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
npx skills add majiayu000/claude-skill-registry --skill domain-ml-actionbook-rust-skills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Machine Learning Domain > **Layer 3: Domain Constraints** ## Domain Constraints → Design Implications | 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 | | Reproducibility | Deterministic | Seeded random, versioning | --- ## Critical Constraints ### Memory Efficiency ``` RULE: Avoid copying large tensors WHY: Memory bandwidth is bottleneck RUST: References, views, in-place ops ``` ### GPU Utilization ``` RULE: Batch operations for GPU efficiency WHY: GPU overhead per kernel launch RUST: Batch sizes, async data loading ``` ### Model Portability ``` RULE: Use standard model formats WHY: Train in Python, deploy in Rust RUST: ONNX via tract or candle ``` --- ## Trace Down ↓ From constraints to design (Layer 2): ``` "Need efficient data pipelines" ↓ m10-performance: Streaming, batching ↓ polars: Lazy evaluation "Need GPU inference" ↓
- Domain Constraints → Design Implications
- Critical Constraints
- Memory Efficiency
- GPU Utilization
- Model Portability
- Trace Down ↓
- Use Case → Framework
- Key Crates
- Design Patterns
- Code Pattern: Inference Server
- Code Pattern: Batched Inference
- Common Mistakes
- Trace to Layer 1
- Related Skills
What does the domain-ml skill do?
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
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill domain-ml-actionbook-rust-skills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.