Define and Invoke Kernel Functions
Semantic Kernel Functions demonstrates defining and invoking native functions, prompt functions, and plugins with kernel arguments for extensible AI assistants.
Why it matters
Leverage Semantic Kernel to define and invoke both native and prompt-based functions. This asset enables the creation of sophisticated agents and applications by managing function execution and arguments.
Outcomes
What it gets done
Define native functions for direct code execution.
Create prompt functions for LLM-driven tasks.
Manage kernel arguments for function invocation.
Build agent frameworks using function composition.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/sk-concept-functions | bash Overview
Semantic Kernel - Functions
This skill demonstrates how to define and invoke kernel functions in Microsoft's Semantic Kernel framework using Python. It includes examples for creating native functions, prompt functions, and plugins, along with techniques for handling kernel arguments. The two sample files cover agent framework tools and kernel argument patterns. Use this when you need to learn how to structure reusable functions for Semantic Kernel or extend AI assistants with custom capabilities. It is ideal for developers building plugins, integrating agent tools, or designing function-calling patterns in Semantic Kernel applications.
What it does
This skill provides working examples for defining and invoking kernel functions within Microsoft's Semantic Kernel framework. It demonstrates how to create native functions, prompt functions, and plugins, along with techniques for passing kernel arguments to extend AI assistant capabilities.
When to use - and when NOT to
Use this skill when you need to understand how to structure reusable functions for Semantic Kernel, integrate agent framework tools, or pass arguments between kernel components. It is ideal when building custom plugins or extending existing AI assistants with domain-specific capabilities.
Do not use this if you need production-ready implementations rather than learning examples, or if you are working with AI frameworks other than Semantic Kernel.
Inputs and outputs
Users provide Python code that defines functions following Semantic Kernel conventions. The examples show how to structure agent_framework_tools.py for agent tooling and kernel_arguments.py for argument handling. The output is executable sample code demonstrating function invocation patterns and plugin architecture within the Semantic Kernel ecosystem.
Who it's for
This skill serves Python developers building AI applications with Semantic Kernel who need to understand function definition patterns. It is particularly valuable for engineers creating custom plugins, integrating agent frameworks, or designing reusable AI capabilities. Developers familiar with function-calling patterns in LLM applications will find these examples directly applicable to extending Semantic Kernel assistants with specialized tools and behaviors.
Source README
Defining and invoking kernel functions: native functions, prompt functions, plugins
Examples (2 files):
agent_framework_tools.pykernel_arguments.py
FAQ
Common questions
Discussion
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