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dotnet-semantic-kernel

Integrates LLM-powered workflows and manages semantic kernel sessions for .NET applications.

71|10|Updated Feb 11, 2026
Search Tags:#ai#llm#rag#agents#plugins#.net#semantic kernel
One-click install
npx skills add https://github.com/wshaddix/dotnet-skills --skill dotnet-semantic-kernel-wshaddix
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dotnet-semantic-kernel
Source: https://github.com/wshaddix/dotnet-skills/tree/main/skills/dotnet-semantic-kernel
Command: npx skills add https://github.com/wshaddix/dotnet-skills --skill dotnet-semantic-kernel-wshaddix

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes 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 without deep AI expertise.

Core Features & Use Cases

  • Semantic Kernel Setup: Configure and manage the core Semantic Kernel object for AI service connections.
  • Plugin Development: Define and register .NET methods as AI-callable functions.
  • Prompt Templating: Utilize Handlebars and YAML for dynamic and structured prompt creation.
  • Memory & RAG: Integrate vector stores for retrieval-augmented generation.
  • Agent Framework: Build multi-agent systems for complex task orchestration.
  • Use Case: Develop a .NET application that can understand customer support queries, retrieve relevant information from a knowledge base using RAG, and generate a helpful response, potentially by invoking specialized plugins.

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 LLM features into a .NET application?▼

You integrate LLM features in .NET by configuring the core Semantic Kernel object for AI service connections, enabling intelligent capabilities without requiring deep AI expertise.

Can I use Semantic Kernel with local models via Ollama?▼

Yes, Semantic Kernel supports local models via Ollama. You can configure the kernel to connect to local models alongside Azure OpenAI and OpenAI for your AI service connections.

How do I build a retrieval-augmented generation pipeline in .NET?▼

Build a RAG pipeline in .NET by integrating vector store connectors like Qdrant or Azure AI Search with Semantic Kernel. This retrieves relevant information from a knowledge base to generate context-aware responses.

What is the best way to define AI-callable functions in .NET?▼

The best way to define AI-callable functions is through Semantic Kernel plugin development. You register .NET methods as plugins, allowing the AI to invoke specialized functions during task orchestration.

Does Semantic Kernel support multi-agent systems for complex tasks?▼

Yes, Semantic Kernel supports multi-agent systems through its experimental agents framework. This allows you to build and orchestrate complex task workflows by configuring multiple specialized agents.

How do I create dynamic prompts using Handlebars and YAML in .NET?▼

Create dynamic prompts in .NET using Semantic Kernel's prompt templating features. This supports Handlebars, Liquid, and YAML formats to structure and generate prompts for your AI models dynamically.

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