domain-ml skillA
domain-ml is agent-read markdown (skill) from moeru-ai/auv: 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.
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# 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
…Read the whole file at its exact version.
How to install
mdr add moeru-ai/auv/domain-ml@git:20260725.8a8e541mdr add moeru-ai/auv/domain-ml@sha256:27db4b6a8e891ff9Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_monju3tmr45wknos)
1 badge views in 30 days
Versions
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (4711 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
moeru-ai/auv · 50 stars · license Apache-2.0 · pushed 2026-09-23 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_monju3tmr45wknos GET https://markdownregistry.com/api/v1/resolve?ref=moeru-ai/auv/domain-ml GET https://markdownregistry.com/api/v1/blob/27db4b6a8e891ff903aa5a5f10bc4f28c9f319c9a1c7012cbb34f50148033d20
Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.