Dotnet ai
AI and ML skills for .NET: technology selection, LLM integration, agentic workflows, RAG pipelines, MCP, and classic ML with ML.NET.From its SKILL.md
npx -y skills add autohandai/community-skills --skill dotnet-aiAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 9 stars9 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its file declares
Copied from the file, not written here
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
1.3 KB, 211 tokens by cl100k_base, as published. Nobody here has run it
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.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.