Scikit Learn Machine Learning
jaechang-hits/SciAgent-Skills
Classical ML in Python: classification, regression, clustering, dim reduction, evaluation, tuning, preprocessing pipelines.
A skill your agent uses when building ML/AI apps in Rust. An agent skill from majiayu000/claude-skill-registry.
Install Domain ML
$ npx skills add majiayu000/claude-skill-registry --skill domain-ml -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install majiayu000/claude-skill-registry domain-ml --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-ml/domain-ml .claude/skills/domain-ml && rm -rf skills-srcUse ~/.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/
Install the "domain-ml" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/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 install https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/domain-mlType 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.
$ npx skills add majiayu000/claude-skill-registry --skill domain-ml -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install majiayu000/claude-skill-registry domain-ml --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ai-ml/domain-ml .agents/skills/domain-ml && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "domain-ml" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/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.
$ npx skills add majiayu000/claude-skill-registry --skill domain-ml -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install majiayu000/claude-skill-registry domain-ml --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ai-ml/domain-ml .cursor/skills/domain-ml && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "domain-ml" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/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 https://github.com/majiayu000/claude-skill-registry.git --path skills/ai-ml/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.
$ npx skills add majiayu000/claude-skill-registry --skill domain-ml -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install majiayu000/claude-skill-registry domain-ml --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ai-ml/domain-ml .gemini/skills/domain-ml && rm -rf skills-srcUse ~/.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/
Install the "domain-ml" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/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.
$ gh skill install majiayu000/claude-skill-registry domain-mlInstalls 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).
$ npx skills add majiayu000/claude-skill-registry --skill domain-ml -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ai-ml/domain-ml .github/skills/domain-ml && rm -rf skills-srcUse ~/.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/
Install the "domain-ml" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/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.
$ npx skills add majiayu000/claude-skill-registry --skill domain-ml -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install majiayu000/claude-skill-registry domain-ml --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ai-ml/domain-ml .opencode/skills/domain-ml && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "domain-ml" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/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
- 666
- Used in
- 1 other repo
- Token cost
- ~1.2k tokens
- SKILL.md length
- 236 words
- Files
- 2
- Skills in repo
- 1,273
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when building ML/AI apps in Rust. An agent skill from majiayu000/claude-skill-registry.
- 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 majiayu000/claude-skill-registry. 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. The skill folder holds 1 other file (for example `metadata.json`).
It sits in Data & Analytics, covering Deep learning and Machine learning. It works with NumPy, Rust and ONNX. The repository describes itself as: Searchable Claude Code skills catalog with source-linked guides and generated registry artifacts. The licence is MIT.
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 2d14a69. 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.
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 majiayu000/claude-skill-registry at commit 2d14a69, republished under its MIT licence (© majiayu000). 236 words, ~1,162 tokens.
Download SKILL.mdSave it as
.claude/skills/domain-ml/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- 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
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 opsGPU Utilization
RULE: Batch operations for GPU efficiency
WHY: GPU overhead per kernel launch
RUST: Batch sizes, async data loadingModel Portability
RULE: Use standard model formats
WHY: Train in Python, deploy in Rust
RUST: ONNX via tract or candleTrace 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 runtimeUse 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
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
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 |
Related Skills
| When | See |
|---|---|
| Performance | m10-performance |
| Lazy initialization | m12-lifecycle |
| Async patterns | m07-concurrency |
| Memory efficiency | m01-ownership |
© majiayu000, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file in skills/ai-ml/domain-ml of majiayu000/claude-skill-registry.
- SKILL.md
- metadata.json
Open the folder on GitHubat commit 2d14a69
Used in 1 other repository
We found 6 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in majiayu000/claude-skill-registry, which our catalogue first saw on October 7, 2026.
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Domain ML this skillmajiayu000/claude-skill-registry | 666 | 1 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Scikit Learn Machine Learningjaechang-hits/SciAgent-Skills | 370 | 1 repos | ~4k | Automated safety check: Pass | BSD-3-Clause | |
| GPU OptimizerMathews-Tom/armory | 328 | — | ~3.5k | Automated safety check: Notes | MIT | |
| Xybrid Initxybrid-ai/xybrid | 466 | — | ~3k | Automated safety check: Pass | Apache-2.0 | |
| Optimize For GPUK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.4k | Automated safety check: Pass | MIT | |
| ML EngineerRightNow-AI/openfang | 18k | — | ~987 | Automated safety check: Pass | Apache-2.0 |
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Generate model metadata for an ML model so it works with xybrid.
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Related
Categories
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 majiayu000/claude-skill-registry. Domain ML is an agent skill from majiayu000/claude-skill-registry. 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 majiayu000/claude-skill-registry --skill domain-ml -a claude-code`. Or copy the skill folder (skills/ai-ml/domain-ml in majiayu000/claude-skill-registry) 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 majiayu000/claude-skill-registry --skill domain-ml -a codex`. Or copy the skill folder (skills/ai-ml/domain-ml in majiayu000/claude-skill-registry) 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 majiayu000/claude-skill-registry --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 MIT 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 4.6k 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, 370 stars), GPU Optimizer (Mathews-Tom/armory, 328 stars), Xybrid Init (xybrid-ai/xybrid, 466 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?
majiayu000 (a GitHub user) maintains it in majiayu000/claude-skill-registry, which has 666 GitHub stars. The repository holds 1,273 skills in this directory. The repository was last updated on October 7, 2026.
Source: majiayu000/claude-skill-registry on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.