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github/semantic-kernel

github

semantic-kernel

Create, update, refactor, explain, or review Semantic Kernel solutions using shared guidance plus language-specific references for .NET and Python.

v1.0Latest
New~745Updated Jun 26, 2026

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.
Files3
3 files · 2.3 KB

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Overall Score

82/100

Grade

B

Good

Safety

90

Quality

82

Clarity

85

Completeness

78

Summary

This skill provides guidance for creating and maintaining Semantic Kernel applications in .NET (C#) and Python. It directs agents to determine the target language, consult live Microsoft documentation, and apply shared architectural patterns (async operations, plugin patterns, error handling, strong typing) across both ecosystems. The skill delegates language-specific implementation details to two reference files.

Detected Capabilities

documentation readingcode guidance and patternslanguage-specific reference selectionexternal documentation retrieval (Microsoft Docs)repository structure analysisarchitecture and best-practices explanation

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

semantic kernelnet ai integrationpython plugin setupazure openai connectorfunction calling flowsemantic kernel refactor

Risk Signals

INFO

References external Microsoft documentation via https://learn.microsoft.com (authenticated Microsoft Docs endpoint)

SKILL.md: 'Always consult live documentation' section
INFO

Recommends DefaultAzureCredential for Azure authentication

SKILL.md: Shared guidance section
INFO

No file write or shell execution patterns detected

entire skill

Referenced Domains

External domains referenced in skill content, detected by static analysis.

github.comlearn.microsoft.com

Use Cases

  • .NET Semantic Kernel project setup and configuration
  • Python Semantic Kernel implementation guidance
  • Creating plugins and function-calling flows in Semantic Kernel
  • AI service connector setup (Azure OpenAI, Azure AI Foundry)
  • Refactoring or reviewing existing Semantic Kernel solutions
  • Authentication and credential management using DefaultAzureCredential

Quality Notes

  • Clear language-selection logic using file extension heuristics (.cs, .py, .csproj, pyproject.toml)
  • Explicitly directs agent to consult live documentation rather than relying on training data, reducing stale guidance
  • Well-structured workflow with numbered steps for deterministic decision-making
  • Shared guidance emphasizes modern best practices (async patterns, strong typing, error handling)
  • Reference files are present and include authoritative source URLs for both ecosystems
  • Completion criteria are testable and tied to the core workflow
  • Appropriate emphasis on using official samples over memory-based assumptions
  • Minor: reference files are concise (623-675 B each) but lack depth on common patterns like memory management, custom plugins, orchestration; author could expand with more detailed examples or edge cases
Model: claude-haiku-4-5-20251001Analyzed: Jun 26, 2026

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