{"workflow":{"id":11386,"name":"Generate article-based posts for LinkedIn, Twitter (X), and Reddit with OpenAI","views":150,"recentViews":0,"totalViews":150,"createdAt":"2025-12-01T05:50:02.985Z","description":"## Overview\nAI-powered workflow that transforms any article URL into platform-optimized social media posts for LinkedIn, Twitter (X), and Reddit. Uses Mozilla Readability for content extraction, multi-agent AI with RAG from viral LinkedIn post database, and interactive review forms for content refinement before auto-publishing.\n\n**Key Capabilities:**\n- Extracts article content: title, author, text, images, metadata\n- Generates LinkedIn posts using 3-agent system with viral pattern matching\n- Creates Twitter threads under 280 characters with article links\n- Auto-posts to Reddit with AI-selected flairs\n- Interactive review/regeneration workflow with feedback loops\n- Auto-publishes with images or links to all platforms\n\n# How It Works\n\n## Stage 1: Article Content Extraction\n1. **Form Submission**: User enters article URL (with basic auth protection)\n2. **URL Validation**: Checks if valid URL format\n3. **Article Scraping**: HTTP request fetches HTML content\n4. **Readability Parsing**: Mozilla Readability extracts:\n - Clean article text (removes ads, navigation, etc.)\n - Title, author, excerpt\n - Word count, site name\n - Featured image (from og:image, twitter:image, or first img tag)\n5. **Error Handling**: Returns user-friendly error if scraping fails\n\n## Stage 2: LinkedIn Post Generation (3-Agent System)\n\n**Agent 1: LinkedIn Post Strategist**\n- **Input**: Extracted article content (title, text, author, excerpt)\n- **RAG Process**: Queries Supabase vector database for similar viral LinkedIn posts\n- **Analysis**: Identifies patterns, hooks, formatting, engagement triggers\n- **Output**: Strategic insights and viral content patterns\n\n**Agent 2: LinkedIn Post Generator**\n- **Input**: Article content + strategist insights\n- **Process**: Creates post using viral patterns from database\n- **Rule**: Must include article URL in post\n- **Output**: Draft LinkedIn post\n\n**Agent 3: LinkedIn Post Formatter**\n- **Input**: Generated post\n- **Process**: \n - Removes extraneous content\n - Applies Sans Serif Bold Unicode for emphasis (๐ฏ๐ผ๐น๐ฑ ๐๐ฒ๐
๐)\n - Removes markdown/em dashes\n - Ensures clean formatting\n- **Output**: Polished, ready-to-post LinkedIn content\n\n**Review Loop**:\n1. User sees formatted post in web form\n2. Options: \"Regenerate\" or \"Continue\"\n3. If regenerate: Provide feedback โ Agent creates new version\n4. Second review form with same options\n5. After 2 iterations or approval, proceeds to image selection\n\n## Stage 3: Image Handling for LinkedIn\n1. **Image Preview**: Shows extracted article image\n2. **User Choice**:\n - \"Yes\" โ Downloads image, posts with text + image\n - \"Continue without Image\" โ Posts with text + article link preview\n3. **Auto-Publish**: Posts to LinkedIn with selected format\n\n## Stage 4: Twitter (X) Post Generation\n**Parallel process** (runs alongside LinkedIn):\n1. **Twitter Agent**: \n - Creates tweet under 280 characters (including spaces)\n - Must include article URL\n - Uses GPT-4.1 or GPT-5 models\n2. **Tweet Review Form**: User reviews generated tweet\n3. **Regeneration Loop** (if requested):\n - User provides feedback\n - Re-generate Tweet Agent creates new version\n - Second review form\n4. **Auto-Tweet**: Posts with article image attachment\n\n## Stage 5: Reddit Post Automation\n**Parallel process** (runs alongside LinkedIn/Twitter):\n1. **Subreddit Selection**: User picks from dropdown (r/n8n, r/mcp, r/technews)\n2. **Flair Retrieval**: Fetches available flairs for selected subreddit via Reddit API\n3. **AI Flair Selection**: \n - GPT-4o-mini analyzes article title + available flairs\n - Selects