This article provides a complete, practical walkthrough of the Code Schedule Automate Scheduled 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 Editorial Keyword Discovery from Google Trends with n8n and AI
   
   Meta Description:  
   Leverage the power of Google Trends and AI-driven content scraping with this automated n8n workflow that identifies high-traffic trending keywords, extracts article summaries, and saves structured data to Google Sheets for editorial planning.
   
   Keywords:
   - Google Trends automation
   - n8n workflow
   - content automation
   - editorial planning
   - AI content scraping
   - Jina.ai API
   - Google Sheets automation
   - RSS feed parsing
   - marketing automation
   - keyword scraping workflow
   
   Third-Party APIs Used:
   1. Google Trends RSS (publicly available endpoint)
   2. Google Sheets API (via OAuth2)
   3. Jina.ai API (for AI-powered text extraction and summarization from URLs)
   
   —
   
   Article:
   
   Harnessing Google Trends & AI for Automated Editorial Keyword Research Using n8n
   
   In a world where content is king, staying on top of trending topics is crucial for marketers, content creators, and SEO professionals. But manually monitoring trends, researching supporting articles, and organizing the findings can be time-intensive and prone to oversight. What if there were a smarter, fully-automated approach?
   
   Thanks to workflow automation platform n8n and some thoughtfully integrated third-party services, it’s entirely possible to automate this process end-to-end. In this article, we'll explore how an n8n workflow can be used to automatically identify high-traffic trending keywords from Google Trends, extract summary content from related articles using AI, and populate a structured editorial calendar in Google Sheets — all without human intervention.
   
   Let’s break it down.
   
   ⏰ Scheduled Triggers for Timely Monitoring
   
   At the core of this n8n workflow is a cron-based trigger node ("Start every hour past 11 minutes"), which runs the automation once every hour. This timing is intentional — Google Trends RSS feeds update roughly every 10 minutes, and starting the flow 1 minute after ensures the most updated data is compiled in near real-time.
   
   🛠 Configurable Parameters
   
   A "CONFIG" set node serves as the workflow’s control center, offering handy variables:
   - min_traffic: Only keywords surpassing a set traffic threshold (default: 500) are considered.
   - max_results: Limits the number of resulting keywords handled per run (default: 3).
   - jina_key: API key for Jina.ai, used for web content extraction.
   
   📈 Fetching & Processing Google Trends RSS
   
   The workflow pulls trending topics for Italy via Google Trends RSS (https://trends.google.it/trending/rss?geo=IT), parses the returned XML into JSON, and then feeds this structured data into a custom logic block called "New keywords."
   
   Here, the code filters out previously saved keywords (avoiding duplication), applies the min_traffic threshold, flattens nested RSS metadata for easier handling, and selects the top-ranking items.
   
   🧠 AI-Powered Content Scraping
   
   For each trending item, the workflow pulls up to 3 related news article URLs listed in the RSS feed. These links are passed to Jina.ai's scraping API to retrieve clean, structured summaries of their content by removing unnecessary HTML elements like scripts, footers, and images.
   
   Each response is stored in a designated variable (content1, content2, content3), then stitched together into a combined "summary" field. This gives a high-level overview of what each trend is about from multiple perspectives — a fantastic resource for journalists and marketers alike.
   
   📋 Data Mapping and Validation
   
   Before persisting the data, the workflow maps all relevant fields (keyword, traffic, article links, titles, images, sources, etc.) into a shape suitable for Google Sheets.
   
   A filter node then checks whether at least one article was successfully scraped and whether the generated summary has meaningful length (>100 characters) to avoid storing empty or broken data.
   
   📊 Saving to Google Sheets
   
   If validation passes, the cleaned and structured dataset is appended to a Google Sheet that acts as the central editorial database. The sheet includes an editable status column (set to "idea" by default), which can trigger further workflows like article creation, publishing, or social sharing.
   
   This setup not only organizes trending keywords based on interest and coverage, but also tracks the top news and narratives around them with ready-to-use summaries.
   
   🧠 Smarter Work, Not Harder
   
   This n8n automation is a powerful example of how modern workflows can combine free data sources, AI-based utilities, and cloud services like Google Sheets to create a dynamic content intelligence system. By automating the early stages of trend scouting and keyword research, editorial teams can focus more energy on creativity and content strategy.
   
   In summary, this n8n workflow acts as your tireless content scout: scanning Google Trends hourly, vetting data, gathering supporting articles, synthesizing summaries, and preparing a fully visualized plan — your editorial assistant, available 24/7.
   
   Whether used as-is or customized further, this setup opens the door to a smarter, scalable approach to content marketing in the AI age.
   
   —
   
   Want to future-proof your editorial strategy? This workflow proves that with the right tools and integrations, you can be both timely and efficient — no manual effort needed.

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.