Modern Best Practices (January 2026):
Run in your terminal
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionai-ml-timeseriesExecute the skills CLI command in your project's root directory to begin installation:
Package manager
npx skills add https://github.com/vasilyu1983/ai-agents-public --skill ai-ml-timeseriesFetches ai-ml-timeseries 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:
Restart Cursor to activate ai-ml-timeseries. Access via /ai-ml-timeseriesin 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
Package manager
npx skills add https://github.com/vasilyu1983/ai-agents-public --skill ai-ml-timeseriesWorks with
0
total installs
0
this week
53
GitHub stars
0
upvotes
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.
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Keeps context tight: ai-ml-timeseries is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend ai-ml-timeseries for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in ai-ml-timeseries — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
ai-ml-timeseries is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
ai-ml-timeseries is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in ai-ml-timeseries — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend ai-ml-timeseries for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: ai-ml-timeseries is focused, and the summary matches what you get after install.
Keeps context tight: ai-ml-timeseries is the kind of skill you can hand to a new teammate without a long onboarding doc.
ai-ml-timeseries has been reliable in day-to-day use. Documentation quality is above average for community skills.
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