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dotnet-semantic-kernel

Configure Microsoft Semantic Kernel for .NET AI integration with plugins and prompt templating.

10|Updated Jan 28, 2026
Search Tags:#ai#llm#rag#agents#plugins#.net#semantic kernel
One-click install
npx skills add https://github.com/AGIBuild/Agibuild.Fulora --skill dotnet-semantic-kernel
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dotnet-semantic-kernel
Source: https://github.com/AGIBuild/Agibuild.Fulora/tree/main/.cursor/skills/dotnet-semantic-kernel
Command: npx skills add https://github.com/AGIBuild/Agibuild.Fulora --skill dotnet-semantic-kernel

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the integration of advanced AI and Large Language Model (LLM) capabilities into .NET applications, enabling developers to build intelligent features efficiently.

Core Features & Use Cases

  • AI Orchestration: Set up and configure the Semantic Kernel for various AI services (Azure OpenAI, OpenAI, Ollama).
  • Function Calling & Plugins: Define and integrate custom .NET functions that AI models can invoke to access real-world data and perform actions.
  • Prompt Engineering: Utilize powerful templating engines like Handlebars for dynamic and context-aware prompt generation.
  • Memory & RAG: Implement Retrieval-Augmented Generation patterns by integrating with vector stores for knowledge retrieval.
  • Agent Framework: Build sophisticated multi-agent systems for complex task collaboration.
  • Use Case: Develop a customer support chatbot that can access order history via a plugin, understand user queries through natural language prompts, and provide personalized responses by retrieving relevant information from a knowledge base.

Quick Start

Use the dotnet-semantic-kernel skill to set up a basic kernel with Azure OpenAI chat completion.

Frequently Asked Questions about dotnet-semantic-kernel

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I integrate AI orchestration into a .NET application using Semantic Kernel?▼

AI orchestration in .NET is achieved by configuring the Semantic Kernel to connect with services like Azure OpenAI, OpenAI, or Ollama. This provides a structured framework for setting up chat completion and invoking native functions.

Can I use Semantic Kernel to build Retrieval-Augmented Generation (RAG) patterns?▼

Yes, Semantic Kernel supports RAG patterns by integrating with vector stores for knowledge retrieval. This allows your .NET applications to fetch external context and feed it into the language model for accurate responses.

What is the best way to define custom functions that an LLM can invoke in .NET?▼

The best way to enable LLM function calling is by developing custom .NET plugins. Semantic Kernel allows you to define native functions that the AI models can automatically invoke to access real-world data and perform actions.

Does Semantic Kernel support prompt templating for dynamic prompt generation?▼

Semantic Kernel supports dynamic prompt generation using powerful templating engines like Handlebars and Liquid. This allows developers to create context-aware prompts by injecting variables and structured data directly into the template.

How to build multi-agent systems for complex task collaboration in .NET?▼

You can build multi-agent systems in .NET using the Semantic Kernel agent framework. This provides the infrastructure to orchestrate multiple AI agents, enabling them to collaborate on sophisticated tasks and share contextual data.

Do I need specific dependencies to use Semantic Kernel with local LLMs like Ollama?▼

To use Semantic Kernel with Ollama for local LLMs, you need to configure the kernel to point to your local Ollama service endpoint. The framework handles the integration, allowing you to run AI features without external API dependencies.

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