This article provides a complete, practical walkthrough of the Code Schedule Create 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 News Summarization with n8n: A No-Code Workflow for Colt Telecom's Website
   
   **Meta Description:**
   Learn how to automate news extraction, summarization, and keyword tagging using n8n, OpenAI, and NocoDB. This no-code workflow targets Colt’s press page to deliver structured data for further use.
   
   **Keywords:**
   n8n workflow, web scraping, Colt Telecom news, OpenAI GPT-4, automated summarization, NocoDB integration, news automation, press release scraper, CSS selectors, OpenAI API
   
   ---
   
   **Automating Press Release Extraction with n8n: A Case Study in No-Code Workflows**
   
   Keeping track of the latest press releases is an essential task for professionals in tech, PR, and journalism. Unfortunately, not all news sources offer an RSS feed or API access—especially corporate pages like Colt Telecom’s [news site](https://www.colt.net/resources/type/news/). To solve this, we’ve built an automated workflow using the open-source automation tool n8n.
   
   This workflow scrapes the latest news links from Colt’s press release page, extracts the article content, summarizes the information using OpenAI’s GPT-4 API, identifies the top three technical keywords, and finally stores everything in a NocoDB database for easy access and further use.
   
   Let’s break down each component of this powerful end-to-end automation pipeline.
   
   ---
   
   ### Step 1: Scheduled Trigger
   
   The process begins with a Schedule Trigger node in n8n. This is configured to run weekly on Wednesdays at 4:32 AM. It ensures consistent updates without manual checks, making it ideal for ongoing content monitoring.
   
   ---
   
   ### Step 2: Scrape the Press Page for Links and Dates
   
   The page at https://www.colt.net/resources/type/news/ lacks traditional syndication methods like RSS. The workflow uses n8n's HTTP Request and HTML Extractor nodes to:
   
   - Pull the HTML content of the page
   - Extract links to individual news posts using CSS selectors
   - Extract corresponding post dates
   
   Because these values are returned as arrays, they are split into individual JSON items for looping and merging later in the process.
   
   ---
   
   ### Step 3: Filter for Recent Posts
   
   With both dates and links in hand, a Code node filters out posts older than 7 days. This ensures that only the most recent news gets processed and stored—perfect for reducing clutter and focusing on what's new.
   
   > Note: Despite the node’s name implying a 7-day range, the code comment suggests it goes back 70 days. Make sure this aligns with your desired frequency.
   
   ---
   
   ### Step 4: Extract Full Content and Metadata
   
   For each of the filtered links, another series of HTTP requests retrieves the full text of the press release along with its title. Using precise CSS selectors, the ‘Extract Individual Posts’ node pulls out the article body and headings.
   
   ---
   
   ### Step 5: Summarization and Keyword Creation with OpenAI
   
   This is where things get exciting. Each piece of content is sent to OpenAI’s GPT-4-1106-preview model via n8n’s OpenAI node, twice:
   
   - Once to generate a concise summary in under 70 words
   - And once to extract the top 3 technical keywords
   
   Through templated prompts, the AI outputs actionable metadata that transforms verbose corporate press releases into distilled, searchable assets.
   
   ---
   
   ### Step 6: Merge and Structure the Output
   
   Following the AI processing, the data is merged:
   
   - Summary and keywords are first joined
   - Then merged again with their original date, title, and URL
   - Fields are renamed appropriately for downstream processing
   
   This structured dataset becomes a versatile, condensed version of each news item—perfect for newsletters, tagging, or internal reporting.
   
   ---
   
   ### Step 7: Store to NocoDB Database
   
   The final step leverages n8n’s NocoDB integration. Data points—source, title, date, link, summary, and keywords—are pushed to a cloud-based SQL database. NocoDB provides a spreadsheet-like interface which makes further filtering, tagging, and exporting incredibly simple.
   
   ---
   
   ### Why This Workflow Matters
   
   This approach enables:
   
   - **Complete Automation**: Zero manual intervention needed after setup.
   - **Ongoing Monitoring**: Weekly scanning ensures you're always up to date.
   - **Natural Language Processing**: GPT-4 imparts keyword intelligence and executive summaries.
   - **Structured Storage**: Easy access and further filtering in SQL-based environments like NocoDB.
   
   Professionals and teams can use this setup to track competitors, monitor industry trends, or populate internal dashboards—without needing engineering resources.
   
   ---
   
   ### Technologies & APIs Used
   
   1. **n8n** – Open-source workflow automation tool
   2. **OpenAI GPT-4 API** – For summarization and keyword extraction
   3. **NocoDB API** – To store structured results
   4. **CSS Selectors (via browser inspection tools)** – Used to scrape targeted content
   5. **JavaScript (in Code nodes)** – For filtering recent posts
   
   ---
   
   ### Final Thoughts
   
   This n8n-based solution is a shining example of how no-code tools can bridge gaps in web infrastructure—like the absence of an RSS feed—using smart automation, cloud APIs, and a few lines of JavaScript. Whether you're in media, marketing, or monitoring, this setup delivers a turnkey framework for staying informed without lifting a finger every week.
   
   Want to adapt this for a different site or dataset? You’ll need some basic knowledge of CSS selectors—and of course, respect the target website’s terms of use. For questions or improvements, the workflow creator is open to suggestions over on Mastodon: [@askans@bonn.social](https://bonn.social/@askans)
   
   ---
   
   Happy Scraping!

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.