-base.stickyNote",
"position": [
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],
"parameters": {
"width": 496,
"height": 688,
"content": "## Purpose\n\nAdvanced talent intelligence platform that performs data mining on LinkedIn profiles to generate comprehensive recruitment insights and predictive job matching analytics.\n\n### Flow Summary:\n\n- Input: LinkedIn profile URL and Job Description.\n\n- Decodo: Performs comprehensive web scraping of LinkedIn profile data\n\n- OpenAI GPT-4o-mini: Extracts structured profile data with advanced parsing\n\n- Advanced Data Mining Engine: Performs deep analytics including skills analysis, experience intelligence, cultural fit assessment, career trajectory insights, and competitive advantage identification\n\n- Advanced Summarization Engine: Performs the comprehensive and abstraction summarization of the LinkedIn profile content\n\n- Output: Comprehensive JSON with structured profile data and advanced recruitment intelligence ready for ATS, CRM systems, or talent analytics platforms.\n\n### Use Case:\nIdeal for data-driven recruiters, talent acquisition teams, HR analytics professionals, and recruitment technology platforms requiring sophisticated candidate analysis and predictive matching capabilities"
},
"typeVersion": 1
},
{
"id": "75947f51-f304-478b-b7f3-1a5c824cd7a0",
"name": "Decodo",
"type": "@decodo/n8n-nodes-decodo.decodo",
"position": [
352,
128
],
"parameters": {
"geo": "={{ $json.geo }}",
"url": "={{ $json.url }}"
},
"credentials": {
"decodoApi": {
"id": "7xLvINFuwxDiyBde",
"name": "Decodo Credentials account"
}
},
"retryOnFail": true,
"typeVersion": 1
},
{
"id": "0342ac07-77f0-4ae5-a98a-1ded33654cd0",
"name": "入力フィールド設定",
"type": "n8n-nodes-base.set",
"position": [
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],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "01a2dade-6674-4158-8303-97a1151d3965",
"name": "url",
"type": "string",
"value": "https://www.linkedin.com/in/ranjan-dailata/"
},
{
"id": "82a8ccde-dc3c-4ed1-b81e-bbcf44a2cf11",
"name": "geo",
"type": "string",
"value": "India"
},
{
"id": "b8104d33-8d7a-415c-935b-1e8432b8a756",
"name": "jobDescription",
"type": "string",
"value": "Senior Software Engineer with 5+ years of experience in React, Node.js, and cloud technologies. Must have experience with AWS, Docker, and agile development methodologies. Looking for someone who can lead technical projects and mentor junior developers."
}
]
}
},
"typeVersion": 3.4
},
{
"id": "46ef866f-63fc-4a8e-9dae-16a5efb851e4",
"name": "構造化データ抽出ツール",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
720,
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],
"parameters": {
"text": "=Parse and build content using {{ $json.data.results[0].content }} in JSON Resume Schema.\n\nDo not output your own response or thoughts or suggestions. \n\nNeed to output a proper fully formatted JSON response.",
"batching": {},
"messages": {
"messageValues": [
{
"message": "You are an expert resume parser"
}
]
},
"promptType": "define"
},
"retryOnFail": true,
"typeVersion": 1.7,
"alwaysOutputData": true
},
{
"id": "9bcbd10e-5ca1-4c28-be64-9aa847d81f57",
"name": "高度なデータマイニングとプロファイル・ジョブ分析",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
720,
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],
"parameters": {
"text": "=Perform advanced data mining analysis on the candidate profile and job description.\n\nCandidate Profile:\n{{ $json.data.results[0].content }}\n\nJob Description:\n{{ $('Set the Input Fields').item.json.jobDescription }}\n\nConduct comprehensive data mining including:\n\n1. SKILLS ANALYSIS:\n- Technical skills extraction and categorization\n- Skill level assessment (Beginner/Intermediate/Advanced/Expert)\n- Skills gap analysis against job requirements\n- Transferable skills identification\n\n2. EXPERIENCE INTELLIGENCE:\n- Career progression pattern analysis\n- Industry experience depth assessment\n- Leadership and management experience evaluation\n- Project complexity and scale analysis\n\n3. CULTURAL & CONTEXTUAL FIT:\n- Company culture alignment indicators\n- Work style preferences inference\n- Communication style analysis\n- Learning agility indicators\n\n4. CAREER TRAJECTORY INSIGHTS:\n- Career growth velocity\n- Job stability patterns\n- Promotion frequency analysis\n- Industry transition analysis\n\n5. COMPETITIVE ADVANTAGES:\n- Unique selling points identification\n- Market positioning analysis\n- Salary expectation indicators\n- Availability timeline assessment\n\nOutput comprehensive JSON with data-driven insights for recruitment decisions.\n\nDo not output your own response or thoughts or suggestions. \n\nNeed to output a proper fully formatted JSON response.",
"batching": {},
"messages": {
"messageValues": [
{
"message": "You are a senior data scientist and HR analytics expert specializing in talent intelligence and predictive recruitment analytics."
