technology-selection

Select AI/ML technology stacks for .NET 8+ projects using a decision-tree workflow.

2|Updated Mar 17, 2026

Search Tags:#architecture #ai #llm #dotnet #guardrails #ml #mlnet

Ready to run

npx skills add https://github.com/sayedihashimi/copilot-skill-eval --skill technology-selection-sayedihashimi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: technology-selection
Source: https://github.com/sayedihashimi/copilot-skill-eval/tree/main/examples/aspnet-razor-pages/plugins/dotnet-skills/dotnet-ai/skills/technology-selection
Command: npx skills add https://github.com/sayedihashimi/copilot-skill-eval --skill technology-selection-sayedihashimi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Classify and automate the decision process for selecting the right AI/ML technologies and toolchains for .NET 8+ projects, ensuring scalable, maintainable, and compliant architectures.

Core Features & Use Cases

Identify the appropriate ML or AI stack for a given .NET project (ML.NET, MEAI, MAF, Copilot SDK, ONNX, OllamaSharp) based on task type and constraints.
Provide a decision tree-based workflow to determine when to use ML versus LLM orchestration, local inference versus cloud providers, and how to integrate with vector stores or Copilot extensions.
Real-world use cases include building classification/prediction pipelines, RAG workflows, and agent-assisted development tools in .NET ecosystems.

Quick Start

Install the evaluation framework and follow the decision-tree guide to select the right technologies for your .NET AI/ML project.

Frequently Asked Questions about technology-selection

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I choose the right AI and ML technology stack for a .NET 8 project?▼

Selecting an AI stack for .NET 8 involves evaluating task types and constraints through a structured decision tree, ensuring reproducible architectures across classic ML, LLM orchestration, and local inference integrations.

When should I use ML.NET versus LLM orchestration in my .NET applications?▼

ML.NET suits classic classification and prediction pipelines, while LLM orchestration fits RAG workflows and agent-assisted tools, determined by applying a structured decision-tree workflow to your specific project constraints.

Does the technology selection guide support local inference and Copilot integrations?▼

Yes, the technology selection guide supports local inference and Copilot integrations, providing decision criteria for tools like ONNX, OllamaSharp, and the Copilot SDK across various .NET frameworks.

What is the best way to integrate vector stores into a .NET RAG workflow?▼

The best way to integrate vector stores into a .NET RAG workflow is by following the decision-tree guide, which provides criteria for selecting appropriate AI technologies and tooling to build scalable and maintainable architectures.

What are the guardrails for ensuring compliant .NET AI architectures?▼

Guardrails for compliant .NET AI architectures are enforced through a structured workflow with specific inputs and decision criteria, ensuring scalable, maintainable, and reproducible technology stack selections.