This article provides a complete, practical walkthrough of the Manual Stickynote Create Webhook 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:  
   Automating Company Story Generation from LinkedIn Using n8n, Bright Data, and Google Gemini
   
   Meta Description:  
   Discover how to automate the extraction and summarization of LinkedIn company data into compelling narratives using the n8n workflow builder with Bright Data APIs and Google’s Gemini AI models.
   
   Keywords:  
   n8n automation, LinkedIn data extraction, Bright Data API, Google Gemini AI, workflow automation, AI content generation, HR automation, company storytelling, LLM AI, automated summaries
   
   Third-Party APIs Used:
   
   - Bright Data Dataset API (LinkedIn Data Extraction)
   - Google Gemini API (LM Chat and Summarization Model)
   - Webhook.site (For testing and sending final processed data)
   
   Article:
   
   In an age where data drives storytelling and brand engagement, Human Resources teams, PR professionals, and recruiters are constantly seeking effective ways to turn company data into compelling content. Enter n8n, the open-source workflow automation platform. Leveraging third-party APIs like Bright Data and Google Gemini AI, a new automated workflow demonstrates how businesses can extract valuable LinkedIn information and convert it into human-sounding narratives—all with minimal manual effort.
   
   Let’s unpack this powerful automation step-by-step with the “Generate Company Stories from LinkedIn” n8n workflow.
   
   📥 Step 1: Initiating the Workflow  
   The process begins with a simple manual trigger: the user activates the workflow via n8n’s interface. A LinkedIn company URL is set (e.g., https://il.linkedin.com/company/bright-data), specifying the business whose story we want to generate.
   
   🔗 Step 2: Extracting LinkedIn Data via Bright Data API  
   The Bright Data Dataset API is called next to initiate a data extraction request. This is a POST request to the Bright Data scraper endpoint that begins crawling and capturing the relevant LinkedIn company data.
   
   Once initiated, the system waits for the snapshot of that data to be marked as "ready." This is accomplished by periodically checking the snapshot status through Bright Data’s “progress” endpoint, pausing every 30 seconds until the data is fully available.
   
   📦 Step 3: Downloading the Extracted Snapshot  
   Once the snapshot is ready, a GET request using the snapshot ID downloads the extracted LinkedIn data in JSON format. This JSON payload generally includes company size, industry, location, description, employee count, recent updates, and more.
   
   📤 Step 4: Transforming Raw Data into Narrative  
   Now the magic of artificial intelligence begins. The downloaded JSON is passed through a language model (LLM) prompt chain powered by Google Gemini Flash Experimental Models. A system prompt like "Write a complete story of the provided company information..." is fed to the Gemini-based Large Language Model. The AI is instructed to format the LinkedIn response into a well-written story or blog post, integrating all available information.
   
   🧠 Step 5: AI-Powered Summarization  
   Long-form outputs are insightful but aren’t always practical in fast-paced environments. To solve this, a summarization chain—again powered by Google Gemini—condenses the detailed company story into a concise executive summary.
   
   🧬 Step 6: Data Delivery via Webhook  
   Once content (both the long-form story and the summary) is generated, it is sent to designated webhook endpoints. In this workflow, Webhook.site is used as a listener for logging and testing, but this can be replaced with a CRM, content CMS, Slack channel, or even a branded email automation service.
   
   🎯 Why This Workflow Matters  
   This workflow encapsulates the collaborative potential of modern AI and automation tools for HR and marketing teams:
   
   - Recruiters can enrich company profiles quickly for branding.
   - Marketing teams can automate storytelling tasks for social media.
   - HR departments can continuously provide fresh content for careers pages.
   
   Crucially, the solution is fully modular. With n8n’s visual builder, it's easy to customize this workflow to extract competitor analysis, track growth over time, or compare companies across industries.
   
   🔧 Technologies That Power the Workflow
   
   1. Bright Data Dataset API  
      Used for LinkedIn scraping and structured data delivery. It facilitates real-time data access without the need for complex crawlers.
   
   2. Google Gemini API (via LangChain Integration)  
      Provides LLM capabilities including formatting raw input into a narrative and summarizing verbose content into executive briefs.
   
   3. Webhook.site  
      A temporary receipt platform to test where outputs are being submitted. In production, this would be replaced with internal endpoints or Slack bots.
   
   💡 Final Thoughts  
   The “Generate Company Stories from LinkedIn” workflow stands as a showcase of how far automation and AI have come. Tasks that previously required human research, content writing, and editorial oversight can now be triggered, executed, and delivered—all within minutes.
   
   Whether you’re an HR specialist looking to modernize your company’s content pipeline or a developer excited about scaling LLM solutions, this workflow represents a tangible step forward into the AI-powered workplace.
   
   With just three APIs and a clear workflow, the mundane becomes magical.

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.