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by githubgithub/awesome-copilot40k stars
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Create, update, refactor, explain, or review Semantic Kernel solutions using shared guidance plus language-specific references for .NET and Python.

Use this Skill: https://skilld.dev/gh/github/awesome-copilot/semantic-kernel

This session only. Nothing lands on disk.

SKILL.md

≈41 tokens always: the name and description. ≈708 when used: this file. ≈325 more on demand in 2 files.

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.

Source: SKILL.md on GitHub

1 warning16d5 checks · Risk SAFE
  • Gen Agent Trust Hub16d

    The skill provides guidelines and reference workflows for developing application logic using the Semantic Kernel framework in .NET and Python. No security risks or malicious behaviors were found.

  • Socket16d

    No alerts

  • Snyk16d

    Risk: LOW · No issues

  • Runlayer6mo

    3 files scanned · No issues

  • ZeroLeaks5mo

    1 finding · Score: 69/100

Signed by skilld at 9272be1. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 7 months ago
  • Python
  • semantic-kernel
  • dotnet
  • ai-integration
  • function-calling
  • plugins
  • azure-openai
  • llm

README badge

README badge for github/awesome-copilot/semantic-kernel

Guides creation and refactoring of Semantic Kernel applications in .NET or Python, grounding recommendations in current official documentation and language-specific best practices. Covers plugin development, function-calling flows, and AI service integration for both ecosystems.

Generated from the current SKILL.md.

Does this skill support both .NET and Python?
Yes. The skill includes language-specific references for .NET and Python. It determines the target language first by inspecting project files (.cs, .csproj for .NET; .py, pyproject.toml for Python) or explicit user requests, then applies language-appropriate guidance.
Does this skill rely on cached knowledge or live documentation?
It prioritizes live documentation. The skill directs you to consult the official Semantic Kernel documentation and samples rather than relying on memory alone, and uses Microsoft Docs MCP tooling when available to fetch current API surface and examples.
What AI services does this skill recommend?
The skill covers built-in connectors for Azure AI Foundry, Azure OpenAI, OpenAI, and other AI services, with a preference for Azure AI Foundry services in new projects when appropriate.
Does this skill handle plugin and function-calling patterns?
Yes. It covers creation, updates, refactoring, and review of Semantic Kernel solutions including plugins, function-calling flows, and AI integrations, following official plugin and function-calling patterns.

Generated from the current SKILL.md. These answers refresh after source changes.