Hacker News タイムマシン - 今日から一年前の人気コンテンツを確認
中級
これはOther, AI分野の自動化ワークフローで、13個のノードを含みます。主にSet, Code, Html, Merge, SplitOutなどのノードを使用、AI技術を活用したスマート自動化を実現。 Hacker Newsタイムマシン - 今日の毎年人気コンテンツを閲覧
前提条件
- •Telegram Bot Token
- •ターゲットAPIの認証情報が必要な場合あり
- •Google Gemini API Key
使用ノード (13)
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
{
"nodes": [
{
"id": "6ea4e702-1af8-407b-b653-964a519db1c2",
"name": "Basic LLM チェーン",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
1560,
-360
],
"parameters": {
"text": "=You are a highly skilled news categorizer, specializing in indentifying interesting stuff from Hacker News front-page headlines.\n\nYou are provided with JSON data containing a list of dates and their corresponding top headlines from the Hacker News front page. Each headline will also include a URL linking to the original article or discussion. Importantly, the dates provided will be the SAME DAY across MULTIPLE YEARS (e.g., January 1st, 2023, January 1st, 2022, January 1st, 2021, etc.). You need to indentify key headlines and also analyze how the tech landscape has evolved over the years, as reflected in the headlines for this specific day.\n\nYour task is to indentify top 10-15 headlines from across the years from the given json data and return in Markdown formatted bullet points categorizing into themes and adding markdown hyperlinks to the source URL with Prefixing Year before the headline. Follow the Output Foramt Mentioned.\n\n**Input Format:**\n\n```json\n[\n {\n \"headlines\": [\n \"Headline 1 Title [URL1]\",\n \"Headline 2 Title [URL2]\",\n \"Headline 3 Title [URL3]\",\n ...\n ]\n \"date\": \"YYYY-MM-DD\",\n },\n {\n \"headlines\": [\n \"Headline 1 Title [URL1]\",\n \"Headline 2 Title [URL2]\",\n ...\n ]\n \"date\": \"YYYY-MM-DD\",\n },\n ...\n]\n```\n\n**Output Format In Markdown**\n\n```\n# HN Lookback <FullMonthName-DD> | <start YYYY> to <end YYYY> \n\n## [Theme 1]\n- YYYY [Headline 1](URL1)\n- YYYY [Headline 2](URL2)\n...\n\n## [Theme 2]\n- YYYY [Headline 1](URL1)\n- YYYY [Headline 2](URL2)\n...\n\n... \n\n## <this is optional>\n<if any interesing ternds emerge mention them in oneline>\n```\n\n**Here is the Json data for Hackernews Headlines across the years**\n\n```\n{{ JSON.stringify($json.data) }}\n```",
"promptType": "define"
},
"typeVersion": 1.5
},
{
"id": "b5a97c2a-0c3b-4ebe-aec5-7bca6b55ad4c",
"name": "Google Gemini チャットモデル",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
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"parameters": {
"options": {},
"modelName": "models/gemini-1.5-pro"
},
"credentials": {
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"id": "Hx1fn2jrUvojSKye",
"name": "Google Gemini(PaLM) Api account"
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},
"typeVersion": 1
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{
"id": "18cba750-aef5-451d-880f-2c12d8540d78",
"name": "スケジュールトリガー",
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"position": [
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"parameters": {
"rule": {
"interval": [
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}
]
}
},
"typeVersion": 1.2
},
{
"id": "341da616-8670-4cd9-b47a-ee25e2ae9862",
"name": "CreateYearsList",
"type": "n8n-nodes-base.code",
"position": [
-200,
-360
],
"parameters": {
"jsCode": "for (const item of $input.all()) {\n const currentDateStr = item.json.timestamp.split('T')[0];\n const currentDate = new Date(currentDateStr);\n const currentYear = currentDate.getFullYear();\n const currentMonth = currentDate.getMonth(); // 0 for January, 1 for February, etc.\n const currentDay = currentDate.getDate();\n\n const datesToFetch = [];\n for (let year = currentYear; year >= 2007; year--) {\n let targetDate;\n if (year === 2007) {\n // Special handling for 2007 to start from Feb 19\n if (currentMonth > 1 || (currentMonth === 1 && currentDay >= 19))\n {\n targetDate = new Date(2007, 1, 19); // Feb 19, 2007\n } else {\n continue; // Skip 2007 if currentDate is before Feb 19\n }\n } else {\n targetDate = new Date(year, currentMonth, currentDay);\n }\n \n // Format the date as YYYY-MM-DD\n const formattedDate = targetDate.toISOString().split('T')[0];\n datesToFetch.push(formattedDate);\n }\n item.json.datesToFetch = datesToFetch;\n}\n\nreturn $input.all();"
},
"typeVersion": 2
},
{
"id": "42e24547-be24-4f29-8ce8-c0df7d47a6ff",
"name": "CleanUpYearList",
"type": "n8n-nodes-base.set",
"position": [
0,
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],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "b269dc0d-21e1-4124-8f3a-2c7bfa4add5c",
"name": "datesToFetch",
"type": "array",
"value": "={{ $json.datesToFetch }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "6e51ad05-0f3d-4bfb-8c8d-5b71e7355344",
"name": "SplitOutYearList",
"type": "n8n-nodes-base.splitOut",
