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semantic-kernel

Develop AI applications with Microsoft's Semantic Kernel SDK.

3|1|Updated Feb 2, 2026
Search Tags:#ai development#plugin#copilot#planner#microsoft#azure ai#semantic kernel
One-click install
npx skills add https://github.com/HouseGarofalo/claude-code-base --skill semantic-kernel-housegarofalo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: semantic-kernel
Source: https://github.com/HouseGarofalo/claude-code-base/tree/main/.claude/skills/semantic-kernel
Command: npx skills add https://github.com/HouseGarofalo/claude-code-base --skill semantic-kernel-housegarofalo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the development of AI applications by providing tools and guidance for integrating Microsoft's Semantic Kernel SDK, enabling the creation of complex AI functionalities.

Core Features & Use Cases

  • Plugin Development: Create and manage semantic and native functions for modular AI.
  • Planner Integration: Utilize planners to orchestrate complex AI tasks and workflows.
  • Memory Systems: Implement semantic and vector memory for AI context and data retrieval.
  • Use Case: Develop a sophisticated AI assistant that can research topics, summarize findings, and interact with users through a chat interface, all powered by Semantic Kernel.

Quick Start

Use the semantic_kernel skill to add a chat completion service to your kernel.

Frequently Asked Questions about semantic-kernel

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

FAQPage Schema
How do I build an AI copilot using Microsoft Semantic Kernel?▼

You can build an AI copilot using Semantic Kernel by creating plugins with semantic and native functions, then utilizing planners to orchestrate complex workflows and memory systems for context retrieval. This SDK simplifies integrating enterprise AI functionalities.

What is the best way to orchestrate complex AI tasks in a .NET application?▼

The best way to orchestrate complex AI tasks in .NET is using Semantic Kernel's planner integration. Planners coordinate modular semantic and native functions to execute sophisticated workflows and automate multi-step AI processes seamlessly.

Can I use Semantic Kernel with Python for enterprise AI development?▼

Yes, Semantic Kernel supports both Python and .NET installations for enterprise AI development. You can implement semantic and vector memory, develop plugins, and integrate chat completion services within your Python environment.

How do I implement semantic memory for AI context retrieval in Azure AI?▼

To implement semantic memory for AI context retrieval in Azure AI, use Semantic Kernel's memory systems. This facilitates vector data storage and retrieval, providing persistent context for your AI assistant and enhancing interaction accuracy.

Does Semantic Kernel support adding a chat completion service to an existing kernel?▼

Yes, Semantic Kernel supports adding a chat completion service to an existing kernel. You can quickly integrate this functionality to enable your AI assistant to interact with users through a responsive chat interface.

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