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.

4311760Updated Today

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/

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

I want to build a low-code agentic AI application for [COMPANY] in the [INDUSTRY] sector. The application should [DESCRIBE PRIMARY FUNCTIONALITY]. I need guidance on integrating [SPECIFIC TOOLS OR PLATFORMS] and structuring the workflow. Can you provide a step-by-step plan with best practices for this project?

Example Output

# Low-Code Agentic AI Application Plan for GreenTech Solutions

## Project Overview
- **Company**: GreenTech Solutions
- **Industry**: Renewable Energy
- **Primary Functionality**: Automated energy consumption analysis and optimization
- **Key Tools**: n8n, Loveable, Supabase, MCP

## Step-by-Step Plan
1. **Define Core Workflow**:
- Use n8n to create a workflow that ingests energy consumption data from IoT devices.
- Implement data validation and preprocessing steps.
2. **Integrate AI Components**:
- Utilize Loveable to build a conversational interface for user queries.
- Deploy MCP for predictive analytics on energy usage patterns.
3. **Database Setup**:
- Configure Supabase to store historical data and user preferences.
- Ensure real-time data synchronization with the frontend.
4. **User Experience Design**:
- Use UXPilot to design intuitive dashboards for energy consumption insights.
- Implement role-based access control for different user levels.
5. **Testing and Deployment**:
- Conduct thorough testing of the integrated workflow.
- Deploy the application on a scalable cloud platform.

## Best Practices
- **Modular Design**: Ensure each component is modular for easy updates and maintenance.
- **Security Measures**: Implement robust security protocols to protect sensitive data.
- **Scalability**: Design the system to handle increased data loads and user interactions.

## Expected Outcomes
- Automated energy consumption analysis and optimization.
- Enhanced user experience with intuitive dashboards.
- Scalable and secure application for future expansion.