"position": [
200,
-360
],
"parameters": {
"options": {},
"fieldToSplitOut": "datesToFetch"
},
"typeVersion": 1
},
{
"id": "6f827071-718f-4e27-9f7a-cc50296f7bc4",
"name": "GetFrontPage",
"type": "n8n-nodes-base.httpRequest",
"position": [
420,
-360
],
"parameters": {
"url": "=https://news.ycombinator.com/front",
"options": {
"batching": {
"batch": {
"batchSize": 1,
"batchInterval": 3000
}
}
},
"sendQuery": true,
"queryParameters": {
"parameters": [
{
"name": "day",
"value": "={{ $json.datesToFetch }}"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "7287e6b1-337f-4634-ac23-5ceaa87b0db3",
"name": "ExtractDetails",
"type": "n8n-nodes-base.html",
"position": [
640,
-360
],
"parameters": {
"options": {},
"operation": "extractHtmlContent",
"extractionValues": {
"values": [
{
"key": "=headlines",
"cssSelector": ".titleline",
"returnArray": true,
"skipSelectors": "span"
},
{
"key": "date",
"cssSelector": ".pagetop > font"
}
]
}
},
"typeVersion": 1.2
},
{
"id": "fceff31e-4dcd-4199-89c5-8eb75cd479bf",
"name": "GetHeadlines",
"type": "n8n-nodes-base.set",
"position": [
920,
-460
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "e1ce33e9-e4f8-4215-bbdb-156a955a0a97",
"name": "headlines",
"type": "array",
"value": "={{ $json.headlines }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "f7683614-7225-4f05-ba12-86b326fdb4a1",
"name": "GetDate",
"type": "n8n-nodes-base.set",
"position": [
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-280
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "fc1d15f6-a999-4d6b-a7bc-3ffa9427679e",
"name": "date",
"type": "string",
"value": "={{ $json.date }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "7e09ce85-ece1-46a0-aa59-8e3da66413b2",
"name": "マージHeadlinesDate",
"type": "n8n-nodes-base.merge",
"position": [
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],
"parameters": {
"mode": "combine",
"options": {},
"combineBy": "combineByPosition"
},
"typeVersion": 3
},
{
"id": "db3bf408-8179-4ca4-a5b4-8a390b68f994",
"name": "SingleJson",
"type": "n8n-nodes-base.aggregate",
"position": [
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],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData"
},
"typeVersion": 1
},
{
"id": "2abbc0e9-ed1e-4ba0-9d2f-7c3cd314a0fe",
"name": "Telegram",
"type": "n8n-nodes-base.telegram",
"position": [
2020,
-360
],
"parameters": {
"text": "={{ $json.text }}",
"chatId": "@OnThisDayHN",
"additionalFields": {
"parse_mode": "Markdown",
"appendAttribution": false
}
},
"credentials": {
"telegramApi": {
"id": "6nIwfhIWcwJFTPTg",
"name": "OnThisDayHNBot"
}
},
"typeVersion": 1.2
}
],
"pinData": {},
"connections": {
"f7683614-7225-4f05-ba12-86b326fdb4a1": {
"main": [
[
{
"node": "MergeHeadlinesDate",
"type": "main",
"index": 1
}
]
]
},
"db3bf408-8179-4ca4-a5b4-8a390b68f994": {
"main": [
[
{
"node": "Basic LLM Chain",
"type": "main",
"index": 0
}
]
]
},
"6f827071-718f-4e27-9f7a-cc50296f7bc4": {
"main": [
[
{
"node": "7287e6b1-337f-4634-ac23-5ceaa87b0db3",
"type": "main",
"index": 0
}
]
]
},
"fceff31e-4dcd-4199-89c5-8eb75cd479bf": {
"main": [
[
{
"node": "MergeHeadlinesDate",
"type": "main",
"index": 0
}
]
]
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"7287e6b1-337f-4634-ac23-5ceaa87b0db3": {
"main": [
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{
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{
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"type": "main",
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},
"Basic LLM Chain": {
"main": [
[
{
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"type": "main",
"index": 0
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"42e24547-be24-4f29-8ce8-c0df7d47a6ff": {
"main": [
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"type": "main",
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}
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"341da616-8670-4cd9-b47a-ee25e2ae9862": {
"main": [
[
{
"node": "42e24547-be24-4f29-8ce8-c0df7d47a6ff",
"type": "main",
"index": 0
}
]
]
},
"Schedule Trigger": {
"main": [
[
{
"node": "341da616-8670-4cd9-b47a-ee25e2ae9862",
"type": "main",
"index": 0
}
]
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},
"6e51ad05-0f3d-4bfb-8c8d-5b71e7355344": {
"main": [
[
{
"node": "6f827071-718f-4e27-9f7a-cc50296f7bc4",
"type": "main",
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},
"MergeHeadlinesDate": {
"main": [
[
{
"node": "db3bf408-8179-4ca4-a5b4-8a390b68f994",
"type": "main",
"index": 0
}
]
]
},
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "Basic LLM Chain",
"type": "ai_languageModel",
"index": 0
}
]
]
}
}
}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？
中級 - その他, 人工知能
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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