spring-ai-agent-utils

🥈Silver

Spring AI Agent Utils is a Java library that integrates Claude Code-inspired tools and agent skills into Spring-based AI applications. It enables developers to automate tasks, enhance workflows, and improve operational efficiency. The library connects to supported agents like Claude, facilitating integration with existing Spring projects.

243130Updated 5d ago

Intermediate30min to implementautomation

Saves ~180 min per use

Quick InstallView Source

git clone https://github.com/spring-ai-community/spring-ai-agent-utils.git

Works with:

Claude

Overview

About This Skill

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/spring-ai-community/spring-ai-agent-utils

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

Using the spring-ai-agent-utils library, create a workflow that automates customer support ticket categorization for [COMPANY] in the [INDUSTRY] sector. The workflow should process [DATA] from customer emails and categorize them into predefined support categories with at least 90% accuracy. Include error handling for ambiguous cases.

Example Output

# Customer Support Ticket Categorization Workflow

## Workflow Overview
This workflow automates the categorization of customer support tickets for TechSolutions Inc., a software company in the technology sector. The workflow processes customer email data and categorizes them into predefined support categories with 92% accuracy.

## Input Data
- Customer email data from the past 30 days
- Predefined support categories: Technical Issues, Billing Questions, Feature Requests, Account Management, and General Inquiries

## Output
- Categorized support tickets with confidence scores
- List of ambiguous cases requiring human review

## Workflow Steps
1. **Data Ingestion**: Ingest customer email data from the company's email server.
2. **Text Preprocessing**: Clean and preprocess the email text data.
3. **Categorization**: Use the spring-ai-agent-utils library to categorize each email into one of the predefined support categories.
4. **Confidence Scoring**: Assign a confidence score to each categorization.
5. **Ambiguity Handling**: Flag emails with confidence scores below a threshold for human review.
6. **Output Generation**: Generate a report of categorized tickets and ambiguous cases.

## Error Handling
- For emails with low confidence scores, flag them for human review.
- For emails that do not fit into any category, categorize them as 'General Inquiries'.