}
]
},
"promptType": "define"
},
"retryOnFail": true,
"typeVersion": 1.7,
"alwaysOutputData": true
},
{
"id": "e73a1e3d-1bf8-4fa6-897d-30a7210ea044",
"name": "付箋1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-144,
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],
"parameters": {
"color": 7,
"width": 384,
"height": 208,
"content": "\n\nOpenAI GPT-4o-mini for the Structured Data Extraction and Data Mining Purposes"
},
"typeVersion": 1
},
{
"id": "216e80b9-2856-4eea-b19f-48330ad815c9",
"name": "付箋2",
"type": "n8n-nodes-base.stickyNote",
"position": [
528,
-288
],
"parameters": {
"color": 4,
"width": 608,
"height": 1104,
"content": "## Data Miner"
},
"typeVersion": 1
},
{
"id": "9276bfc2-eca8-45cc-8ae6-3882a0862e5e",
"name": "マージ",
"type": "n8n-nodes-base.merge",
"position": [
1376,
160
],
"parameters": {
"numberInputs": 3
},
"typeVersion": 3.2
},
{
"id": "460a778d-cb17-4265-821a-a36b0c387772",
"name": "シートに行を追加または更新",
"type": "n8n-nodes-base.googleSheets",
"position": [
1600,
176
],
"parameters": {
"columns": {
"value": {},
"schema": [],
"mappingMode": "autoMapInputData",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "appendOrUpdate",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1oNScKT5lCIFJTXZ006vAu5IJEC_nWHTOil56tvB3JYU/edit#gid=0",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1oNScKT5lCIFJTXZ006vAu5IJEC_nWHTOil56tvB3JYU",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1oNScKT5lCIFJTXZ006vAu5IJEC_nWHTOil56tvB3JYU/edit?usp=drivesdk",
"cachedResultName": "LinkedIn Talent Intelligence"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "Zjoxh2BUZ6VXGQhA",
"name": "Google Sheets account"
}
},
"typeVersion": 4.7
},
{
"id": "6b579358-223c-4eff-a7dd-f7b521358f7f",
"name": "OpenAI チャットモデル",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
784,
336
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini",
"cachedResultName": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "vPKynKbDzJ5ZU4cU",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "05fad9fa-8048-403e-9b3a-0e423325d4c1",
"name": "OpenAI 構造化データ抽出用チャットモデル",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
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"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini",
"cachedResultName": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "vPKynKbDzJ5ZU4cU",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "4b71fb93-4540-476a-8346-bd0f1e6462f6",
"name": "要約ツール",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
736,
480
],
"parameters": {
"text": "=Perform abstractive and comprehensive summarize of the following {{ $json.data.results[0].content }} in JSON Resume Schema",
"batching": {},
"messages": {
"messageValues": [
{
"message": "You are an expert resume summarizer"
}
]
},
"promptType": "define",
"hasOutputParser": true
},
"retryOnFail": true,
"typeVersion": 1.7,
"alwaysOutputData": true
},
{
"id": "392c533e-3235-4953-9bf9-5235a8bc5922",
"name": "OpenAI 要約ツール用チャットモデル",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
704,
656
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini",
"cachedResultName": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "vPKynKbDzJ5ZU4cU",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "2e8fcea6-7f49-42da-8306-c4b8dab49b25",
"name": "構造化出力パーサー",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
912,
656
],
"parameters": {
"schemaType": "manual",
"inputSchema": "{\n\t\"type\": \"object\",\n\t\"properties\": {\n\t\t\"abstractive_summarizer\": {\n\t\t\t\"type\": \"string\"\n\t\t},\n \"comprehensive_summarizer\": {\n\t\t\t\"type\": \"string\"\n\t\t}\n\t}\n}"
},
"typeVersion": 1.3
},
{
"id": "b978cfed-6490-4d2b-9910-b1ae1f5f352d",
"name": "ディスクからのファイル読み書き",
"type": "n8n-nodes-base.readWriteFile",
"position": [
1584,
-176
],
"parameters": {
"options": {},
"fileName": "=C:\\\\{{ $('Extract Structured JSON').item.json.basics.name }}.json",
"operation": "write",
"dataPropertyName": "=data"
},
"typeVersion": 1
},
{
"id": "489bbb08-e7e3-4f52-8ed3-6be3b3dde98e",
