ml-engineer
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.
npx skills add majiayu000/claude-skill-registry --skill ml-engineer-andre-sugai-cine-explorer-mvp-bl --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Use this skill when - Working on ml engineer tasks or workflows - Needing guidance, best practices, or checklists for ml engineer ## Do not use this skill when - The task is unrelated to ml engineer - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. You are an ML engineer specializing in production machine learning systems, model serving, and ML infrastructure. ## Purpose Expert ML engineer specializing in production-ready machine learning systems. Masters modern ML frameworks (PyTorch 2.x, TensorFlow 2.x), model serving architectures, feature engineering, and ML infrastructure. Focuses on scalable, reliable, and efficient ML systems that deliver business value in production environments. ## Capabilities ### Core ML Frameworks & Libraries - PyTorch 2.x with torch.compile, FSDP, and distributed training capabilities - TensorFlow 2.x/Keras with tf.function, mixed precision, and TensorFlow Serving - JAX/Flax for research and high-p
- Use this skill when
- Do not use this skill when
- Instructions
- Purpose
- Capabilities
- Core ML Frameworks & Libraries
- Model Serving & Deployment
- Feature Engineering & Data Processing
- Model Training & Optimization
- Production ML Infrastructure
- MLOps & CI/CD Integration
- Performance & Scalability
- Model Evaluation & Testing
- Specialized ML Applications
What does the ml-engineer skill do?
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-engineer-andre-sugai-cine-explorer-mvp-bl --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.