technology-selection

仓库创建 2026年2月4日最近提交 15 小时前SkillHot 收录 20 天前
▸ 精选理由

系统性比较多种 AI/ML 方案,适合架构决策和方案落地评估。

这个 Skill 做什么

在 .NET 8+ 环境下指导 AI/ML 技术与框架的选型与实现方案。

在 .NET 8+ 项目里帮你选型并给出落地实现建议,涉及 ML.NET、Microsoft.Extensions.AI、MAF、GitHub Copilot SDK、ONNX Runtime、OllamaSharp 等工具链。适合要做分类、回归、聚类、异常检测、推荐、LLM 集成、RAG 向量检索或本地推理部署的场景。会把模型格式、推理库、部署约束和工程化考量一起权衡,给出可执行的技术路线而不是抽象建议。

▸ 展开 SKILL.md 英文原文

Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

Skill 开发管理技术选型.NETAI/ML通用
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# .NET AI and Machine Learning

## Inputs

| Input | Required | Description |
|-------|----------|-------------|
| Task description | Yes | What the AI/ML feature should accomplish (e.g., "classify support tickets", "summarize documents") |
| Data description | Yes | Type and shape of input data (structured/tabular, unstructured text, images, mixed) |
| Deployment constraints | No | Cloud vs. local, latency SLO, cost budget, offline requirements |
| Existing project context | No | Current .csproj, existing packages, target framework |

## Workflow

### Step 1: Classify the task using the decision tree

Evaluate the developer's task against this decision tree and select the appropriate techno
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