Semantic Kernel

SkillDev tools

Once added, your AI can create, update, refactor, explain, and review Semantic Kernel code, which developers use to add AI features to applications. It supports both .NET and Python, drawing on shared guidance plus language-specific references for each. You get help building or improving a Semantic Kernel solution without needing to be an expert in it yourself.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add the skill, then ask your AI to create a new Semantic Kernel solution or work on one you already have in .NET or Python. You can also ask it to explain or review code you already have.

Then ask your AI: use the Semantic Kernel skill

What your AI can do with it

  • Create new Semantic Kernel solutions in .NET or Python
  • Update existing Semantic Kernel code
  • Refactor Semantic Kernel code
  • Explain how a Semantic Kernel solution works
  • Review Semantic Kernel code
  • Apply language-specific references for .NET and Python

What this skill tells your AI

The instructions your AI receives, as published by github/awesome-copilot in skills/semantic-kernel/SKILL.md and read by ahel’s review.

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.

Signals

GitHub stars
40k
Forks
5k
Last commit
Oct 2026
Advanced
Item type
skill
Key
semantic-kernel
Source
github.com/github/awesome-copilot