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Semantic Kernel

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Streamline your Semantic Kernel development with targeted guidance.

by github37.7k stars on github/awesome-copilot
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Updated Aug 10, 2026
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What Semantic Kernel does

The Semantic Kernel skill is designed for developers working with applications, plugins, or AI integrations based on the Semantic Kernel framework. This skill provides structured guidance to create, update, refactor, explain, or review solutions, ensuring that all advice is grounded in the latest documentation and best practices. By utilizing this skill, developers can navigate the complexities of Semantic Kernel more effectively, leading to better implementation outcomes.

When using this skill, the first step is to determine the target programming language, either .NET or Python. This is crucial as it dictates the workflow and the specific guidance that will be provided. The skill includes references for both languages, allowing users to follow appropriate coding patterns and practices. Developers are encouraged to consult live documentation to ensure that they are using the most current information and examples, which is essential for maintaining compatibility with the evolving Semantic Kernel framework.

The skill emphasizes shared guidance applicable across both languages, such as the use of async patterns, error handling, and strong typing. It also highlights the importance of utilizing built-in connectors for Azure services, promoting best practices for new projects. By following the structured workflow outlined in the skill, developers can ensure that their implementations align with the latest standards and practices, ultimately leading to more maintainable and robust solutions.

Overall, the Semantic Kernel skill is a valuable resource for developers who want to enhance their productivity and effectiveness when working with Semantic Kernel projects. It provides a clear path for leveraging the framework's capabilities while adhering to best practices in software development.

When to use it

Use this skill when developing applications or plugins that integrate with Semantic Kernel, especially in .NET or Python environments.

When not to use it

This skill may not be suitable for projects outside the Semantic Kernel framework or for languages other than .NET and Python.

What you can build with it

Developing a .NET Plugin

Use this skill to get specific guidance on creating a plugin for a .NET application using Semantic Kernel.

Refactoring Python Code

Leverage this skill to receive recommendations for refactoring existing Python code that utilizes Semantic Kernel.

Integrating AI Services

Utilize this skill to understand how to effectively integrate Azure AI services with your Semantic Kernel application.

How to install Semantic Kernel

View source

1. Install with the skills CLI

npx skills add github/awesome-copilot/semantic-kernel --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by 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.

Frequently asked questions about Semantic Kernel

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