Agent skill

Domain ML

A skill your agent uses when building ML/AI apps in Rust. An agent skill from moeru-ai/auv.

Apache-2.0Auto-check passedData & Analytics

Install Domain ML

skills CLI

$ npx skills add moeru-ai/auv --skill domain-ml -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install moeru-ai/auv domain-ml --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/moeru-ai/auv.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/domain-ml .claude/skills/domain-ml && rm -rf skills-src

Use ~/.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/

Paste into Claude Code

Install the "domain-ml" agent skill from https://github.com/moeru-ai/auv/tree/main/.agents/skills/domain-ml into .claude/skills/domain-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "domain-ml", 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

$skill-installer install https://github.com/moeru-ai/auv/tree/main/.agents/skills/domain-ml

Type 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.

skills CLI

$ npx skills add moeru-ai/auv --skill domain-ml -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install moeru-ai/auv domain-ml --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/moeru-ai/auv.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/domain-ml .agents/skills/domain-ml && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "domain-ml" agent skill from https://github.com/moeru-ai/auv/tree/main/.agents/skills/domain-ml into .agents/skills/domain-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "domain-ml", 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.

skills CLI

$ npx skills add moeru-ai/auv --skill domain-ml -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install moeru-ai/auv domain-ml --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/moeru-ai/auv.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/domain-ml .cursor/skills/domain-ml && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "domain-ml" agent skill from https://github.com/moeru-ai/auv/tree/main/.agents/skills/domain-ml into .cursor/skills/domain-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "domain-ml", 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

$ gemini skills install https://github.com/moeru-ai/auv.git --path .agents/skills/domain-ml

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add moeru-ai/auv --skill domain-ml -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install moeru-ai/auv domain-ml --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/moeru-ai/auv.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/domain-ml .gemini/skills/domain-ml && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "domain-ml" agent skill from https://github.com/moeru-ai/auv/tree/main/.agents/skills/domain-ml into .gemini/skills/domain-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "domain-ml", 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.

GitHub CLI

$ gh skill install moeru-ai/auv domain-ml

Installs 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).

skills CLI

$ npx skills add moeru-ai/auv --skill domain-ml -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/moeru-ai/auv.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/domain-ml .github/skills/domain-ml && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "domain-ml" agent skill from https://github.com/moeru-ai/auv/tree/main/.agents/skills/domain-ml into .github/skills/domain-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "domain-ml", 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.

skills CLI

$ npx skills add moeru-ai/auv --skill domain-ml -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install moeru-ai/auv domain-ml --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/moeru-ai/auv.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/domain-ml .opencode/skills/domain-ml && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "domain-ml" agent skill from https://github.com/moeru-ai/auv/tree/main/.agents/skills/domain-ml into .opencode/skills/domain-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "domain-ml", 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: domain-ml
GitHub stars: 100
Used in: 1 other repo
Token cost: ~1.2k tokens
SKILL.md length: 236 words
Files: 1
Repo updated: Oct 11, 2026
First seen: Oct 11, 2026
Licence: Apache-2.0

At a glance

A skill your agent uses when building ML/AI apps in Rust. An agent skill from moeru-ai/auv.

Building ML/AI apps in Rust
SKILL.md covers Domain Constraints → Design…, Critical Constraints, Trace Down ↓ and Use Case → Framework, plus 7 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
Tasks that involve Deep learning

What it does

Domain ML is an agent 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

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Deep learning and Machine learning. It works with NumPy, Rust and ONNX. The repository describes itself as: 📱🐭 No we are not computer use, it's Application Use Via... a unified orchestration layer of OS automation, 0 token cost. The licence is Apache-2.0.

When your agent uses it

Building ML/AI apps in Rust
Tasks that involve Deep learning
Tasks that involve Machine learning

Example prompts

“/domain-ml”

What it can do on your machine

Read from SKILL.md and the folder at commit 4042350. 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 (its code samples are rust).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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

Domain ML loads about 1.2k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 236 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~53
When it runs · the whole SKILL.md, loaded when a task matches: ~1.2k

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 moeru-ai/auv at commit 4042350, republished under its Apache-2.0 licence (© moeru-ai). 236 words, ~1,168 tokens.

Download SKILL.mdSave it as .claude/skills/domain-ml/SKILL.md (or your agent's skills folder).

name: domain-ml
description: 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
user-invocable: false

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

Code Pattern: Inference Server

rust

use std::sync::OnceLock;
use tract_onnx::prelude::*;

static MODEL: OnceLock<SimplePlan<TypedFact, Box<dyn TypedOp>, Graph<TypedFact, Box<dyn TypedOp>>>> = OnceLock::new();

fn get_model() -> &'static SimplePlan<...> {
    MODEL.get_or_init(|| {
        tract_onnx::onnx()
            .model_for_path("model.onnx")
            .unwrap()
            .into_optimized()
            .unwrap()
            .into_runnable()
            .unwrap()
    })
}

async fn predict(input: Vec<f32>) -> anyhow::Result<Vec<f32>> {
    let model = get_model();
    let input = tract_ndarray::arr1(&input).into_shape((1, input.len()))?;
    let result = model.run(tvec!(input.into()))?;
    Ok(result[0].to_array_view::<f32>()?.iter().copied().collect())
}

