semantic-kernel — GitHub Copilot skill: what it does, how to install
This skill helps build and inspect Semantic Kernel applications using current documentation. Use it for projects involving plugins, function calling, or AI integrations.
npx skills add github/awesome-copilot --skill semantic-kernelWhat semantic-kernel does
The skill identifies the project language from its files or the user's request, then follows a separate reference for .NET or Python. If both ecosystems are present, it focuses on the files being edited. It checks implementation advice against current Semantic Kernel documentation and Microsoft samples.
Shared guidance covers async kernel operations, error handling, logging, and function calling. It recommends DefaultAzureCredential when Azure authentication fits the task. This is for projects built with Semantic Kernel; it does not replace the official docs. Install it when you want an agent to create, update, explain, or review this kind of code.
What it helps with
- choose the right Semantic Kernel reference for .NET or Python
- inspect a function-calling flow in an application
- update an AI integration against the current API
- review error handling and logging
Example prompts for the agent
- Inspect this Semantic Kernel project and choose the reference for its language.
- Update this app's function-calling flow using the current Semantic Kernel documentation.
- Explain this Semantic Kernel integration and review its error handling.
How to install semantic-kernel
1Skills CLI (Vercel)
npx skills add github/awesome-copilot --skill semantic-kernel2GitHub CLI
gh skill install github/awesome-copilot semantic-kernel3Manually: put the files in place
.github/skills/semantic-kernel/SKILL.md1Skills CLI (Vercel)
npx skills add github/awesome-copilot --skill semantic-kernel -a claude-code2Manually: put the files in place
~/.claude/skills/semantic-kernel/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/semantic-kernel/
Manually: put the files in place
.agents/skills/semantic-kernel/SKILL.mdFor all projects use ~/.agents/skills/
Manually: put the files in place
.cursor/skills/semantic-kernel/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
Platform CLI
gemini skills install https://github.com/github/awesome-copilot --path skills/semantic-kernelManually: put the files in place
.devin/skills/semantic-kernel/SKILL.mdThe legacy .windsurf/skills/ path still works
Claude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
From the same set554
The whole set →Similar
Questions about semantic-kernel
How do I install semantic-kernel in GitHub Copilot?
Run npx skills add github/awesome-copilot --skill semantic-kernel. You can also use gh skill install github/awesome-copilot semantic-kernel.
What does semantic-kernel do?
It guides an agent through creating, updating, explaining, or reviewing Semantic Kernel solutions. It selects .NET or Python guidance and checks current documentation.
How does the skill choose between .NET and Python?
It checks the repository files or the language named in the request. When both ecosystems are present, it follows the files being edited or the stated target.
Source text
SKILL.md · MIT
--- name: semantic-kernel description: 'Create, update, refactor, explain, or review Semantic Kernel solutions using shared guidance plus language-specific references for .NET and Python.' --- # 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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