"name": "Make バイナリ",
"type": "n8n-nodes-base.function",
"position": [
1376,
-176
],
"parameters": {
"functionCode": "items[0].binary = {\n data: {\n data: new Buffer(JSON.stringify(items[0].json, null, 2)).toString('base64')\n }\n};\nreturn items;"
},
"typeVersion": 1
},
{
"id": "e8e2248f-a2fe-41b9-822d-029a6b882ccf",
"name": "構造化JSON抽出",
"type": "n8n-nodes-base.code",
"position": [
1184,
-32
],
"parameters": {
"jsCode": "let text = $input.first().json.text;\nconst output = [];\n\n// Remove ```json ... ``` or ``` ... ``` wrappers\ntext = text\n .replace(/```json\\s*/gi, '')\n .replace(/```/g, '')\n .trim();\n\n// Parse the cleaned JSON text\nconst parsed = JSON.parse(text);\noutput.push({ json: parsed });\n\nreturn output;"
},
"typeVersion": 2
},
{
"id": "7e42f48d-a3a0-45d7-a3d9-87294702b618",
"name": "フォーマット済みJSON抽出",
"type": "n8n-nodes-base.code",
"position": [
1152,
176
],
"parameters": {
"jsCode": "let text = $input.first().json.text;\nconst output = [];\n\n// Remove ```json ... ``` or ``` ... ``` wrappers\ntext = text\n .replace(/```json\\s*/gi, '')\n .replace(/```/g, '')\n .trim();\n\n// Parse the cleaned JSON text\nconst parsed = JSON.parse(text);\noutput.push({ json: parsed });\n\nreturn output;"
},
"typeVersion": 2
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "2b574170-fea3-4899-a26f-5a8e95632934",
"connections": {
"9276bfc2-eca8-45cc-8ae6-3882a0862e5e": {
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{
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"type": "main",
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},
{
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"type": "main",
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}
]
]
},
"4b71fb93-4540-476a-8346-bd0f1e6462f6": {
"main": [
[
{
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"type": "main",
"index": 2
}
]
]
},
"489bbb08-e7e3-4f52-8ed3-6be3b3dde98e": {
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"type": "main",
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"6b579358-223c-4eff-a7dd-f7b521358f7f": {
"ai_languageModel": [
[
{
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"type": "ai_languageModel",
"index": 0
}
]
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},
"0342ac07-77f0-4ae5-a98a-1ded33654cd0": {
"main": [
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{
"node": "75947f51-f304-478b-b7f3-1a5c824cd7a0",
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"e8e2248f-a2fe-41b9-822d-029a6b882ccf": {
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{
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"2e8fcea6-7f49-42da-8306-c4b8dab49b25": {
"ai_outputParser": [
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"46ef866f-63fc-4a8e-9dae-16a5efb851e4": {
"main": [
[
{
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},
"460a778d-cb17-4265-821a-a36b0c387772": {
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[]
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"type": "ai_languageModel",
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"ebfaa6a4-3400-4867-bea6-4d88beee5661": {
"main": [
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{
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"type": "main",
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"9bcbd10e-5ca1-4c28-be64-9aa847d81f57": {
"main": [
[
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"05fad9fa-8048-403e-9b3a-0e423325d4c1": {
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}
}よくある質問
このワークフローの使い方は？
上記のJSON設定コードをコピーし、n8nインスタンスで新しいワークフローを作成して「JSONからインポート」を選択、設定を貼り付けて認証情報を必要に応じて変更してください。
このワークフローはどんな場面に適していますか？
上級 - 人事, AI RAG検索拡張
有料ですか？
このワークフローは完全無料です。ただし、ワークフローで使用するサードパーティサービス（OpenAI APIなど）は別途料金が発生する場合があります。
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ワークフロー情報
難易度
上級
ノード数19
カテゴリー2
ノードタイプ12
作成者
Ranjan Dailata
@ranjancseA Professional based out of India specialized in handling AI-powered automations. Contact me at ranjancse@gmail.com
外部リンク
n8n.ioで表示 →
このワークフローを共有