Re AI Model
dslsdzc/rev-skills
AI 模型文件逆向与静态分析：ONNX/PyTorch/Safetensors/TFLite 格式解析、 网络结构还原、权重提取、文件级水印分析（权重 pattern/metadata/tensor hash/embedding 异常）。
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…
Install Technology Selection
$ npx skills add dotnet/skills --skill technology-selection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install dotnet/skills technology-selection --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/dotnet/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/dotnet-ai/skills/technology-selection .claude/skills/technology-selection && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "technology-selection" agent skill from https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection into .claude/skills/technology-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "technology-selection", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selectionType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add dotnet/skills --skill technology-selection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install dotnet/skills technology-selection --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dotnet/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/dotnet-ai/skills/technology-selection .agents/skills/technology-selection && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "technology-selection" agent skill from https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection into .agents/skills/technology-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "technology-selection", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add dotnet/skills --skill technology-selection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install dotnet/skills technology-selection --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dotnet/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/dotnet-ai/skills/technology-selection .cursor/skills/technology-selection && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "technology-selection" agent skill from https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection into .cursor/skills/technology-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "technology-selection", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/dotnet/skills.git --path plugins/dotnet-ai/skills/technology-selection--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add dotnet/skills --skill technology-selection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install dotnet/skills technology-selection --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dotnet/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/dotnet-ai/skills/technology-selection .gemini/skills/technology-selection && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "technology-selection" agent skill from https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection into .gemini/skills/technology-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "technology-selection", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install dotnet/skills technology-selectionInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add dotnet/skills --skill technology-selection -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/dotnet/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/dotnet-ai/skills/technology-selection .github/skills/technology-selection && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "technology-selection" agent skill from https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection into .github/skills/technology-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "technology-selection", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add dotnet/skills --skill technology-selection -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install dotnet/skills technology-selection --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dotnet/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/dotnet-ai/skills/technology-selection .opencode/skills/technology-selection && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "technology-selection" agent skill from https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection into .opencode/skills/technology-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "technology-selection", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
technology-selection- GitHub stars
- 5.6k
- Used in
- 2 other repos
- Token cost
- ~2.1k tokens
- SKILL.md length
- 826 words
- Files
- 8 (incl. references)
- Skills in repo
- 91
- Repo updated
- First seen
- Licence
- MIT
At a glance
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…
- Works in 2 steps: Classify the task (decision tree) → Cover the branch essentials, then decide…
- Adding classification
- SKILL.md covers Step 1: Classify the task…, Step 1b: Pick the library layer, Step 2: Cover the branch… and Validation, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Technology Selection is an agent skill from dotnet/skills, published by the product's own GitHub organization. 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…
Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `references/agentic.md`, `references/classic-ml.md` and `references/copilot.md`).
It sits in AI & LLM Engineering, covering Deep learning, Data pipelines and ETL and Vector databases. It works with .NET, ONNX, Python and PyTorch. The repository describes itself as: Repository for skills to assist AI coding agents with .NET and C. The licence is MIT.
When your agent uses it
- Adding classification
- Anomaly detection
- LLM integration (text generation
- RAG pipelines with vector search
Example prompts
- “Use the technology-selection skill to guide technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET…”
- “/technology-selection”
Requirements
- Python 3
Workflow steps
2 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 8d670fa. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
ollama.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Technology Selection loads about 2.1k tokens when it runs, and up to ~5k if it reads all its reference files. Until then it costs about 228 tokens; SKILL.md has 826 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from dotnet/skills at commit 8d670fa, republished under its MIT licence (© dotnet). 826 words, ~2,116 tokens.
Download SKILL.mdSave it as
.claude/skills/technology-selection/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.- name
- technology-selection
- description
- 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).
- license
- MIT
.NET AI and Machine Learning
Pick the right technology first, then deliver only what the task asks for. If the task asks for a plan, comparison, or architecture (or says "do not write code"), produce that — do not scaffold, build, or run code unprompted.
Step 1: Classify the task (decision tree)
State which branch applies and why, then choose that technology.
