HN 채용 정보 추출
고급
이것은HR, AI분야의자동화 워크플로우로, 20개의 노드를 포함합니다.주로 Set, Code, Limit, Filter, Airtable 등의 노드를 사용하며인공지능 기술을 결합하여 스마트 자동화를 구현합니다. Hacker News 채용 정보 추출 및 분석
사전 요구사항
- •Airtable API Key
- •대상 API의 인증 정보가 필요할 수 있음
- •OpenAI API Key
사용된 노드 (20)
워크플로우 미리보기
노드 연결 관계를 시각적으로 표시하며, 확대/축소 및 이동을 지원합니다
워크플로우 내보내기
다음 JSON 구성을 복사하여 n8n에 가져오면 이 워크플로우를 사용할 수 있습니다
{
"id": "0JsHmmyeHw5Ffz5m",
"meta": {
"instanceId": "d4d7965840e96e50a3e02959a8487c692901dfa8d5cc294134442c67ce1622d3",
"templateCredsSetupCompleted": true
},
"name": "HN Who is Hiring Scrape",
"tags": [],
"nodes": [
{
"id": "f7cdb3ee-9bb0-4006-829a-d4ce797191d5",
"name": "'워크플로우 테스트' 클릭 시",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-20,
-220
],
"parameters": {},
"typeVersion": 1
},
{
"id": "0475e25d-9bf4-450d-abd3-a04608a438a4",
"name": "스티커 메모",
"type": "n8n-nodes-base.stickyNote",
"position": [
60,
-620
],
"parameters": {
"width": 460,
"height": 340,
"content": "## Go to https://hn.algolia.com\n- filter by \"Ask HN: Who is hiring?\" (important with quotes for full match)\n- sort by date\n- Chrome Network Tab > find API call > click \"Copy as cURL\"\n- n8n HTTP node -> import cURL and paste \n- I've set the API key as Header Auth so you will have to do the above yourself to make this work"
},
"typeVersion": 1
},
{
"id": "a686852b-ff84-430b-92bb-ce02a6808e19",
"name": "분할 출력",
"type": "n8n-nodes-base.splitOut",
"position": [
400,
-220
],
"parameters": {
"options": {},
"fieldToSplitOut": "hits"
},
"typeVersion": 1
},
{
"id": "cdaaa738-d561-4fa0-b2c7-8ea9e6778eb1",
"name": "스티커 메모1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1260,
-620
],
"parameters": {
"width": 500,
"height": 340,
"content": "## Go to HN API \nhttps://github.com/HackerNews/API\n\nWe'll need following endpoints: \n- For example, a story: https://hacker-news.firebaseio.com/v0/item/8863.json?print=pretty\n- comment: https://hacker-news.firebaseio.com/v0/item/2921983.json?print=pretty\n\n"
},
"typeVersion": 1
},
{
"id": "4f353598-9e32-4be4-9e7b-c89cc05305fd",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
2680,
-20
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "Fbb2ueT0XP5xMRme",
"name": "OpenAi account 2"
}
},
"typeVersion": 1.2
},
{
"id": "5bd0d7cc-497a-497c-aa4c-589d9ceeca14",
"name": "Structured Output Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
2840,
-20
],
"parameters": {
"schemaType": "manual",
"inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"company\": {\n \"type\": [\n \"string\",\n null\n ],\n \"description\": \"Name of the hiring company\"\n },\n \"title\": {\n \"type\": [\n \"string\",\n null\n ],\n \"description\": \"Job title/role being advertised\"\n },\n \"location\": {\n \"type\": [\n \"string\",\n null\n ],\n \"description\": \"Work location including remote/hybrid status\"\n },\n \"type\": {\n \"type\": [\n \"string\",\n null\n ],\n \"enum\": [\n \"FULL_TIME\",\n \"PART_TIME\",\n \"CONTRACT\",\n \"INTERNSHIP\",\n \"FREELANCE\",\n null\n ],\n \"description\": \"Employment type (Full-time, Contract, etc)\"\n },\n \"work_location\": {\n \"type\": [\n \"string\",\n null\n ],\n \"enum\": [\n \"REMOTE\",\n \"HYBRID\",\n \"ON_SITE\",\n null\n ],\n \"description\": \"Work arrangement type\"\n },\n \"salary\": {\n \"type\": [\n \"string\",\n null\n ],\n \"description\": \"Compensation details if provided\"\n },\n \"description\": {\n \"type\": [\n \"string\",\n null\n ],\n \"description\": \"Main job description text including requirements and team info\"\n },\n \"apply_url\": {\n \"type\": [\n \"string\",\n null\n ],\n \"description\": \"Direct application/job posting URL\"\n },\n \"company_url\": {\n \"type\": [\n \"string\",\n null\n ],\n \"description\": \"Company website or careers page\"\n }\n }\n}\n"
},
"typeVersion": 1.2
},
{
"id": "b84ca004-6f3b-4577-8910-61b8584b161d",
"name": "Who is hiring 게시물 검색",
"type": "n8n-nodes-base.httpRequest",
"position": [
200,
-220
],
"parameters": {
"url": "https://uj5wyc0l7x-dsn.algolia.net/1/indexes/Item_dev_sort_date/query",
"method": "POST",
"options": {},
"jsonBody": "{\n \"query\": \"\\\"Ask HN: Who is hiring\\\"\",\n \"analyticsTags\": [\n \"web\"\n ],\n \"page\": 0,\n \"hitsPerPage\": 30,\n \"minWordSizefor1Typo\": 4,\n \"minWordSizefor2Typos\": 8,\n \"advancedSyntax\": true,\n \"ignorePlurals\": false,\n \"clickAnalytics\": true,\n \"minProximity\": 7,\n \"numericFilters\": [],\n \"tagFilters\": [\n [\n \"story\"\n ],\n []\n ],\n \"typoTolerance\": \"min\",\n \"queryType\": \"prefixNone\",\n \"restrictSearchableAttributes\": [\n \"title\",\n \"comment_text\",\n \"url\",\n \"story_text\",\n \"author\"\n ],\n \"getRankingInfo\": true\n}",
"sendBody": true,
"sendQuery": true,
"sendHeaders": true,
"specifyBody": "json",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"queryParameters": {
"parameters": [
{
"name": "x-algolia-agent",
"value": "Algolia for JavaScript (4.13.1); Browser (lite)"
},
{
"name": "x-algolia-application-id",
"value": "UJ5WYC0L7X"
}
]
},
"headerParameters": {
"parameters": [
{
"name": "Accept",
"value": "*/*"
},
{
"name": "Accept-Language",
"value": "en-GB,en-US;q=0.9,en;q=0.8"
},
{
"name": "Connection",
"value": "keep-alive"
},
{
"name": "DNT",
"value": "1"
},
{
"name": "Origin",
"value": "https://hn.algolia.com"
},
{
"name": "Referer",
"value": "https://hn.algolia.com/"
},
{
"name": "Sec-Fetch-Dest",
"value": "empty"
},
{
"name": "Sec-Fetch-Mode",
"value": "cors"
},
{
"name": "Sec-Fetch-Site",
"value": "cross-site"
},
{
"name": "User-Agent",
"value": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36"
},
{
"name": "sec-ch-ua",
"value": "\"Chromium\";v=\"133\", \"Not(A:Brand\";v=\"99\""
},
{
"name": "sec-ch-ua-mobile",
"value": "?0"
},
{
"name": "sec-ch-ua-platform",
"value": "\"macOS\""
}
]
}
},
"credentials": {
"httpHeaderAuth": {
"id": "oVEXp2ZbYCXypMVz",
"name": "Algolia Auth"
}
},
"typeVersion": 4.2
},
{
"id": "205e66f6-cd6b-4cfd-a6ec-2226c35ddaac",
"name": "관련 데이터 가져오기",
"type": "n8n-nodes-base.set",
"position": [
700,
-220
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "73dd2325-faa7-4650-bd78-5fc97cc202de",
"name": "title",
"type": "string",
"value": "={{ $json.title }}"
},
{
"id": "44918eac-4510-440e-9ac0-bf14d2b2f3af",
"name": "createdAt",
"type": "string",
"value": "={{ $json.created_at }}"
},
{
"id": "00eb6f09-2c22-411c-949c-886b2d95b6eb",
"name": "updatedAt",
"type": "string",
"value": "={{ $json.updated_at }}"
},
{
"id": "2b4f9da6-f60e-46e0-ba9d-3242fa955a55",
"name": "storyId",
"type": "string",
"value": "={{ $json.story_id }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "16bc5628-8a29-4eac-8be9-b4e9da802e1e",
"name": "최신 게시물 가져오기",
"type": "n8n-nodes-base.filter",
"position": [
900,
-220
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "d7dd7175-2a50-45aa-bd3e-4c248c9193c4",
"operator": {
"type": "dateTime",
"operation": "after"
},
"leftValue": "={{ $json.createdAt }}",
"rightValue": "={{$now.minus({days: 30})}} "
}
]
}
},
"typeVersion": 2.2
},
{
"id": "92e1ef74-5ae1-4195-840b-115184db464f",
"name": "하위 항목(채용 공고) 분할",
"type": "n8n-nodes-base.splitOut",
"position": [
1460,
-220
],
"parameters": {
"options": {},
"fieldToSplitOut": "kids"
},
"typeVersion": 1
},
{
"id": "d0836aae-b98a-497f-a6f7-0ad563c262a0",
"name": "구조화된 데이터로 변환",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
2600,
-220
],
"parameters": {
"text": "={{ $json.cleaned_text }}",
"messages": {
"messageValues": [
{
"message": "Extract the JSON data"
}
]
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.5
},
{
"id": "fd818a93-627c-435d-91ba-5d759d5a9004",
"name": "스티커 메모2",
"type": "n8n-nodes-base.stickyNote",
"position": [
2600,
-620
],
"parameters": {
"width": 840,
"height": 340,
"content": "## Data Structure\n\nWe use Openai GPT-4o-mini to transform the raw data in a unified data structure. Feel free to change this.\n\n```json\n{\n \"company\": \"Name of the hiring company\",\n \"title\": \"Job title/role being advertised\",\n \"location\": \"Work location including remote/hybrid status\",\n \"type\": \"Employment type (Full-time, Contract, etc)\",\n \"salary\": \"Compensation details if provided\",\n \"description\": \"Main job description text including requirements and team info\",\n \"apply_url\": \"Direct application/job posting URL\",\n \"company_url\": \"Company website or careers page\"\n}\n```"
},
"typeVersion": 1
},
{
"id": "b70c5578-5b81-467a-8ac2-65374e4e52f3",
"name": "텍스트 추출",
"type": "n8n-nodes-base.set",
"position": [
1860,
-220
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "6affa370-56ce-4ad8-8534-8f753fdf07fc",
"name": "text",
"type": "string",
"value": "={{ $json.text }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "acb68d88-9417-42e9-9bcc-7c2fa95c4afd",
"name": "텍스트 정리",
"type": "n8n-nodes-base.code",
"position": [
2060,
