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* Removing a codex-specific agent (model deprecated) and removing model from blueprint mode * Combining skills into a single skill with an internal decision tree * Converting agents to skill with decision tree Closes #998 * Converting agents to skill with decision tree" Fixes #999
57 lines
3.0 KiB
Markdown
57 lines
3.0 KiB
Markdown
---
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name: semantic-kernel
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description: 'Create, update, refactor, explain, or review Semantic Kernel solutions using shared guidance plus language-specific references for .NET and Python.'
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---
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# Semantic Kernel
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Use this skill when working with applications, plugins, function-calling flows, or AI integrations built on Semantic Kernel.
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Always ground implementation advice in the latest Semantic Kernel documentation and samples rather than memory alone.
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## Determine the target language first
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Choose the language workflow before making recommendations or code changes:
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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).
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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).
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3. If the repository contains both ecosystems, match the language used by the files being edited or the user's stated target.
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4. If the language is ambiguous, inspect the current workspace first and then choose the closest language-specific reference.
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## Always consult live documentation
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- Read the Semantic Kernel overview first: <https://learn.microsoft.com/semantic-kernel/overview/>
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- Prefer official docs and samples for the current API surface.
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- Use the Microsoft Docs MCP tooling when available to fetch up-to-date framework guidance and examples.
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## Shared guidance
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When working with Semantic Kernel in any language:
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- Use async patterns for kernel operations.
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- Follow official plugin and function-calling patterns.
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- Implement explicit error handling and logging.
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- Prefer strong typing, clear abstractions, and maintainable composition patterns.
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- 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.
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- Use the kernel's memory and context-management capabilities when they simplify the solution.
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- Use `DefaultAzureCredential` when Azure authentication is appropriate.
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## Workflow
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1. Determine the target language and read the matching reference file.
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2. Fetch the latest official docs and samples before making implementation choices.
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3. Apply the shared Semantic Kernel guidance from this skill.
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4. Use the language-specific package, repository, sample paths, and coding practices from the chosen reference.
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5. When examples in the repo differ from current docs, explain the difference and follow the current supported pattern.
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## References
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- [.NET reference](references/dotnet.md)
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- [Python reference](references/python.md)
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## Completion criteria
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- Recommendations match the target language.
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- Package names, repository paths, and sample locations match the selected ecosystem.
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- Guidance reflects current Semantic Kernel documentation rather than stale assumptions.
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