This article provides a complete, practical walkthrough of the Stopanderror Splitout Export 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:  
   Automated Clockify Time Tracking Backup to GitHub with n8n
   
   Meta Description:  
   Discover how this n8n workflow automates the extraction and backup of Clockify time tracking data into GitHub, ensuring secure, up-to-date, and versioned archival of timesheet reports.
   
   Keywords:  
   n8n workflow, Clockify automation, time tracking backup, GitHub integration, time report automation, API data sync, automated data pipeline, open source workflow automation, JSON manipulation, timesheet archive
   
   Third-party APIs Used:  
   1. Clockify API  
   2. GitHub API
   
   —
   
   Article:
   
   Automating Time Tracking Backups from Clockify to GitHub with n8n
   
   In the age of distributed teams and digital time management, keeping accurate logs and backups of time tracking data is mission-critical. Yet, doing it manually is error-prone and time-consuming, especially when data changes retroactively. That’s where n8n — the powerful open-source workflow automation tool — comes into play.
   
   This article breaks down an advanced, yet accessible, n8n workflow template that automatically backs up detailed time tracking reports from Clockify and syncs them into a GitHub repository. Designed for daily execution, this workflow ensures your company or personal project has reliable, version-controlled access to timesheet data from the current and previous months.
   
   Let’s explore how this automation works.
   
   Overview of the Workflow
   
   Named the “Clockify Backup Template,” this workflow connects the Clockify API and GitHub API through a set of logic-driven nodes. The workflow is scheduled to run daily and backs up Clockify’s detailed monthly reports into a GitHub repository as JSON files.
   
   Here’s how the workflow functions from start to finish:
   
   1. Scheduled Execution  
   The Schedule Trigger node kicks off the workflow once a day (by default at 5 AM). This ensures that your backup files in GitHub stay up-to-date with data from Clockify.
   
   2. Initialize Context and Inputs  
   The workflow proceeds by retrieving your Clockify workspace via the Clockify API. This gets the workspace ID, which can be manually overridden if needed.
   
   It also initializes global variables for the target GitHub repository (owner and repo name) and defines a default backup range of three months: the current month, the previous month, and two months ago.
   
   3. Monthly Iteration Logic  
   Using the Set and Split Out nodes, the workflow goes through the month indexes (0, 1, 2) and dynamically sets the report name, start date, and end date for each backup iteration.
   
   For example:  
   - “detailed_report_2024-09”  
   - Start date: 2024-09-01  
   - End date: 2024-09-30
   
   4. Pulling the Data  
   A POST request is made to the Clockify Detailed Reports API for each monthly interval. This retrieves all time entries in the workspace for the specified date range in JSON format.
   
   5. GitHub File Check  
   The workflow checks if a corresponding monthly report JSON file already exists in your GitHub repo under the /reports/ directory. If the file does not exist (404), a new one is created. If it does exist, the workflow proceeds to compare and possibly update it.
   
   6. Compare and Sync  
   The Extract from File node loads the existing file’s content (if any), and the Compare Datasets node checks for differences between the fresh data from Clockify and what’s stored in GitHub. If they differ, the file is updated with a new commit.
   
   7. Smart Backup Behavior  
   The workflow intelligently skips empty reports (i.e., if no time entries have been recorded for the month) and halts with an error if unexpected API issues arise (excluding known 404s handled by design).
   
   Why This Matters
   
   - 📌 Version Control: By storing backups in GitHub, you're harnessing all the benefits of Git, including transparency, audit trails, and collaboration tools.
   - ♻️ Data Freshness: Since user-entered time entries in Clockify may be edited retroactively (especially if associated tags or projects change), the system ensures previous months’ data remains accurate.
   - 🕒 Set-and-Forget: The daily automation minimizes effort while maximizing accountability and data integrity long-term.
   
   Customization Possibilities
   
   This template-style workflow is designed to be flexible:
   - Update the monthIndex array to expand or reduce the number of months archived.
   - Modify the schedule for more or less frequent backups.
   - Adapt to another version-control platform beyond GitHub using similar HTTP logic.
   
   Final Notes
   
   This workflow exemplifies the power of combining n8n’s node-based automation with modern APIs to replace what used to be manual, repetitive, and error-prone processes. Whether you're a solo freelancer, a team lead, or a CTO building internal tooling, automating your time-tracking backups can bring peace of mind and ensure historical accuracy.
   
   Ready to automate your timesheet backups?
   
   Explore the full workflow and adapt it to your needs with n8n.
   
   —  
   By embracing open-source workflows like this one, you can keep your operations lean, maintain data integrity, and unlock the full potential of API-first work culture.

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.