most appropriate flair\n4. **Auto-Post**: Submits link post to Reddit with title and selected flair\n\n# How To Use\n\n## Prerequisites\n\n### API Credentials Required\n1. **OpenAI API**: GPT-4.1, GPT-5, GPT-5-mini, GPT-4o-mini access\n2. **Supabase**: Vector database with `linkedin_post` table (from previous workflow)\n3. **LinkedIn OAuth2**: Developer app with posting permissions\n4. **Twitter OAuth2**: Developer account with tweet permissions\n5. **Reddit OAuth2**: App credentials with submit permissions\n6. **Basic Auth**: For form password protection\n\n## Setup Steps\n\n### 1. Configure Form Access\n- Open **\"On Article Submission\"** node\n- Set up basic auth credentials for form protection\n- Get form URL from webhook settings\n\n### 2. Link Vector Database\n- Ensure Supabase vector store has viral LinkedIn posts (use previous workflow to populate)\n- Verify **\"LinkedIn Post Vector Store\"** credentials\n- Check **\"Embedding\"** node has OpenAI API key\n\n### 3. Set Up Social Media APIs\n\n**LinkedIn**:\n- Configure **\"Text + Image\"** and **\"Text + Link\"** nodes\n- Update `person` parameter with your LinkedIn profile ID\n- Add OAuth2 credentials\n\n**Twitter**:\n- Configure **\"Tweet\"** and **\"Re-generated Tweet\"** nodes\n- Add Twitter OAuth2 credentials\n\n**Reddit**:\n- Update subreddit list in **\"Reddit Form\"** dropdown (customize to your subreddits)\n- Configure **\"Get Flair\"**, **\"Reddit Post\"** nodes with OAuth2\n- Update subreddit name in **\"Reddit Post\"** query parameters\n\n### 4. Configure AI Models\n- Verify all OpenAI credentials in language model nodes\n- Models used: GPT-4.1, GPT-5, GPT-5-mini (adjust based on your access)\n\n","workflow":{"meta":{"instanceId":"6c118a8acfbd6c5a7401dc5bfa65b57bbe715feb57d00cd265fb540cc778096d","templateCredsSetupCompleted":true},"nodes":[{"id":"ded81242-91f9-48b2-92e7-93b5c427c22c","name":"5","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[1184,3104],"parameters":{"model":{"__rl":true,"mode":"list","value":"gpt-5","cachedResultName":"gpt-5"},"options":{}},"credentials":{"openAiApi":{"id":"nFuggaqEYFlgAYJm","name":"n8n - Vekrro"}},"typeVersion":1.2},{"id":"db3f31ca-0104-4484-a5a3-718a05e6db9a","name":"If","type":"n8n-nodes-base.if","position":[-128,3728],"parameters":{"options":{},"conditions":{"options":{"version":2,"leftValue":"","caseSensitive":true,"typeValidation":"loose"},"combinator":"and","conditions":[{"id":"c2a21f68-1dd7-4614-be11-45c3f2129a63","operator":{"type":"boolean","operation":"equals"},"leftValue":"={{ $json['Article Link'].isUrl() }}","rightValue":true}]},"looseTypeValidation":true},"typeVersion":2.2},{"id":"b79d6081-d689-4a93-b927-64060828aac0","name":"Extract Details","type":"n8n-nodes-base.code","position":[336,3696],"parameters":{"jsCode":"const { Readability } = require(\"@mozilla/readability\");\nconst { JSDOM } = require(\"jsdom\");\n\ntry {\n const inputData = items[0].json;\n const html = inputData.data;\n const url = inputData.url\n\n if (!html || html.trim().length === 0) {\n throw new Error(\"No HTML content found\");\n }\n\n const dom = new JSDOM(html, { \n url,\n contentType: \"text/html\"\n });\n\n const document = dom.window.document;\n const reader = new Readability(dom.window.document);\n const article = reader.parse();\n\n if (!article) {\n return [{\n json: {\n error: \"Readability could not extract article content\",\n reason: \"The page may not contain article-like content or structure\",\n url,\n htmlLength: html.length\n }\n }];\n }\n\n let imageUrl = null;\n \n const ogImage = document.querySelector('meta[property=\"og:image\"]');\n if (ogImage) imageUrl = ogImage.getAttribute('content');\n \n if (!imageUrl) {\n const twitterImage = document.querySelector('meta[name=\"twitter:image\"]');\n if (twitterImage) imageUrl = twitterImage.getAttribute('content');\n }\n \n if (!imageUrl && article.content) {\n const contentDom = new JSDOM(article.content);\n const firstImg = contentDom.window.document.querySelector('img');\n if (firstImg) imageUrl = firstImg.getAttribute('src');\n }\n \n if (imageUrl && !imageUrl.startsWith('http')) {\n try {\n imageUrl = new URL(imageUrl, url).href;\n } catch (e) {\n // Invalid URL, keep as is\n }\n }\n \n return [{\n json: {\n title: article.title || \"No title found\",\n author: article.byline || \"Unknown\",\n contentText: article.textContent || \"\",\n wordCount: article.length || 0,\n siteName: article.siteName || \"Unknown site\",\n excerpt: article.excerpt || \"\",\n imageUrl: imageUrl || null,\n }\n }];\n\n} catch (error) {\n return [{\n json: {\n error: error.message,\n stack: error.stack,\n inputKeys: Object.keys(items[0].json)\n }\n }];\n}"},"typeVersion":2},{"id":"80bb41d8-03b8-46d5-a113-bccd7bcb4953","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[80,3488],"parameters":{"color":4,"width":464,"height":576,"content":"## ๐ Extract Article Content\n- Title\n- Article Content\n- Author\n- Excerpt\n- Website Name\n- Article Image"},"typeVersion":1},{"id":"d84233ad-ebd5-4335-9569-53047fcfd74e","name":"LinkedIn Post Vector Store","type":"@n8n/n8n-nodes-langchain.vectorStoreSupabase","position":[752,3008],"parameters":{"mode":"retrieve-as-tool","options":{},"tableName":{"__rl":true,"mode":"list","value":"linkedin_post","cachedResultName":"linkedin_post"},"toolDescription":"Supabase"},"credentials":{"supabaseApi":{"id":"IrLqyk8yYvZkIUwg","name":"n8n"}},"typeVersion":1.3},{"id":"d853347a-78d9-4b55-8ee3-a3c415921805","name":"Embedding","type":"@n8n/n8n-nodes-langchain.embeddingsOpenAi","position":[752,3152],"parameters":{"options":{}},"credentials":{"openAiApi":{"id":"nFuggaqEYFlgAYJm","name":"n8n - Vekrro"}},"typeVersion":1.2},{"id":"970b2a6e-d9d1-4be3-846f-879511ecadeb","name":"5 mini","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[832,3424],"parameters":{"model":{"__rl":true,"mode":"list","value":"gpt-5-mini","cachedResultName":"gpt-5-mini"},"options":{}},"credentials":{"openAiApi":{"id":"nFuggaqEYFlgAYJm","name":"n8n - Vekrro"}},"typeVersion":1.2},{"id":"3ef397ee-868a-414d-ac0c-6785644b86f0","name":"Structured Output Parser","type":"@n8n/n8n-nodes-langchain.outputParserStructured","position":[832,3280],"parameters":{"autoFix":true,"jsonSchemaExample":"{\n\t\"Post Content\": \"Post Content\"\n}"},"typeVersion":1.3},{"id":"754be77e-3289-469e-b1d0-2931ac62a980","name":"4.1","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[1088,3104],"parameters":{"model":{"__rl":true,"mode":"list","value":"gpt-4.1","cachedResultName":"gpt-4.1"},"options":{}},"credentials":{"openAiApi":{"id":"nFuggaqEYFlgAYJm","name":"n8n - Vekrro"}},"typeVersion":1.2},{"id":"e0551a27-fe1c-4e5e-a12d-ab92a91d67c4","name":"On Article Submission","type":"n8n-nodes-base.formTrigger","position":[-304,3728],"webhookId":"89a83694-7c0e-4129-b902-be8d90352d2d","parameters":{"options":{"path":"article-post","ignoreBots":true,"buttonLabel":"Submit","appendAttribution":false},"formTitle":"Article โพ๏ธ Post","formFields":{"values":[{"fieldType":"textarea","fieldLabel":"Article Link","requiredField":true}]},"responseMode":"lastNode","authentication":"basicAuth"},"credentials":{"httpBasicAuth":{"id":"Lxv3eci1rg0UvcH3","name":"Common Basic Auth"}},"typeVersion":2.3},{"id":"5151194a-dc73-4de1-98f7-f4b5d668582a","name":"Scrape Article","type":"n8n-nodes-base.httpRequest","onError":"continueErrorOutput","position":[128,3712],"parameters":{"url":"={{ $json['Article Link'] }}","options":{}},"retryOnFail":true,"typeVersion":4.3},{"id":"11299a31-40c0-48ab-9ebd-d272475439f0","name":"โ ๏ธUnable to Process Article","type":"n8n-nodes-base.form","position":[336,3872],"webhookId":"f4ec2203-9121-47cc-a355-97e9e2d16c32","parameters":{"options":{},"operation":"completion","completionTitle":"โ ๏ธUnable to Process Article","completionMessage":"Please enter different Article"},"typeVersion":2.3},{"id":"e734aa10-21f6-4359-8649-08698b70b1ac","name":"Sticky Note10","type":"n8n-nodes-base.stickyNote","position":[656,2656],"parameters":{"color":5,"width":2800,"height":912,"content":"## ๐ LinkedIn\n"},"typeVersion":1},{"id":"b600ba23-5504-4f36-952d-71b88a026613","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[656,3600],"parameters":{"color":7,"width":1472,"height":640,"content":"## ๐ X (Twitter)\n"},"typeVersion":1},{"id":"e4b3e783-38c8-47b5-b684-b72d2f427935","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[656,4272],"parameters":{"color":2,"width":1120,"height":304,"content":"## ๐ Reddit"},"typeVersion":1},{"id":"dfb9de11-ecc1-4a1c-bf30-81b21a896d37","name":"Get Flair","type":"n8n-nodes-base.httpRequest","position":[912,4368],"parameters":{"url":"=https://oauth.reddit.com/r/{{ $json.Subreddit.split(\"/\")[1] }}/api/link_flair_v2","options":{},"authentication":"genericCredentialType","genericAuthType":"oAuth2Api"},"credentials":{"oAuth2Api":{"id":"ehkveBOmu6N95jWb","name":"Custom Reddit - Bhavy Shekhaliya"},"redditOAuth2Api":{"id":"aCvtLxRhnnUtSHp4","name":"Bhavy Shekhaliya"}},"typeVersion":4.3},{"id":"d6d88d8d-3693-45ff-94f2-797744fc45dc","name":"Reddit Post","type":"n8n-nodes-base.httpRequest","position":[1520,4368],"parameters":{"url":"https://oauth.reddit.com/r/n8n/api/submit","method":"POST","options":{},"sendQuery":true,"authentication":"genericCredentialType","genericAuthType":"oAuth2Api","queryParameters":{"parameters":[{"name":"sr"},{"name":"title","value":"={{ $('Extract Details').item.json.title }}"},{"name":"kind","value":"link"},{"name":"flair_id","value":"c167b438-1bc8-11f0-8814-1e37289161d8"},{"name":"url","value":"={{ $('On Article Submission').item.json['Article Link'] }}"}]}},"credentials":{"oAuth2Api":{"id":"ehkveBOmu6N95jWb","name":"Custom Reddit - Bhavy Shekhaliya"},"redditOAuth2Api":{"id":"aCvtLxRhnnUtSHp4","name":"Bhavy Shekhaliya"}},"typeVersion":4.3},{"id":"de5d4e02-e6c5-4009-a9de-c3423e2a9b6a","name":"LinkedIn Post Strategist","type":"@n8n/n8n-nodes-langchain.agent","position":[720,2784],"parameters":{"text":"=# Article Content\n\n- Title: {{ $json.title }}\n- Author: {{ $json.author }}\n- Content Text: {{ $json.contentText }}\n- Site Name: {{ $json.siteName }}\n- Excerpt: {{ $json.excerpt }}","options":{"systemMessage":"=# LinkedIn Viral Content Analyzer - System Prompt\n\nYou are an expert LinkedIn content strategist and analyst with access to a Supabase vector database containing thousands of viral LinkedIn posts. Your primary role is to help users create engaging, high-performing LinkedIn content by analyzing patterns, formats, and hooks from proven viral posts.\n\n## Your Capabilities\n\n1. **Pattern Analysis**: Search and analyze viral posts to identify successful patterns in:\n - Content structure and flow\n - Storytelling techniques\n - Engagement triggers\n - Tone and voice variations\n - Post length and formatting\n\n2. **Hook Identification**: Extract and categorize powerful opening lines that:\n - Grab attention in the first 1-2 lines\n - Create curiosity or emotional resonance\n - Compel readers to click \"see more\"\n - Match specific industries or audiences\n\n3. **Format Recognition**: Identify effective formatting styles including:\n - Line breaks and spacing patterns\n - Emoji usage and placement\n - Bullet points and lists\n - Call-to-action placement\n - Hashtag strategies\n\n4. **Content Strategy**: Provide actionable recommendations based on:\n - Similar successful posts in the user's niche\n - Trending topics and formats\n - Audience engagement patterns\n - Optimal posting frameworks\n\n## How to Use the Vector Database\n\nWhen a user asks for help, follow this process:\n\n1. **Query the Database**: Use semantic search to find relevant viral posts based on:\n - Topic/theme keywords\n - Industry or niche\n - Content type (story, tip, announcement, etc.)