-220
],
"parameters": {
"jsCode": "// In a Function node in n8n\nconst inputData = $input.all();\n\nfunction cleanAllPosts(data) {\n return data.map(item => {\n try {\n // Check if item exists and has the expected structure\n if (!item || typeof item !== 'object') {\n return { cleaned_text: '', error: 'Invalid item structure' };\n }\n\n // Get the text, with multiple fallbacks\n let text = '';\n if (typeof item === 'string') {\n text = item;\n } else if (item.json && item.json.text) {\n text = item.json.text;\n } else if (typeof item.json === 'string') {\n text = item.json;\n } else {\n text = JSON.stringify(item);\n }\n\n // Make sure text is a string\n text = String(text);\n \n // Perform the cleaning operations\n try {\n text = text.replace(///g, '/');\n text = text.replace(/'/g, \"'\");\n text = text.replace(/&\\w+;/g, ' ');\n text = text.replace(/<[^>]*>/g, '');\n text = text.replace(/\\|\\s*/g, '| ');\n text = text.replace(/\\s+/g, ' ');\n text = text.replace(/\\s*(https?:\\/\\/[^\\s]+)\\s*/g, '\\n$1\\n');\n text = text.replace(/\\n{3,}/g, '\\n\\n');\n text = text.trim();\n } catch (cleaningError) {\n console.log('Error during text cleaning:', cleaningError);\n // Return original text if cleaning fails\n return { cleaned_text: text, warning: 'Partial cleaning applied' };\n }\n\n return { cleaned_text: text };\n \n } catch (error) {\n console.log('Error processing item:', error);\n return { \n cleaned_text: '', \n error: `Processing error: ${error.message}`,\n original: item\n };\n }\n }).filter(result => result.cleaned_text || result.error); \n}\n\ntry {\n return cleanAllPosts(inputData);\n} catch (error) {\n console.log('Fatal error:', error);\n return [{ \n cleaned_text: '', \n error: `Fatal error: ${error.message}`,\n input: inputData \n }];\n}\n"
},
"typeVersion": 2
},
{
"id": "a0727b55-565d-47c0-9ab5-0f001f4b9941",
"name": "테스트용 제한 (선택 사항)",
"type": "n8n-nodes-base.limit",
"position": [
2280,
-220
],
"parameters": {
"maxItems": 5
},
"typeVersion": 1
},
{
"id": "650baf5e-c2ac-443d-8a2b-6df89717186f",
"name": "스티커 메모3",
"type": "n8n-nodes-base.stickyNote",
"position": [
580,
-620
],
"parameters": {
"width": 540,
"height": 340,
"content": "## Clean the result \n\n```json\n{\n\"title\": \"Ask HN: Who is hiring? (February 2025)\",\n\"createdAt\": \"2025-02-03T16:00:43Z\",\n\"updatedAt\": \"2025-02-17T08:35:44Z\",\n\"storyId\": \"42919502\"\n},\n{\n\"title\": \"Ask HN: Who is hiring? (January 2025)\",\n\"createdAt\": \"2025-01-02T16:00:09Z\",\n\"updatedAt\": \"2025-02-13T00:03:24Z\",\n\"storyId\": \"42575537\"\n},\n```"
},
"typeVersion": 1
},
{
"id": "1ca5c39f-f21d-455a-b63a-702e7e3ba02b",
"name": "결과를 airtable에 저장",
"type": "n8n-nodes-base.airtable",
"position": [
3040,
-220
],
"parameters": {
"base": {
"__rl": true,
"mode": "list",
"value": "appM2JWvA5AstsGdn",
"cachedResultUrl": "https://airtable.com/appM2JWvA5AstsGdn",
"cachedResultName": "HN Who is hiring?"
