Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add moeru-ai/auv --skill domain-mlgit clone --depth 1 https://github.com/moeru-ai/auvWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/moeru-ai/auv/domain-ml)<a href="https://agentmods.dev/skills/moeru-ai/auv/domain-ml"><img src="https://agentmods.dev/badge/skills/moeru-ai/auv/domain-ml/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/moeru-ai/auv/domain-ml"><img src="https://agentmods.dev/badge/skills/moeru-ai/auv/domain-ml.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00061 | $0.01129 |
| Opus 5 | $0.00030 | $0.00564 |
| Sonnet 5 | $0.00012 | $0.00226 |
| Haiku 4.5 | $0.00006 | $0.00113 |
Grade A, and why
domain-ml scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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"
↓ m07-concurrency: Async data loading
↓ candle/tch-rs: CUDA backend
"Need model loading"
↓ m12-lifecycle: Lazy init, caching
↓ tract: ONNX runtime
Use Case → Framework
| Use Case | Recommended | Why |
|---|---|---|
| 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 |
Key Crates
| Purpose | Crate |
|---|---|
| Tensors | ndarray |
| ONNX inference | tract |
| ML framework | candle, burn |
| PyTorch bindings | tch-rs |
| Data processing | polars |
| Embeddings | fastembed |
Design Patterns
| Pattern | Purpose | Implementation |
|---|---|---|
| Model loading | Once, reuse | OnceLock<Model> |
| Batching | Throughput | Collect then process |
| Streaming | Large data | Iterator-based |
| GPU async | Parallelism | Data loading parallel to compute |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 182 lines · 61 tokens per session scan A 27db4b6a8e89
domain-ml is a skill published in the GitHub repository moeru-ai/auv (27 stars, last pushed yesterday), licensed Apache-2.0. It adds 61 tokens to every session and 1,129 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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