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semantic-kernel

面向 Semantic Kernel 应用、插件和函数调用流程,先判断 .NET 或 Python 目标语言,再结合对应参考与最新文档给出实现、重构、解释或审查建议,并强调异步、错误处理、类型安全和认证实践。

github
热度 9.92
最近更新 2026-10-20 13:19:16
分类:开发依赖:读写文件 / 联网浏览搜索 / MCP 服务应用场景:代码生成 / 代码审查 / API 集成 / 概念解释

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add github/awesome-copilot/semantic-kernel
Metadata
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.
  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.