This article provides a complete, practical walkthrough of the Limit Code Automation 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 Strava Activity Tracking with n8n and Google Sheets
   
   Meta Description:
   Discover how this n8n workflow automatically syncs your latest Strava activities to Google Sheets every two hours, filters duplicates, and ensures clean data logging using advanced data processing nodes.
   
   Keywords:
   Strava automation, n8n workflow, Google Sheets integration, automating Strava to Google Sheets, Strava API, fitness tracking automation, n8n examples, cycling data logging, remove duplicates n8n, JavaScript n8n code
   
   Third-Party APIs Used:
   
   - Strava API (via n8n's native Strava node)
   - Google Sheets API (via n8n's Google Sheets node)
   
   Article:
   
   Automating Strava to Google Sheets: A No-Code Workflow with n8n
   
   For athletes, cyclists, and data enthusiasts who regularly track their stats on Strava, having a centralized and accessible view of performance data—like kilometers ridden, elevation gain, and time—is invaluable. But manually transferring that data into spreadsheets can be both repetitive and error-prone.
   
   In this article, we showcase an elegant automation built using n8n, the powerful open-source workflow automation tool. This workflow pulls the latest Strava activities, filters out already-logged entries, and appends the new data to a Google Sheet—all without lifting a finger.
   
   Let’s walk through how the workflow functions and what makes it a robust automation for fitness tracking.
   
   ⌛ Triggered Every Two Hours
   
   At its foundation is a Schedule Trigger node set to activate every two hours. This ensures that your automation continuously keeps tabs on recent activity without any manual initiation. Whether you're on a short bike commute or a multi-hour endurance ride, this workflow ensures frequent updates.
   
   🌐 Fetching New Activities from Strava
   
   The Strava node ('Strava') is used to retrieve your most recent 10 activities using the “getAll” operation. This data includes everything from timestamps to route distance, elevation gained, and total moving time. Once fetched, the data moves to a sorting node to be organized by activity ID, preparing it for later deduplication and processing.
   
   📈 Reading Existing Activities from Google Sheets
   
   Concurrently, the workflow reads from your target Google Sheet (named ‘n8n’ within the document ‘Sherlo_Bike’) to retrieve the last 10 previously saved activities. After sorting and limiting, a Set node ('saved_last') extracts only the unique reference IDs (activity IDs) of the last entries to cross-reference against the new Strava data.
   
   🧠 Data Processing with Custom JavaScript
   
   The core logic resides in a Code node that acts as a “smart gatekeeper.” Here, the workflow compares the freshly pulled Strava activities against the existing saved IDs. Written in JavaScript, the script converts all IDs to strings, creates a Set of existing ones, and filters out any activities that have already been logged.
   
   This ensures that duplicate entries never clutter your Google Sheet—even if the activity history fetched from Strava contains previously saved workouts. You can think of it as an intelligent layer that keeps your data tidy and up-to-date.
   
   🧽 Cleaning Up Duplicates
   
   To further bulletproof the process, a Remove Duplicates node sits between the sort and limit nodes in the Strava branch of the workflow. It guarantees each activity is unique before it goes through the main processing lane. Though the Code node is already responsible for filtering based on saved IDs, this extra layer makes the pipeline more resilient and avoids accidental duplicates due to inconsistencies or retries.
   
   🧾 Structuring the Output with Enriched Data
   
   A Set node ('strava_last') converts raw Strava responses into formatted and readable information for spreadsheet logging. Instead of dumping raw JSON, it calculates:
   
   - Date (in DD/MM/YYYY format)
   - Distance in kilometers (rounded to the nearest 0.1 km)
   - Total elevation (in meters)
   - Moving time (in HH:MM:SS)
   - Direct URL to the Strava activity
   
   This transformation step is essential for readability and future analysis.
   
   📤 Appending to Google Sheets
   
   Finally, the clean and contextualized activity data is appended to a Google Spreadsheet using the Google Sheets node in n8n. Field mappings ensure each value lands in the correct column: date, distance, time, elevation, unique reference (ID), and a clickable link to the ride on Strava.
   
   What's impressive is that you can customize this further to include fields like terrain type, bike used, or heart rate zones—just extend your Strava node’s fields and tweak the Set and Google Sheets nodes accordingly.
   
   🎯 Why This Workflow Stands Out
   
   This isn’t just a simple fetch-and-log pipeline. It demonstrates powerful conditional filtering and real-time deduplication—a major consideration when dealing with repetitively fetched external data. The combination of nodes (sort, limit, set, code, removeDuplicates) provides both data hygiene and flexibility without the need to write a full backend service.
   
   Furthermore, everything is done visually in n8n, making it approachable even for users with limited coding background, while keeping it extensible for power users.
   
   🚀 Final Thoughts
   
   With just a handful of nodes and a bit of JavaScript, this n8n workflow transforms how you interact with your fitness data. Whether you're gearing up for your next big ride or simply like keeping your data log pristine, automating Strava-to-Sheets syncing is a game-changer.
   
   Want to take it a step further? Add alerts via Telegram or Slack, visualize data using Google Data Studio, or trigger recovery tips after long rides—all with n8n’s modular ecosystem.
   
   The future of fitness tracking lies in smart, automated systems like these. Roll on, automate more, and break a sweat knowing your data is well taken care of.
   
   — End —

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.