yomo

🥈Silver

YoMo is a serverless AI agent framework with geo-distributed edge AI infrastructure. It enables real-time, low-latency automation for operations teams. Connects to OpenAI, Claude, and other AI models for distributed cloud workflows.

1,8841400Updated Today

Intermediate30min to implementautomation

Saves ~190 min per use

Quick InstallView Source

git clone https://github.com/yomorun/yomo.git

Works with:

Claude

Overview

About This Skill

https://yomo.run/

Use Cases

Automate real-time weather information retrieval for user queries.

Build AI agents that provide personalized recommendations based on user preferences.

Create chatbots that can handle customer inquiries with low latency responses.

Implement serverless AI functions for processing streaming data in real-time.

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/yomorun/yomo

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

Deploy a serverless AI agent using Yomo for [APPLICATION]. Configure it to process data from [DATA_SOURCE] and provide real-time insights based on [SPECIFIC_CRITERIA]. Ensure it utilizes geo-distributed edge AI infrastructure to optimize performance.

Example Output

After deploying the serverless AI agent for the smart city traffic management system, the Yomo framework processed data from real-time traffic cameras located at various intersections. The agent analyzed traffic flow patterns and identified peak congestion times at Main St. and 5th Ave. It recommended adjusting traffic light timings to reduce wait times by 30% during rush hours. Additionally, the agent provided insights on alternative routes for drivers, helping to alleviate congestion and improve overall traffic efficiency. The geo-distributed edge AI infrastructure ensured that data processing occurred locally, minimizing latency and enhancing responsiveness to real-time conditions.