MR
Mayur Rathi
@github
⭐ 34.1k GitHub stars

Semantic-Kernel

Semantic-Kernel是一款code方向的AI技能,核心价值是Create, update, refactor, explain, or review Semantic Kernel solutions using shared guidance plus language-specific references for ,可用于解决开发者在code领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。

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

Last verified on: 2026-05-30
mkdir -p ./skills/semantic-kernel && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/semantic-kernel/SKILL.md -o ./skills/semantic-kernel/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# 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.

🎯 Best For

  • Engineering teams doing code reviews
  • Open source maintainers
  • Tech leads planning refactors
  • Developers modernizing legacy code
  • UI designers

💡 Use Cases

  • Reviewing pull requests for security vulnerabilities
  • Checking code style consistency
  • Migrating from class components to hooks
  • Breaking apart monolithic functions

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Semantic-Kernel to Your Work

    Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.

  4. 4

    Review and Refine

    Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.

❓ Frequently Asked Questions

Does this skill check for OWASP Top 10?

Security-focused review skills often include OWASP checks. Check the skill content for specific vulnerability categories covered.

Does this handle breaking changes?

Refactoring skills identify breaking changes but always run your test suite after applying suggestions.

Does this work with Figma?

Some design skills integrate with Figma plugins. Check the Works With section for supported tools.

Is Semantic-Kernel compatible with Cursor and VS Code?

Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.

Do I need specific dependencies for Semantic-Kernel?

Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.

⚠️ Common Mistakes to Avoid

Blindly accepting AI suggestions

Always verify AI-generated review comments. Some suggestions may not apply to your specific codebase conventions.

Refactoring without tests

Never refactor critical paths without a comprehensive test suite to catch regressions.

Skipping usability testing

AI-generated designs should be validated with real users before development.

Skipping validation

Always test AI-generated code changes, even for simple refactors.

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