domain-ml
✓Use when building ML/AI apps in Rust. Keywords: machine learning, ML, AI, tensor, model, inference, neural network, deep learning, training, prediction, ndarray, tch-rs, burn, candle, 机器学习, 人工智能, 模型推理
Installation
SKILL.md
| Domain Rule | Design Constraint | Rust Implication |
| Large data | Efficient memory | Zero-copy, streaming | | GPU acceleration | CUDA/Metal support | candle, tch-rs | | Model portability | Standard formats | ONNX | | Batch processing | Throughput over latency | Batched inference | | Numerical precision | Float handling | ndarray, careful f32/f64 |
| Inference only | tract (ONNX) | Lightweight, portable | | Training + inference | candle, burn | Pure Rust, GPU | | PyTorch models | tch-rs | Direct bindings | | Data pipelines | polars | Fast, lazy eval |
Use when building ML/AI apps in Rust. Keywords: machine learning, ML, AI, tensor, model, inference, neural network, deep learning, training, prediction, ndarray, tch-rs, burn, candle, 机器学习, 人工智能, 模型推理 Source: actionbook/rust-skills.
Facts (cite-ready)
Stable fields and commands for AI/search citations.
- Install command
npx skills add https://github.com/actionbook/rust-skills --skill domain-ml- Source
- actionbook/rust-skills
- Category
- </>Dev Tools
- Verified
- ✓
- First Seen
- 2026-02-01
- Updated
- 2026-02-18
Quick answers
What is domain-ml?
Use when building ML/AI apps in Rust. Keywords: machine learning, ML, AI, tensor, model, inference, neural network, deep learning, training, prediction, ndarray, tch-rs, burn, candle, 机器学习, 人工智能, 模型推理 Source: actionbook/rust-skills.
How do I install domain-ml?
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/actionbook/rust-skills --skill domain-ml Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code or Cursor
Where is the source repository?
https://github.com/actionbook/rust-skills
Details
- Category
- </>Dev Tools
- Source
- skills.sh
- First Seen
- 2026-02-01