Code Pattern: Batched Inference

rust

async fn batch_predict(inputs: Vec<Vec<f32>>, batch_size: usize) -> Vec<Vec<f32>> {
    let mut results = Vec::with_capacity(inputs.len());

    for batch in inputs.chunks(batch_size) {
        // Stack inputs into batch tensor
        let batch_tensor = stack_inputs(batch);

        // Run inference on batch
        let batch_output = model.run(batch_tensor).await;

        // Unstack results
        results.extend(unstack_outputs(batch_output));
    }

    results
}

Common Mistakes

Mistake	Domain Violation	Fix
Clone tensors	Memory waste	Use views
Single inference	GPU underutilized	Batch processing
Load model per request	Slow	Singleton pattern
Sync data loading	GPU idle	Async pipeline

Trace to Layer 1

Constraint	Layer 2 Pattern	Layer 1 Implementation
Memory efficiency	Zero-copy	ndarray views
Model singleton	Lazy init	OnceLock`<Model>`
Batch processing	Chunked iteration	chunks() + parallel
GPU async	Concurrent loading	tokio::spawn + GPU

When	See
Performance	m10-performance
Lazy initialization	m12-lifecycle
Async patterns	m07-concurrency
Memory efficiency	m01-ownership

Files

Just SKILL.md in .agents/skills/domain-ml of moeru-ai/auv.

Open the folder on GitHubat commit 4042350

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in moeru-ai/auv, which our catalogue first saw on October 11, 2026.

NeverSight/learn-skills.devdata/skills-md/actionbook/rust-skills/domain-ml

Compare with similar skills

Domain ML 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.

Domain ML compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Domain ML this skillmoeru-ai/auv	100	1 repos	~1.2k	Automated safety check: Pass	Apache-2.0	today
Scikit Learn Machine Learningjaechang-hits/SciAgent-Skills	374	1 repos	~4k	Automated safety check: Pass	BSD-3-Clause	12 days ago
GPU OptimizerMathews-Tom/armory	329	—	~3.5k	Automated safety check: Notes	MIT	5 days ago
Xybrid Initxybrid-ai/xybrid	469	—	~3k	Automated safety check: Pass	Apache-2.0	yesterday
Optimize For GPUK-Dense-AI/scientific-agent-skills	48k	1 repos	~3.4k	Automated safety check: Pass	MIT	6 days ago
ML EngineerRightNow-AI/openfang	18k	—	~987	Automated safety check: Pass	Apache-2.0	3 mo ago

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Questions about Domain ML

What does Domain ML do?

A skill your agent uses when building ML/AI apps in Rust. An agent skill from moeru-ai/auv. Domain ML is an agent skill from moeru-ai/auv. Use when building ML/AI apps in Rust.

When should I use Domain ML?

Domain ML fits situations like: building ML/AI apps in Rust; tasks that involve Deep learning; tasks that involve Machine learning.

How do I install Domain ML in Claude Code?

Run `npx skills add moeru-ai/auv --skill domain-ml -a claude-code`. Or copy the skill folder (.agents/skills/domain-ml in moeru-ai/auv) into .claude/skills/domain-ml in your project. Claude Code loads it when a task matches its description.

How do I install Domain ML in Codex?

Run `npx skills add moeru-ai/auv --skill domain-ml -a codex`. Or copy the skill folder (.agents/skills/domain-ml in moeru-ai/auv) into .agents/skills/domain-ml in your project. Codex loads it when a task matches its description.

Can I use Domain ML 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 moeru-ai/auv --skill domain-ml -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/domain-ml, .gemini/skills/domain-ml, .github/skills/domain-ml and .opencode/skills/domain-ml in your project.

What does Domain ML need to run?

SKILL.md names no scripts, command-line tools or credentials: Domain ML is instructions for the agent only.

Does Domain ML access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Domain ML 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 Domain ML use?

Domain ML is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Domain ML use?

About 1.2k tokens (SKILL.md is roughly 4.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Domain ML?

Skills that share tags, products or a category with Domain ML: Scikit Learn Machine Learning (jaechang-hits/SciAgent-Skills, 374 stars), GPU Optimizer (Mathews-Tom/armory, 329 stars), Xybrid Init (xybrid-ai/xybrid, 469 stars) and Optimize For GPU (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Domain ML?

moeru-ai (a GitHub organization) maintains it in moeru-ai/auv, which has 100 GitHub stars. The repository was last updated on October 11, 2026.

Source: moeru-ai/auv on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Domain ML

Facts

At a glance

What it does

When your agent uses it

Example prompts

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Machine Learning Domain

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

Files

Used in 1 other repository

Compare with similar skills

Similar skills

More from moeru-ai/auv

Related

Questions about Domain ML