Machine learning and AI applications in Rust with tensor operations, model inference, and GPU acceleration.
Automate repetitive workflows and reduce manual effort
Example: Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example: Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example: Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Time estimate: 15-45 minutes depending on use case complexity
Prerequisites
Submit your Claude Code skill and start earning
epx add zhanghandong/rust-skills --skill domain-mlCompatible with
Supported agents
Covers tensor libraries (ndarray), inference frameworks (tract for ONNX, candle, burn), and PyTorch bindings (tch-rs) for training and deployment workflows
Emphasizes memory efficiency through zero-copy operations, GPU batching, and standard model formats (ONNX) for portability across Python and Rust
Provides design patterns for model loading with lazy initialization, batched i
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondomain-mlExecute the skills CLI command in your project's root directory to begin installation:
epx add zhanghandong/rust-skills --skill domain-mlFetches domain-ml from zhanghandong/rust-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
.cursor/skills/domain-ml
Restart Cursor to activate domain-ml. Access via /domain-ml in your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
rust-trait-explorer
zhanghandong/rust-skills · Backend · same repo
domain-hunter
resciencelab/opc-skills · AI/ML · tag: domain
ai-video-generation
inference-sh/skills · AI/ML · same category
ml-paper-writing
zechenzhangagi/ai-research-skills · AI/ML · same category
dokie-ai-ppt
myzy-ai/dokie-ai-ppt · AI/ML · same category
pretty-mermaid
imxv/pretty-mermaid-skills · AI/ML · same category
Solid pick for teams standardizing on skills: domain-ml is focused, and the summary matches what you get after install.
I recommend domain-ml for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
domain-ml has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: domain-ml is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend domain-ml for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: domain-ml is focused, and the summary matches what you get after install.
Keeps context tight: domain-ml is the kind of skill you can hand to a new teammate without a long onboarding doc.
domain-ml is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in domain-ml — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
domain-ml has been reliable in day-to-day use. Documentation quality is above average for community skills.
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