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:  
   🚀 Boosting Visibility in the n8n Community: How an AI-Powered Workflow Surfaces Top Creators and Workflows
   
   Meta Description:  
   Discover how an advanced n8n workflow uses AI to analyze and generate detailed stats on top community contributors and their popular workflows. Featuring live data from GitHub and AI-generated Markdown reports.
   
   Keywords:  
   n8n workflow, AI automation, OpenAI GPT-4o, GitHub API, n8n leaderboard, workflow statistics, n8n creators, community analytics, Markdown reporting, workflow metrics, LangChain, open source automation
   
   Third-Party APIs Used:
   
   1. OpenAI API
      - Used within the gpt-4o-mini node to generate comprehensive Markdown reports via AI.
      - Credential used: OpenAi account.
   
   2. GitHub (raw.githubusercontent.com)
      - JSON files containing creator and workflow stats are fetched from:
        https://github.com/teds-tech-talks/n8n-community-leaderboard
      - Used within HTTP Request nodes to retrieve:
        - stats_aggregate_creators.json
        - stats_aggregate_workflows.json
   
   Overview Article:
   
   Discovering and recognizing top contributors within open-source communities is essential for engagement, motivation, and continuous improvement. The “AI Agent for n8n Creators Leaderboard – Find Popular Workflows” n8n workflow precisely fulfills this mission. Through a thoughtful combination of automation, data acquisition, and AI-enhanced reporting, this workflow empowers users, moderators, and project leaders to surface valuable insights about who’s influencing the n8n automation ecosystem and how.
   
   This article explores how this workflow operates, the technology behind it, and how users can benefit from effortless access to creator statistics using AI-driven markdown summaries.
   
   📊 Central Idea  
   At its core, the workflow fetches creator and workflow statistics from a GitHub-hosted JSON dataset and matches them by username. Then, it filters based on input (typically a username), merges statistics, and invokes an AI agent to generate a detailed summary in Markdown format. This summary includes a thorough breakdown of the creator's contributions and why their workflows are popular—all saved beautifully in locally stored `.md` reports.
   
   🚀 Key Functional Blocks
   
   1. Data Aggregation via GitHub  
   The workflow begins by fetching two key JSON datasets directly from a GitHub repository:
   - stats_aggregate_creators.json
   - stats_aggregate_workflows.json
   
   These files contain up-to-date performance metrics such as the number of unique weekly/monthly visitors and inserters for each community-shared workflow.
   
   2. Data Processing and Filtering  
   Once the raw data lands in the workflow via the HTTP Request nodes, it undergoes several stages:
   - Extracted into arrays for manipulation
   - Sorted by key metrics (e.g., weekly inserters)
   - Limited to the most relevant entries (top 25 creators / top 300 workflows)
   - Matched across datasets on a common field (username)
   - Optionally filtered by a specific username (e.g., “joe”) provided by the user either via chat input or direct workflow execution
   
   3. AI-Powered Summary Generator  
   The magic happens when the unified insights are fed into a special LangChain-enabled AI Agent node utilizing OpenAI’s GPT-4o-mini model. The prompt is structured with Markdown formatting rules that instruct the AI to:
   - Write a summary of the creator’s contributions
   - Generate a table of their most used workflows, including visit and insert metrics
   - Analyze patterns and provide community engagement insights
   - Offer contextual reasons for each workflow’s popularity
   
   4. Use of LangChain Tooling   
   The GPT-powered AI agent is powered further by memory tools (like buffer window memory), and tool-calling capabilities that invoke existing workflow functions for data wrangling. This modular approach enhances both scalability and transferability of AI capabilities within n8n workflows.
   
   5. Exporting and Saving Reports  
   After the summary is generated, the text is converted to a Markdown file and saved locally with an automatically formatted timestamp (e.g., creator-summary-2024-05-15-12-45-01.md). This makes organizing and reviewing reports across multiple contributors incredibly easy.
   
   🧠 Example Prompt:  
   “Show me stats for username joe”
   
   With a single chat-like trigger, the workflow is kicked off, installations queried, data processed, and a report returned. This fast, conversational approach lowers the barrier for accessing meaningful insights.
   
   🔥 Why This Matters
   
   For Community Contributors:  
   This is a feedback loop with fidelity. By understanding who’s extracting value from your workflows and how frequently, creators can emphasize what works, explore improvements, and boost collaboration.
   
   For n8n Team or Moderators:  
   Community administrators gain a data-backed view of engagement trends. They can spotlight rising stars, award contributions, or analyze workflow usage patterns across the globe.
   
   For All Users:  
   A fantastic way to explore trending workflows, get inspired, and onboard more rapidly into the n8n ecosystem.
   
   ⚙️ Integration Highlights:
   - OpenAI GPT-4o-mini: For intelligent report generation
   - GitHub Raw URLs: For live data sourcing
   - LangChain: To manage AI memory and execution flow
   - n8n Automation Tools: For sorting, filtering, merging, and triggering workflows
   
   🌐 Summary  
   This is more than just a dashboard—it’s an AI-powered community amplifier. By blending powerful backend automation, accessible AI interfaces, and the transparency of open-source data, the n8n Creators Leaderboard Workflow makes sense of the noise in community interaction. Whether you're building, learning, or leading, tools like this give meaningful insight into what—and who—is driving value across the n8n community.
   
   You can explore the full project and leaderboard here:
   📍 GitHub Repository: https://github.com/teds-tech-talks/n8n-community-leaderboard  
   📊 Leaderboard Site: https://teds-tech-talks.github.io/n8n-community-leaderboard/
   
   Let automation showcase your impact.

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.