brainstorming
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or planning anything new, in software or out of it (a talk, a business, a renovation).
Skills 둘러보기
개발 및 엔지니어링 · SKILL.md
dotnet-ai
dotnet-aiAI and ML skills for .NET: technology selection, LLM integration, agentic workflows, RAG pipelines, MCP, and classic ML with ML.NET.
Independent GitHub snapshot · Automated classification · Original documentation governs
Skill 문서
GitHubname: 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.