scikit-survival
Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.
npx skills add foryourhealth111-pixel/Vibe-Skills --skill scikit-survival --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.
# scikit-survival: Survival Analysis in Python ## Overview scikit-survival is a Python library for survival analysis built on top of scikit-learn. It provides specialized tools for time-to-event analysis, handling the unique challenge of censored data where some observations are only partially known. Survival analysis aims to establish connections between covariates and the time of an event, accou
What does the scikit-survival skill do?
Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.
How do I install it?
Run `npx skills add foryourhealth111-pixel/Vibe-Skills --skill scikit-survival --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 foryourhealth111-pixel/Vibe-Skills, a repository with 2,593 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.