S

semantic-kernel

by @githubv
4.3(21)

Build applications, plugins, function call flows, and AI integrations based on Semantic Kernel, providing implementation advice and best practices.

semantic-kernelmicrosoftai-integrationpluginsfunction-callingGitHub
Installation
npx skills add github/awesome-copilot --skill semantic-kernel
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Before / After Comparison

1
Before

Manually implementing AI features and plugin integration requires a deep understanding of underlying frameworks and results in a long development cycle.

After

Rapidly build AI applications based on Semantic Kernel, leveraging its rich plugin ecosystem and best practices.

SKILL.md

semantic-kernel

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:

  • 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.

  • 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.

  • If the repository contains both ecosystems, match the language used by the files being edited or the user's stated target.

  • 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

  • Determine the target language and read the matching reference file.

  • Fetch the latest official docs and samples before making implementation choices.

  • Apply the shared Semantic Kernel guidance from this skill.

  • Use the language-specific package, repository, sample paths, and coding practices from the chosen reference.

  • 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.

Weekly Installs227Repositorygithub/awesome-copilotGitHub Stars26.2KFirst Seen5 days agoSecurity AuditsGen Agent Trust HubPassSocketPassSnykPassInstalled ongemini-cli206codex203github-copilot195opencode195cursor194kimi-cli193

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Statistics

Installs1.4K
Rating4.3 / 5.0
Version
Updated2026年5月17日
Comparisons1

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4.3(21)
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Compatible Platforms

🔧Claude Code

Timeline

Created2026年3月22日
Last Updated2026年5月17日