learn-low-code-agentic-ai

🥈Silver

Low-code full-stack agentic AI development using LLMs, n8n, Loveable, UXPilot, Supabase, and MCP. Enables operations teams to build and deploy AI agents with minimal coding, integrating workflows and databases for automation.

4431760Updated 4d ago

Intermediate30min to implementautomation

Saves ~240 min per use

Quick InstallView Source

git clone https://github.com/panaversity/learn-low-code-agentic-ai.git

Works with:

Claude

Overview

About This Skill

https://panaversity.org/

How to Use

[{"step":"Define your use case","action":"Identify the specific task or workflow you want to automate (e.g., customer support triage, inventory management, or lead qualification). Document the data sources, actions, and integrations required.","tip":"Start with a small, high-impact process to validate the workflow before scaling."},{"step":"Set up your stack","action":"Create accounts for Supabase (database), n8n (workflow automation), Loveable (AI agents), UXPilot (UI automation), and MCP (service integrations). Configure API keys and permissions for each tool.","tip":"Use Supabase’s free tier for prototyping and n8n’s cloud version for easier deployment."},{"step":"Design the workflow","action":"Use n8n to create a workflow that ingests data into Supabase, triggers the Loveable agent, and handles automated actions via UXPilot. Configure MCP for any external service integrations (e.g., Slack, CRM).","tip":"Break the workflow into small, testable components (e.g., data ingestion first, then AI processing)."},{"step":"Deploy and monitor","action":"Deploy the workflow using n8n’s scheduler or webhook triggers. Monitor performance in Supabase, n8n logs, and UXPilot dashboards. Iterate based on feedback or errors.","tip":"Set up alerts in n8n or Supabase for failures (e.g., API timeouts, agent errors)."},{"step":"Optimize and scale","action":"Review the workflow’s efficiency and expand its scope (e.g., add more data sources, refine AI prompts, or include additional automation steps). Document the process for team onboarding.","tip":"Use Supabase’s real-time features to trigger actions immediately when data changes."}]

Use Cases

Automate data retrieval and processing workflows using n8n to connect various APIs and databases.

Create a responsive AI helpdesk that reads user inquiries and escalates to human agents when necessary.

Develop a report generator that fetches data from APIs, summarizes findings with LLMs, and exports results to various formats.

Build a research assistant that scrapes web pages, organizes data, and allows users to query the information interactively.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/panaversity/learn-low-code-agentic-ai

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Build a low-code agentic AI workflow that [DESCRIBE_TASK] using n8n, Loveable, UXPilot, Supabase, and MCP. The workflow should include: 1) Data ingestion from [SOURCE] into Supabase, 2) An AI agent in Loveable that processes the data with [SPECIFIC_PROMPT], 3) Automated actions triggered by UXPilot when [CONDITION] is met, and 4) Integration with MCP for [SPECIFIC_SERVICE]. Use minimal code and provide a step-by-step deployment guide in Markdown format.

Example Output

### Low-Code Agentic AI Workflow: Automated Customer Support Triage

**Workflow Overview**
This workflow automates the triage of customer support tickets by routing urgent issues to the right team with minimal human intervention. Built using n8n for workflow orchestration, Loveable for AI agent logic, UXPilot for UI automation, Supabase as the database, and MCP for Slack integration.

**Step 1: Data Ingestion**
Customer support tickets from Zendesk are ingested into Supabase via n8n. The n8n workflow runs every 15 minutes, fetching new tickets with a status of 'new' or 'open' and storing them in the `tickets` table with columns: `id`, `subject`, `description`, `priority`, `customer_email`, and `created_at`. A trigger in n8n checks for new tickets and passes the data to the Loveable agent.

**Step 2: AI Agent Processing**
The Loveable agent analyzes each ticket using the following prompt: *"Analyze the following customer support ticket and classify it into one of these categories: 'Technical Issue', 'Billing Question', 'Feature Request', or 'General Inquiry'. Assign a priority score from 1-10 based on urgency. Return the classification, priority score, and a suggested response template in JSON format."* For example, a ticket with subject *"Payment failed for subscription #12345"* is classified as *"Billing Question"* with a priority score of *9* and a suggested response: *"We noticed a failed payment for your subscription. Please update your payment method here: [link]. Let us know if you need assistance."*

**Step 3: Automated Actions**
UXPilot monitors the Supabase `tickets` table for rows where `priority >= 8` or `classification = 'Technical Issue'`. When triggered, UXPilot opens a Slack channel named `#urgent-tickets` and posts a message with the ticket details, suggested response, and a button to 'Assign to Team'. Clicking the button triggers an n8n workflow that updates the ticket status to 'assigned' and emails the assigned team member.

**Step 4: MCP Integration**
MCP is used to fetch real-time Slack user data for assigning tickets. When a ticket is assigned via UXPilot, MCP retrieves the list of available support agents and their current workload from Slack, then suggests the least busy agent for assignment. The agent’s Slack ID is stored in the Supabase `tickets` table.

**Deployment Steps**
1. Set up Supabase with the `tickets` table schema.
2. Configure n8n to fetch Zendesk tickets and trigger the Loveable agent.
3. Deploy the Loveable agent with the classification prompt and JSON output schema.
4. Set up UXPilot to monitor Supabase and trigger Slack actions.
5. Integrate MCP with Slack to fetch agent availability data.

**Result**
This workflow reduced manual triage time by 70% and improved response times for urgent tickets by 40%. The entire system was built with less than 50 lines of custom code, primarily in n8n and Loveable.