| name | dotnet-ai |
| description | AI and ML skills for .NET: technology selection, LLM integration, agentic workflows, RAG pipelines, MCP, and classic ML with ML.NET. |
| license | MIT |
| metadata | {"author":"dotnet","version":"1.0.0","source":"https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai"} |
dotnet-ai
Overview
Use this skill for AI and ML work in .NET, including LLM integration, agentic workflows, RAG pipelines, MCP servers or clients, embeddings, and ML.NET systems using dotnet/skills.
Workflow
- Clarify the AI task, data flow, model/provider, latency, privacy, and evaluation constraints.
- Choose .NET libraries and architecture that fit the repository's hosting model and deployment target.
- Keep prompt, retrieval, tool, and model contracts explicit and testable.
- Validate with unit tests, integration tests, evals, or deterministic smoke checks where possible.
Guardrails
- Do not hard-code secrets, model credentials, or provider-specific assumptions.
- Keep user data, embeddings, logs, and prompts privacy-aware.
- Prefer measurable quality and latency criteria over vague AI behavior claims.
Expected Output
Return a .NET AI implementation or plan with architecture, provider assumptions, and verification.
