semantic-kernel

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

25 stars

Best use case

semantic-kernel is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using semantic-kernel should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/semantic-kernel/SKILL.md --create-dirs "https://raw.githubusercontent.com/ComeOnOliver/skillshub/main/skills/github/awesome-copilot/semantic-kernel/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/semantic-kernel/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How semantic-kernel Compares

Feature / Agentsemantic-kernelStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# 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](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](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

- Read the Semantic Kernel overview first: <https://learn.microsoft.com/semantic-kernel/overview/>
- Prefer official docs and samples for the current API surface.
- Use the Microsoft Docs MCP tooling when available to fetch up-to-date framework guidance and examples.

## 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

- [.NET reference](references/dotnet.md)
- [Python reference](references/python.md)

## 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.

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