| Task type | Technology | Why |
|---|---|---|
| Structured/tabular: classification, regression, clustering, anomaly detection, recommendation | ML.NET (Microsoft.ML) | Deterministic (fixed seed), no cloud dependency, purpose-built |
| NL understanding, generation, summarization, reasoning (single prompt → response, no tools) | LLM via Microsoft.Extensions.AI (IChatClient) | Language capability, no orchestration needed |
| Agentic: multi-step tool/function calling, agent loops, multi-agent | Microsoft Agent Framework (Microsoft.Agents.AI) on Microsoft.Extensions.AI | Needs orchestration, tool dispatch, iteration control IChatClient lacks |
| GitHub Copilot extensions / custom dev-workflow agents | GitHub Copilot SDK (GitHub.Copilot.SDK) | Integrates with the Copilot agent runtime |
| Run a pre-trained/custom model in production | ONNX Runtime (Microsoft.ML.OnnxRuntime) | Hardware-accelerated, format-agnostic inference |
| Local/offline LLM inference | OllamaSharp (Ollama models) | Privacy-sensitive, air-gapped, cost-constrained |
| Semantic search, RAG, embedding storage | Microsoft.Extensions.VectorData.Abstractions (MEVD) + a provider (Azure AI Search, Milvus, MongoDB, pgvector, Pinecone, Qdrant, Redis, SQL) | Provider-agnostic vector search |
| Ingest, chunk, load documents into a vector store | Microsoft.Extensions.AI.DataIngestion (preview) + MEVD | Parses, chunks, embeds, upserts |
| Both structured predictions AND NL reasoning | Hybrid: ML.NET scoring + LLM reasoning layer | ML.NET is reproducible; LLM adds explanation |
Critical rule: Do NOT use an LLM for tasks ML.NET handles well (tabular classification, regression, clustering) — LLMs are slower, costlier, and non-deterministic for these.
Step 1b: Pick the library layer
| Layer | Library | Use when |
|---|---|---|
| Abstraction | Microsoft.Extensions.AI (MEAI) | Always the foundation. Use IChatClient directly for prompt-response and simple, bounded function invocation. |
| Provider SDK | Azure.AI.OpenAI / OpenAI / Azure.AI.Inference / OllamaSharp | Concrete provider behind MEAI via AddChatClient. |
| Orchestration | Microsoft.Agents.AI (prerelease) | Multi-step tool use, durable agent loops, and multi-agent workflows. |
| Copilot | GitHub.Copilot.SDK | Building Copilot-platform extensions only. |
Rules: start with MEAI; put the provider behind it via AddChatClient (don't call the provider in
business logic); use Microsoft.Agents.AI for multi-step or durable agent workflows rather than
hand-rolling an agent loop; never mix a raw HttpClient-to-OpenAI call with MEAI in the same
workflow. Do not use Accord.NET (archived). For new projects, prefer MEAI and Agent Framework
unless existing Semantic Kernel features or investments are a requirement. Register AI/ML services
via DI; load secrets from user-secrets / env / Key Vault — never hardcode keys.
Show full SKILL.md (451 more words)Show less
Step 2: Cover the branch essentials, then decide depth
Every answer — plan or implementation — must address the guardrails for the selected branch:
- ML.NET —
new MLContext(seed: …)(reproducible);TrainTestSplit+ evaluate on the held-out set; report real metrics (MicroAccuracy/MacroAccuracy/LogLoss, AUC/F1, or RMSE/R²); serve withPredictionEnginePool<TIn,TOut>(never a singletonPredictionEngine). - LLM (MEAI) — depend on
IChatClientregistered viaAddChatClient(provider behind it); setTemperatureandMaxOutputTokensinChatOptions; add retry/timeout (RetryingChatClient/Polly); pin a dated model; load keys from user-secrets / env / Key Vault — never hardcode ansk-…key; validate non-deterministic output against a schema with a fallback. - Agentic (Agent Framework) — orchestrate with
Microsoft.Agents.AIonIChatClient(never a hand-rolled loop); setMaximumIterationsand a token/cost ceiling; define each tool with a clear schema (AIFunctionFactory.Create); log each step (never raw sensitive content). - RAG / embeddings — semantic chunking (not fixed-size);
IEmbeddingGeneratorand cache the embeddings (don't re-embed per query); store/query withMicrosoft.Extensions.VectorData.Abstractions(MEVD) + the provider the user asked for (e.g. pgvector); filter by a minimum similarity score; keep source attribution for each answer. Honor the UI/storage the user specified; use only real, existing NuGet packages.
Then choose depth:
- Plan / comparison / architecture only (or "do not write code"): answer from this file alone using the essentials above. Do NOT open a reference — the branch essentials here are sufficient for a selection or plan. For RAG plans, cover chat, ingestion/chunking, embeddings, vector storage, source attribution, and the requested UI/storage.
