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semantic-kernel

Lets your agent create, modify, and explain solutions using Semantic Kernel. It supports .NET and Python, following best practices.

What it does

Helps you create, update, review, or explain code for Semantic Kernel, a tool for building AI applications. It ensures instructions follow best practices and official documentation.

When to use it

  • ✓When starting a new Semantic Kernel project.
  • ✓When you need help understanding or fixing existing code.
  • ✓When integrating different parts of an AI application.

Example of what to ask your AI

“How do I use Semantic Kernel in Python to create a plugin?”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/github/awesome-copilot/blob/main/skills/semantic-kernel/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/github/awesome-copilot/blob/main/skills/semantic-kernel/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The AI first identifies if you are using .NET or Python. Then, it fetches the latest information from the official documentation and applies general and language-specific best practices to give you the best answer.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add github/awesome-copilot --skill semantic-kernel

Or download just the file:

curl -L https://raw.githubusercontent.com/github/awesome-copilot/main/skills/semantic-kernel/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Semantic Kernel

Use this skill when working with applications, plugins, function-calling flows, or AI integrations built on Semantic Kernel.

Always ground implementation advice in the latest Semantic Kernel documentation and samples rather than memory alone.

Determine the target language first

Choose the language workflow before making recommendations or code changes:

  1. Use the .NET workflow when the repository contains .cs, .csproj, .sln, or other .NET project files, or when the user explicitly asks for C# or .NET guidance. Follow references/dotnet.md.
  2. Use the Python workflow when the repository contains .py, pyproject.toml, requirements.txt, or the user explicitly asks for Python guidance. Follow references/python.md.
  3. If the repository contains both ecosystems, match the language used by the files being edited or the user's stated target.
  4. If the language is ambiguous, inspect the current workspace first and then choose the closest language-specific reference.

Always consult live documentation

Shared guidance

When working with Semantic Kernel in any language:

  • Use async patterns for kernel operations.
  • Follow official plugin and function-calling patterns.
  • Implement explicit error handling and logging.
  • Prefer strong typing, clear abstractions, and maintainable composition patterns.
  • Use built-in connectors for Azure AI Foundry, Azure OpenAI, OpenAI, and other AI services, while preferring Azure AI Foundry services for new projects when that fits the task.
  • Use the kernel's memory and context-management capabilities when they simplify the solution.
  • Use DefaultAzureCredential when Azure authentication is appropriate.

Workflow

  1. Determine the target language and read the matching reference file.
  2. Fetch the latest official docs and samples before making implementation choices.
  3. Apply the shared Semantic Kernel guidance from this skill.
  4. Use the language-specific package, repository, sample paths, and coding practices from the chosen reference.
  5. When examples in the repo differ from current docs, explain the difference and follow the current supported pattern.

References

Completion criteria

  • Recommendations match the target language.
  • Package names, repository paths, and sample locations match the selected ecosystem.
  • Guidance reflects current Semantic Kernel documentation rather than stale assumptions.
FAQ

What is Semantic Kernel?

It's a library that helps developers build AI applications by connecting large language models (like those from OpenAI) with traditional code.

What are 'plugins' and 'function-calling'?

Plugins are like extensions you can add to Semantic Kernel for it to perform specific tasks. 'Function-calling' is the AI model's ability to call these functions (plugins) to get information or perform actions.

Why is it important to use the official documentation?

The official documentation is always up-to-date with the latest features and best ways to use the tool, ensuring your code works correctly and securely.

Details

Original description by the author

Create, update, refactor, explain, or review Semantic Kernel solutions using shared guidance plus language-specific references for .NET and Python.

repo: github/awesome-copilot

Popularity

GitHub stars
40k
Ranking position
#5364
License
MIT
Updated
10/15/2026

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