domain-ml
Machine learning and AI applications in Rust with tensor operations, model inference, and GPU acceleration.
Works with
What it does
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
Installation Guide
How to use domain-ml on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
domain-ml
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches domain-ml from zhanghandong/rust-skills and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate domain-ml. Access via /domain-ml in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort