This skill turns raw data and questions into validated, documented models ready for production:
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 vasilyu1983/ai-agents-public --skill ai-ml-data-scienceCompatible with
Supported agents
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionai-ml-data-scienceExecute the skills CLI command in your project's root directory to begin installation:
epx add vasilyu1983/ai-agents-public --skill ai-ml-data-scienceFetches ai-ml-data-science from vasilyu1983/ai-agents-public 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/ai-ml-data-science
Restart Cursor to activate ai-ml-data-science. Access via /ai-ml-data-sciencein 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.
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Registry listing for ai-ml-data-science matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: ai-ml-data-science is the kind of skill you can hand to a new teammate without a long onboarding doc.
ai-ml-data-science reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend ai-ml-data-science for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in ai-ml-data-science — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
ai-ml-data-science has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: ai-ml-data-science is focused, and the summary matches what you get after install.
Useful defaults in ai-ml-data-science — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend ai-ml-data-science for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
ai-ml-data-science fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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