- Writing implementation code: read the matching reference(s) for packages and implementation
guidance (read only the selected branch; for Hybrid, read both Classic ML.NET and LLM):
- Classic ML.NET →
references/classic-ml.md - LLM integration (MEAI) →
references/llm.md - Agentic (Agent Framework) →
references/agentic.md - RAG / embeddings / ingestion →
references/rag.md - GitHub Copilot extensions →
references/copilot.md - ONNX Runtime inference →
references/onnx.md - Local/offline LLM with Ollama →
references/ollama.md
- Classic ML.NET →
Validation
- Selection follows the decision tree — no LLM for tasks ML.NET handles
- Only what was asked is produced (plan-only requests get a plan, not code)
- AI/ML services registered via DI; config via
IOptions<T>; keys from secure sources - Branch guardrails (Step 2 essentials, plus the reference when implementing) are satisfied
- After implementing, build and run existing tests
Anti-Patterns to Reject
| Anti-pattern | Redirect |
|---|---|
| LLM for tabular classification | Use ML.NET — faster, cheaper, deterministic |
| LLM calls without retry/timeout | Add RetryingChatClient or Polly retry |
API keys in committed appsettings.json | user-secrets / env / Key Vault |
| Accord.NET, or defaulting to Semantic Kernel without a requirement | ML.NET; prefer MEAI + Microsoft.Agents.AI for new work |
Hand-rolled multi-step tool loops with IChatClient | Microsoft.Agents.AI (MaximumIterations, tool dispatch) |
| Agent Framework for a single prompt→response | IChatClient directly |
Raw HttpClient/OpenAI SDK in business logic alongside MEAI | one abstraction layer; depend on IChatClient |
PredictionEngine singleton in ASP.NET Core | PredictionEnginePool<TIn,TOut> (not thread-safe) |
| RAG without chunking or relevance filtering | semantic chunking + minimum similarity score |
| Building custom neural nets in .NET from scratch | pre-trained via ONNX Runtime or an LLM API |
© dotnet, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 7 other files (references) in plugins/dotnet-ai/skills/technology-selection of dotnet/skills.
- SKILL.md
- references/agentic.md
- references/classic-ml.md
- references/copilot.md
- references/llm.md
- references/ollama.md
- references/onnx.md
- references/rag.md
Open the folder on GitHubat commit 8d670fa
Used in 2 other repositories
We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in dotnet/skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Technology Selection next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Technology Selection this skilldotnet/skills | 5.6k | 2 repos | ~2.1k | Automated safety check: Pass | MIT | |
| Re AI Modeldslsdzc/rev-skills | 117 | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | |
| Formattingbrendanhasz/probflow | 175 | — | ~381 | Automated safety check: Pass | MIT | |
| Ray Train Distributed TrainingOrchestra-Research/AI-Research-SKILLs | 13k | 3 repos | ~2.7k | Automated safety check: Pass | MIT | |
| Embedded AI Deploymentmatlab/agent-skills-playground | 181 | 1 repos | ~3.4k | Automated safety check: Pass | Custom licence | |
| Ray Data for ML PipelinesOrchestra-Research/AI-Research-SKILLs | 13k | 3 repos | ~1.8k | Automated safety check: Pass | MIT |
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Related
Works with
Categories
Questions about Technology Selection
What does Technology Selection do?
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…. Technology Selection is an agent skill from dotnet/skills, published by the product's own GitHub organization.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp.
When should I use Technology Selection?
Technology Selection fits situations like: adding classification; anomaly detection; LLM integration (text generation; RAG pipelines with vector search.
How do I install Technology Selection in Claude Code?
Run `npx skills add dotnet/skills --skill technology-selection -a claude-code`. Or copy the skill folder (plugins/dotnet-ai/skills/technology-selection in dotnet/skills) into .claude/skills/technology-selection in your project. Claude Code loads it when a task matches its description.
How do I install Technology Selection in Codex?
Run `npx skills add dotnet/skills --skill technology-selection -a codex`. Or copy the skill folder (plugins/dotnet-ai/skills/technology-selection in dotnet/skills) into .agents/skills/technology-selection in your project. Codex loads it when a task matches its description.
Can I use Technology Selection in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add dotnet/skills --skill technology-selection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/technology-selection, .gemini/skills/technology-selection, .github/skills/technology-selection and .opencode/skills/technology-selection in your project.
What does Technology Selection need to run?
SKILL.md names no scripts, command-line tools or credentials: Technology Selection is instructions for the agent only. Our summary lists: Python 3.
Does Technology Selection access the network?
SKILL.md names 1 domain. As links in the text: ollama.com. This is read from the text; nothing was executed.
Is Technology Selection safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Technology Selection use?
Technology Selection is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Technology Selection use?
About 2.1k tokens (SKILL.md is roughly 8.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.9k tokens, read only when the agent opens those files.
What are the alternatives to Technology Selection?
Skills that share tags, products or a category with Technology Selection: Re AI Model (dslsdzc/rev-skills, 117 stars), Formatting (brendanhasz/probflow, 175 stars), Ray Train Distributed Training (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Embedded AI Deployment (matlab/agent-skills-playground, 181 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Technology Selection?
dotnet (a GitHub organization, an official publisher) maintains it in dotnet/skills, which has 5,568 GitHub stars. The repository holds 91 skills in this directory. The repository was last updated on October 7, 2026.
Source: dotnet/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.