ai-tools

🥈Silver

Collection of reusable AI tools including Claude Code skills and MCP servers for enhanced development. Benefits operations teams by automating workflows and improving efficiency. Connects to Flutter/Dart development environments and supports Claude agents.

2770Updated 1mo ago

Intermediate30min to implementautomation

Saves ~10 min per use

Quick InstallView Source

git clone https://github.com/calvingit/ai-tools.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/calvingit/ai-tools

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

Create a reusable AI tool for [COMPANY] that automates [SPECIFIC TASK] in [INDUSTRY]. The tool should integrate with [DEVELOPMENT ENVIRONMENT] and support [SPECIFIC FEATURES]. Provide a detailed implementation plan.

Example Output

# AI Tool: Automated Customer Support Ticket Triage

## Overview
This AI tool automates the initial triage of customer support tickets for a SaaS company, categorizing and prioritizing tickets based on content and urgency.

## Features
- **Automated Categorization**: Uses NLP to classify tickets into predefined categories (Technical, Billing, General Inquiry, etc.)
- **Priority Assignment**: Assigns priority levels (Low, Medium, High, Critical) based on keywords and sentiment analysis
- **Integration**: Connects to Zendesk and Slack for seamless workflow
- **Reporting**: Generates daily/weekly reports on ticket volumes and trends

## Implementation Plan
1. **Data Collection**: Gather historical ticket data for training
2. **Model Training**: Fine-tune a pre-trained NLP model on the collected data
3. **Integration Development**: Build connectors for Zendesk and Slack APIs
4. **Testing**: Validate accuracy with a sample of recent tickets
5. **Deployment**: Roll out to production with monitoring in place

## Expected Outcomes
- 30% reduction in initial response time
- Improved ticket assignment accuracy
- Better resource allocation based on priority