This article provides a complete, practical walkthrough of the Code Ghost Create Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   How to Automate LinkedIn Content Creation from Blog Posts Using n8n and AI
   
   Meta Description:  
   Discover how to automate LinkedIn promotional posts using your blog’s content with n8n, OpenAI, and Ghost CMS. Learn how to streamline your content marketing workflow with this AI-powered automation.
   
   Keywords:  
   n8n workflow, LinkedIn automation, content marketing automation, OpenAI GPT-4, Ghost CMS, LinkedIn post generator, blog automation, AI marketing assistant, Langchain Agent, automated social media, clean HTML content
   
   Third-party APIs & Tools Used:
   
   1. Ghost CMS API (to extract blog content)
   2. OpenAI API via Langchain (GPT-4 model - gpt-4o-mini)
   3. Google Sheets API (to store results)
   
   —
   
   📄 Article:
   
   Automating LinkedIn Content Creation with n8n and AI: Turn Blog Posts Into Engaging Social Media Copy
   
   In the fast-moving world of digital content, staying consistent across platforms like LinkedIn can be a huge challenge for thought leaders and content managers. But what if your blog could write your LinkedIn posts for you? By harnessing the power of n8n’s no-code automation platform, Ghost CMS, and OpenAI's GPT-4o-mini through Langchain, it’s not only possible — it's seamless.
   
   Let's explore how this intelligent n8n workflow turns blog articles into professional LinkedIn updates in just a few steps.
   
   🚀 Overview of the Workflow
   
   This workflow revolves around a simple yet powerful objective: extract recent blog content and transform it into LinkedIn-ready promotional messages. Here’s a high-level summary:
   
   1. Trigger the workflow manually or schedule it.
   2. Fetch recent blog posts from Ghost CMS.
   3. Extract useful content and metadata (title, excerpt, link, HTML).
   4. Clean the HTML to get readable text.
   5. Prompt an AI assistant to generate a polished LinkedIn post.
   6. Save the resulting post and content in Google Sheets.
   
   It’s a highly modular, customizable system — perfect for marketers, solopreneurs, or content creators who want to scale their personal branding efforts.
   
   🧩 Step-by-Step Breakdown
   
   1. Workflow Trigger (Manual Trigger Node)
   The process begins with the “Manual Trigger” node. You can run this on demand to test or schedule it for regular content operations depending on your needs.
   
   2. Fetch Blog Posts from Ghost CMS
   The next node connects with your Ghost account using the Ghost API. It grabs the latest blog posts (by default, 3), extracting data such as:
   - Title
   - Excerpt
   - URL
   - Featured image
   - Full HTML content
   
   The “Set” node then parses and labels this blog data into usable fields.
   
   3. Loop and HTML Cleaning
   The workflow loops over each blog post individually using the “Split In Batches” node.
   
   Since blog content is returned in HTML format, the “Code” node runs a simple JavaScript script to strip away HTML tags and clean the text. This ensures that the AI has clean, reader-friendly data to work with.
   
   4. Generate AI-Powered LinkedIn Posts
   Here’s where the real magic happens.
   
   The AI Agent node (powered by Langchain and OpenAI’s GPT-4o-mini model) is configured to act as a professional content marketing assistant. A thoughtfully crafted system prompt instructs the AI to:
   - Start with a catchy hook
   - Summarize the value of the article
   - Include a strong call-to-action
   - Sign off with the author’s name and contact details
   
   The AI outputs a LinkedIn-ready post that’s both insightful and on-brand.
   
   5. Merge & Save to Google Sheets
   Cleaned content and AI-generated posts are merged using the “Merge” nodes and saved to a chosen Google Sheet via the “Google Sheets” node.
   
   Each row includes the blog’s original content and metadata along with the AI-generated promotional post, ensuring you always have a centralized repository for social content planning or direct uploading.
   
   📎 Optional Tutorial
   The workflow even comes with a supplementary YouTube tutorial linked in the sticky note for reference (https://www.youtube.com/watch?v=Lhi6hV6rWEo), making implementation and customization even easier.
   
   🛠️ Getting Started: What You’ll Need
   
   - A Ghost blog with published posts
   - Access to OpenAI via Langchain (API key required)
   - A Google account with Google Sheets enabled
   - An n8n instance (hosted or cloud)
   
   The setup is beginner-friendly and fully documented within the nodes.
   
   🎯 Final Thoughts
   
   AI is revolutionizing how creators and marketers approach content distribution — this n8n workflow is a perfect example. By automating the extraction and transformation of blog content into LinkedIn-ready posts, you’re not just saving time — you’re freeing up creative energy for strategy and ideation.
   
   Whether you’re building your personal brand or managing content across multiple clients, this dynamic system ensures a consistent, professional presence on LinkedIn — without ever opening a Word doc.
   
   —
   Samir Saci  
   Supply Chain Data Scientist & Founder of LogiGreen  
   📩 Contact me: https://logi-green.com/contactus
   
   —
   
   Ready to set it up? Watch the tutorial here: https://www.youtube.com/watch?v=Lhi6hV6rWEo
   
   Let automation tell your story — smarter, faster, better.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.