Agent skill · software engineering · github
semantic-kernel
Create, update, refactor, explain, or review Semantic Kernel solutions using shared guidance plus language-specific references for .NET and Python.
Why this skill is useful
Provides specific guidance on implementing Semantic Kernel solutions in .NET and Python, including async patterns and error handling that the AI wouldn't reliably generate on its own.
What it needs
About 2k tokens when loaded. Last updated 2026-08-07. 37,534 stars on the source repository.
What this skill does
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 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills github/semantic-kernel