\n - Engagement metrics (minimum likes/comments)\n\n2. **Analyze Results**: Examine the retrieved posts for:\n - Common structural elements\n - Recurring hook patterns\n - Formatting similarities\n - Engagement drivers\n\n3. **Synthesize Insights**: Create clear, actionable recommendations:\n - Show 2-3 specific examples from the database\n - Explain WHY these posts performed well\n - Extract reusable templates and formulas\n - Adapt patterns to the user's specific context\n\n## Response Framework\n\nFor each user request:\n\n### 1. Understand the Context\n- What type of content are they creating?\n- Who is their target audience?\n- What's their goal (engagement, lead gen, authority building)?\n\n### 2. Search the Database\n- Query with relevant semantic search terms\n- Retrieve 5-10 most relevant viral posts\n\n### 3. Present Analysis\n```\n**Pattern Found**: [Name the pattern]\n**Why It Works**: [Psychology/mechanism]\n**Example from Database**: [Show snippet]\n**How to Apply**: [Specific steps]\n```\n\n## Writing Style Guidelines\n\nYour responses should sound like they're coming from a thoughtful, articulate human, not overly robotic, overly formal, or exaggeratedly casual. Use short, clear sentences that flow smoothly. Keep language grounded and confident.\n\n**Style Rules:**\n- Avoid filler phrases like \"in today's world\" or \"needless to say\"\n- Never use em dashes (โ). Use commas, semicolons, or break sentences when needed\n- Maintain a balance between professionalism and relatability, like how a smart professional would write a LinkedIn post, personal article, or thoughtful email\n- Focus on insight and clarity, not just word count\n- Write with confidence but remain approachable\n- **For bold sections, use Sans Serif Bold Unicode characters** (๐ฒ๐
๐ฎ๐บ๐ฝ๐น๐ฒ: ๐ง๐ต๐ถ๐ ๐ถ๐ ๐ฏ๐ผ๐น๐ฑ ๐๐ฒ๐
๐)\n\n## Tone and Style\n\n- Focus on empowering the user's unique voice"},"promptType":"define","needsFallback":true,"hasOutputParser":true},"typeVersion":3},{"id":"34c182d7-a2c9-4427-8903-4b6f5055deda","name":"LinkedIn Post Generator","type":"@n8n/n8n-nodes-langchain.agent","position":[1056,2784],"parameters":{"text":"=Content Text:\n\n{{ $('Extract Details').item.json.contentText }}\n\nArticle URL: {{ $('On Article Submission').item.json['Article Link'] }}\n","options":{"systemMessage":"=# Goal\nGenerate likedin post using given data\n\n# Rule\n- Must add Article URL in Linkedin Post\n\n{{ $json.output['Post Content'] }}"},"promptType":"define","needsFallback":true},"typeVersion":3},{"id":"f56e8776-0adb-4a9b-b3d2-1deab40ba39a","name":"LinkedIn Post Formatter","type":"@n8n/n8n-nodes-langchain.agent","position":[1344,2784],"parameters":{"text":"={{ $json.output }}","options":{"systemMessage":"=You are a helpful LinkedIn Post Formatter.\n\n## Rule\n- Delete content other than actual post (Don't remove Article URL)\n- For important words, use Sans Serif Bold Unicode characters** (๐ฒ๐
๐ฎ๐บ๐ฝ๐น๐ฒ: ๐ง๐ต๐ถ๐ ๐ถ๐ ๐ฏ๐ผ๐น๐ฑ ๐๐ฒ๐
๐) \n- Never use em dashes (โ). Use commas, or break sentences when needed\n- Never include markdown"},"promptType":"define","needsFallback":true},"typeVersion":3},{"id":"b46ae708-d724-4f6c-9119-cfdd95396173","name":"Post Review Form","type":"n8n-nodes-base.form","position":[1696,2784],"webhookId":"e5a6a76b-91c5-4fc6-a2ab-2f168824ae2b","parameters":{"options":{"formTitle":"Review LinkedIn Post"},"formFields":{"values":[{"html":"\n
\n {{ $json.output.replaceAll(\"\\n\",\"
\") }}
\n
\n
\n
\n {{ $json.output.replaceAll(\"\\n\",\"
\") }}
\n
\n
\n
\n {{ $json.output['Post Content'].replaceAll(\"\\n\",\"
\") }}
\n
\n
\n
\n {{ $json.output.replaceAll(\"\\n\",\"
\") }}
\n
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