semantic-kernel

github/awesome-copilot · updated Apr 8, 2026

$npx skills add https://github.com/github/awesome-copilot --skill semantic-kernel
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summary

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

skill.md

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.

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.671 reviews
  • Ishan Farah· Dec 24, 2024

    Solid pick for teams standardizing on skills: semantic-kernel is focused, and the summary matches what you get after install.

  • Alexander Agarwal· Dec 24, 2024

    semantic-kernel reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Chinedu Zhang· Dec 24, 2024

    Useful defaults in semantic-kernel — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Min Huang· Dec 20, 2024

    semantic-kernel has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Kabir Malhotra· Dec 20, 2024

    semantic-kernel fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Li Harris· Dec 8, 2024

    We added semantic-kernel from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Li Dixit· Dec 4, 2024

    Registry listing for semantic-kernel matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Kabir Khanna· Nov 27, 2024

    Useful defaults in semantic-kernel — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Charlotte Torres· Nov 23, 2024

    semantic-kernel reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Yash Thakker· Nov 15, 2024

    semantic-kernel fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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