},
"table": {
"__rl": true,
"mode": "list",
"value": "tblGvcOjqbliwM7AS",
"cachedResultUrl": "https://airtable.com/appM2JWvA5AstsGdn/tblGvcOjqbliwM7AS",
"cachedResultName": "Table 1"
},
"columns": {
"value": {
"type": "={{ $json.output.type }}",
"title": "={{ $json.output.title }}",
"salary": "={{ $json.output.salary }}",
"company": "={{ $json.output.company }}",
"location": "={{ $json.output.location }}",
"apply_url": "={{ $json.output.apply_url }}",
"company_url": "={{ $json.output.company_url }}",
"description": "={{ $json.output.description }}"
},
"schema": [
{
"id": "title",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "title",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "company",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "company",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "location",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "location",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "type",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "type",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "salary",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "salary",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "description",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "description",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "apply_url",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "apply_url",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "company_url",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "company_url",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "posted_date",
"type": "string",
"display": true,
"removed": true,
"readOnly": false,
"required": false,
"displayName": "posted_date",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "create"
},
"credentials": {
"airtableTokenApi": {
"id": "IudXLNj7CDuc5M5a",
"name": "Airtable Personal Access Token account"
}
},
"typeVersion": 2.1
},
{
"id": "d71fa024-86a0-4f74-b033-1f755574080c",
"name": "스티커 메모4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-520,
-300
],
"parameters": {
"width": 380,
"height": 500,
"content": "## Hacker News - Who is Hiring Scrape\n\nIn this template we setup a scraper for the monthly HN Who is Hiring post. This way we can scrape the data and transform it to a common data strcutre.\n\nFirst we use the [Algolia Search](https://hn.algolia.com/) provided by hackernews to drill down the results.\n\nWe can use the official [Hacker News API](https://github.com/HackerNews/API\n) to get the post data and also all the replies!\n\nThis will obviously work for any kind of post on hacker news! Get creative 😃\n\nAll you need is an Openai Account to structure the text data and an Airtable Account (or similar) to write the results to a list.\n\nCopy my base https://airtable.com/appM2JWvA5AstsGdn/shrAuo78cJt5C2laR"
},
"typeVersion": 1
},
{
"id": "7466fb0c-9f0c-4adf-a6de-b2cf09032719",
"name": "HI API: 개별 채용 공고 가져오기",
"type": "n8n-nodes-base.httpRequest",
"position": [
1660,
-220
],
"parameters": {
"url": "=https://hacker-news.firebaseio.com/v0/item/{{ $json.kids }}.json?print=pretty",
"options": {}
},
"typeVersion": 4.2
},
{
"id": "184abccf-5838-49bf-9922-e0300c6b145e",
"name": "HN API: 메인 게시물 가져오기",
"type": "n8n-nodes-base.httpRequest",
"position": [
1260,
-220
],
"parameters": {
"url": "=https://hacker-news.firebaseio.com/v0/item/{{ $json.storyId }}.json?print=pretty",
"options": {}
},
"typeVersion": 4.2
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "387f7084-58fa-4643-9351-73c870d3f028",
"connections": {
"a686852b-ff84-430b-92bb-ce02a6808e19": {
"main": [
[
{
"node": "205e66f6-cd6b-4cfd-a6ec-2226c35ddaac",
"type": "main",
"index": 0
}
]
]
},
"acb68d88-9417-42e9-9bcc-7c2fa95c4afd": {
"main": [
[
{
"node": "a0727b55-565d-47c0-9ab5-0f001f4b9941",
"type": "main",
"index": 0
}
]
]
},
"b70c5578-5b81-467a-8ac2-65374e4e52f3": {
"main": [
[
{
"node": "acb68d88-9417-42e9-9bcc-7c2fa95c4afd",
"type": "main",
"index": 0
}
]
]
},
"16bc5628-8a29-4eac-8be9-b4e9da802e1e": {
"main": [
[
{
"node": "184abccf-5838-49bf-9922-e0300c6b145e",
"type": "main",
"index": 0
}
]
]
},
"205e66f6-cd6b-4cfd-a6ec-2226c35ddaac": {
"main": [
[
{
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}자주 묻는 질문
이 워크플로우를 어떻게 사용하나요?
위의 JSON 구성 코드를 복사하여 n8n 인스턴스에서 새 워크플로우를 생성하고 "JSON에서 가져오기"를 선택한 후, 구성을 붙여넣고 필요에 따라 인증 설정을 수정하세요.
이 워크플로우는 어떤 시나리오에 적합한가요?
고급 - 인사, 인공지능
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Julian Kaiser
@jksrFull Stack Developer turned AI & Automation Engineer, implementing intelligent solutions with custom code, LLMs & n8n. Use my link to book a consultation on automating your business processes.
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