Production-grade ML engineering expertise for deploying models, building MLOps systems, and scaling AI infrastructure.
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 davila7/claude-code-templates --skill senior-ml-engineerCompatible with
Supported agents
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:
epx add davila7/claude-code-templates --skill senior-ml-engineerFetches 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:
.cursor/skills/senior-ml-engineer
Restart Cursor to activate senior-ml-engineer. Access via /senior-ml-engineerin 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.
senior-data-engineer
davila7/claude-code-templates · Productivity · same repo
regulatory-affairs-head
davila7/claude-code-templates · AI/ML · same repo
remotion-best-practices
davila7/claude-code-templates · Video · same repo
whisper
davila7/claude-code-templates · Productivity · same repo
excalidraw
davila7/claude-code-templates · Productivity · same repo
ml-engineer
sickn33/antigravity-awesome-skills · AI/ML · tag: engineer
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.
showing 1-10 of 56