Production-grade ML engineering expertise for deploying models, building MLOps systems, and scaling AI infrastructure.
Works with
Covers model deployment, feature stores, monitoring, and distributed computing with PyTorch, TensorFlow, Spark, and Kubernetes
Includes LLM integration patterns, RAG system architecture, and fine-tuning workflows using LangChain and LlamaIndex
Provides production patterns for scalable data processing, real-time inference, A/B testing, and automated retraining pipelin
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsenior-ml-engineerExecute the skills CLI command in your project's root directory to begin installation:
Fetches senior-ml-engineer from davila7/claude-code-templates 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:
Restart Cursor to activate senior-ml-engineer. Access via /senior-ml-engineer in your agent's command palette.
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.
Submit your Claude Code skill and start earning
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
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World-class senior ml/ai engineer skill for production-grade AI/ML/Data systems.
# Core Tool 1
python scripts/model_deployment_pipeline.py --input data/ --output results/
# Core Tool 2
python scripts/rag_system_builder.py --target project/ --analyze
# Core Tool 3
python scripts/ml_monitoring_suite.py --config config.yaml --deploy
This skill covers world-class capabilities in:
Languages: Python, SQL, R, Scala, Go ML Frameworks: PyTorch, TensorFlow, Scikit-learn, XGBoost Data Tools: Spark, Airflow, dbt, Kafka, Databricks LLM Frameworks: LangChain, LlamaIndex, DSPy Deployment: Docker, Kubernetes, AWS/GCP/Azure Monitoring: MLflow, Weights & Biases, Prometheus Databases: PostgreSQL, BigQuery, Snowflake, Pinecone
Comprehensive guide available in references/mlops_production_patterns.md covering:
Complete workflow documentation in references/llm_integration_guide.md including:
Technical reference guide in references/rag_system_architecture.md with:
Enterprise-scale data processing with distributed computing:
Production ML system with high availability:
High-throughput inference system:
Latency:
Throughput:
Availability:
# Development
python -m pytest tests/ -v --cov
python -m black src/
python -m pylint src/
# Training
python scripts/train.py --config prod.yaml
python scripts/evaluate.py --model best.pth
# Deployment
docker build -t service:v1 .
kubectl apply -f k8s/
helm upgrade service ./charts/
# Monitoring
kubectl logs -f deployment/service
python scripts/health_check.py
references/mlops_production_patterns.mdreferences/llm_integration_guide.mdreferences/rag_system_architecture.mdscripts/ directoryAs a world-class senior professional:
Technical Leadership
Strategic Thinking
Collaboration
Innovation
Production Excellence
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
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.
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
Registry listing for senior-ml-engineer matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in senior-ml-engineer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend senior-ml-engineer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
senior-ml-engineer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
senior-ml-engineer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
senior-ml-engineer has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: senior-ml-engineer is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: senior-ml-engineer is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: senior-ml-engineer is focused, and the summary matches what you get after install.
senior-ml-engineer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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