This article provides a complete, practical walkthrough of the Limit Splitout Automate 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:
   AI-Powered Insights: Exploring the n8n Creators Leaderboard Workflow
   
   Meta Description:
   Discover how the n8n Creators Leaderboard Workflow leverages automation, GitHub data, and AI to generate insightful Markdown reports analyzing top contributors and workflows in the n8n community.
   
   Keywords:
   n8n workflows, n8n community, AI reporting, GitHub automation, leaderboard statistics, OpenAI GPT-4o, workflow analytics, community-driven automation, markdown reports, workflow popularity, n8n creator stats
   
   Third-Party APIs Used:
   
   1. GitHub Raw Content API
      - Used to fetch JSON statistics for creators and workflows from a public GitHub repository.
   
   2. OpenAI API (GPT-4o-mini)
      - Powers the AI agent that generates dynamically structured and detailed Markdown reports.
   
   —
   
   Article:
   
   # AI-Powered Insights: Exploring the n8n Creators Leaderboard Workflow
   
   As the no-code/low-code movement continues to gain traction, n8n has emerged as a powerful tool for visual workflow automation. With a thriving open-source community and a growing library of community-built workflows, understanding what makes certain workflows and creators excel is valuable for contributors, users, and community managers alike.
   
   The "AI Agent for n8n Creators Leaderboard – Find Popular Workflows" is an innovative workflow designed within n8n to automate the process of gathering and analyzing contributions across the community. By integrating GitHub data sources, AI-powered text generation, and local file saving, this workflow generates insight-rich, Markdown-format reports spotlighting individual creators and their most impactful workflows.
   
   Let’s dive into how it works and what makes it a powerful tool for the n8n ecosystem.
   
   ---
   
   ## How It Works
   
   ### 1. Fetch Community Data from GitHub
   
   At the core of this workflow is data gathering from a GitHub repository (https://github.com/teds-tech-talks/n8n-community-leaderboard), which contains regularly updated JSON files. These include statistics like:
   
   - Creator usernames, bios, and insertion metrics
   - Workflow visitation and insertion metrics (weekly/monthly)
     
   Two HTTP Request nodes (`stats_aggregate_creators` and `stats_aggregate_workflows`) retrieve the `stats_aggregate_creators.json` and `stats_aggregate_workflows.json` files from GitHub's raw content URLs. These files are parsed and prepared for further processing within the workflow.
   
   ### 2. Process and Merge the Data
   
   Once the data is pulled in, it’s parsed into arrays by separate “Set” nodes before being split into individual items. The workflow then:
   
   - Sorts workflows by weekly usage (insertions)
   - Limits to the top 300 workflows
   - Sorts creators by weekly performance
   - Limits to the top 25 creators
   
   From there, workflows and creator data are merged based on matching usernames, enriching each workflow with insight from the creator who built it. This association allows for a comprehensive view of creator performance and impact.
   
   ### 3. Filter by Username
   
   Whether run on a schedule, triggered by a chat message, or executed manually, this workflow allows users to focus the analysis on a specific creator using a simple username input (e.g., “joe”). A filter node ensures that only workflows and statistics by this user are further examined.
   
   ### 4. Generate Reports with AI
   
   Here’s where things get exciting—the data is passed to an AI agent powered by the OpenAI GPT-4o-mini model. Using a carefully crafted prompt, the agent generates a rich Markdown report that includes:
   
   - A summary of the selected creator’s contributions
   - A Markdown table highlighting workflow names, descriptions, weekly/monthly usage statistics, and explanations for their popularity
   - Community analysis and behavior trends
   - Additional insights into the creator’s engagement or influence in the community
   
   The use of an AI model elevates this beyond a static data report by providing contextual analysis in natural language.
   
   ### 5. Save and Access Results
   
   Finally, the generated Markdown content is formatted and converted into a downloadable `.md` file. If the user is running the workflow locally, it's saved directly to their file system with a timestamp for easy reference.
   
   ---
   
   ## Key Benefits
   
   - 🧠 Smart Report Generation: Let the AI do the heavy lifting by turning raw metrics into readable, structured insights.
   - 📊 Comprehensive Analysis: Combines data from both creators and their workflows for deeper understanding.
   - 📁 Easy Output: Saves reports locally as Markdown files for documentation or sharing.
   - 💬 Chat-Triggered Automation: Users can activate the workflow by simply messaging `show me stats for username [name]`.
   
   ---
   
   ## Use Cases
   
   - 🎯 For Workflow Creators:
     Monitor the reach and metrics of your workflows, and strategically improve or promote the ones with potential.
     
   - 🧑‍💼 For Community Managers:
     Identify top contributors and community trends to highlight excellence or improve contribution strategies.
   
   - 🧩 For New Users:
     Discover which community workflows are most popular and worth trying in your own automation setups.
   
   ---
   
   ## Closing Thoughts
   
   The n8n Creators Leaderboard Workflow is a brilliant demonstration of n8n’s own power—using automation to understand and support the very community that builds it. With AI integration and GitHub-powered data feeds, this tool automates the generation of performance and engagement insights for better transparency, recognition, and knowledge sharing.
   
   For those building in the n8n ecosystem or managing communities around low-code tools, this workflow is a must-have asset.
   
   You can view the project and contribute on GitHub:
   🔗 https://github.com/teds-tech-talks/n8n-community-leaderboard
   
   Empower your community. Automate your insights.

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.