AI Skills & MCP Server Development
Build custom skills and MCP servers and connect Claude Code, Open Code, and GitHub Copilot to your internal systems and APIs.
What you will learn in this course
WHY THIS COURSE IS A GAMECHANGER: Teams often struggle to extend AI assistants beyond their standard functions. This means missing out on the opportunity to connect AI with smart and effective internal systems. In this training, we show you how to develop custom skills and MCP servers and turn your AI assistant into a powerful interface for your entire toolchain.
ABOUT THIS COURSE: Our comprehensive 2-day training provides engineering teams with practical skills for extending AI assistants through custom skills and Model Context Protocol (MCP) servers. You'll learn to evaluate and deploy existing MCP servers, develop custom skills for Claude Code, Open Code, and GitHub Copilot, and create your own MCP servers that connect AI assistants to internal REST APIs and enterprise systems.
In extensive hands-on labs, you will develop a working MCP server that connects to an API of your choice and can serve as a template for future integrations. Whether you want to connect AI to databases, internal tools, or proprietary systems, this training provides the foundation.
Agenda
Skills & MCP Overview
MCP Architecture Deep Dive
Finding & Evaluating Servers
MCP Server Installation
Building Custom Skills
MCP Server Development
Testing & Debugging
Enterprise Integration Patterns
Security Considerations
Hands-On Labs
audience
This course is designed for
- Software engineers extending AI assistants with custom capabilities
- DevOps and platform engineers connecting AI to infrastructure tools
- Tech leads evaluating MCP adoption for their teams
prerequisites
To get most out of this course, you should have:
- Basic experience with AI coding assistant (like Claude Code, Open Code, or GitHub Copilot)
- Basic proficiency in Python, TypeScript, or Go for server development
- Basic understanding of REST APIs and JSON
style
Our trainers have years of experience and will deliver the right mix of:
- Interactive lectures with live demonstrations
- Hands-on labs
- Architecture discussions and code reviews
- Q&A sessions
Technical requirements
We recommend the following equipment:
- Stable internet connection
- Access to AI assistant (Claude Code, Open Code, or GitHub Copilot)
- Modern web browser (Chrome, Firefox, Edge, etc.)

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