scikit-survival
bysynthetic-sciences· 59 skills
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 to install scikit-survival?
synthetic-sciences/openscience/scikit-survival
$npx -y skills add synthetic-sciences/openscience --skill scikit-survivalInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/synthetic-sciences/openscience" --skill "synthetic-sciences/openscience/scikit-survival"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/synthetic-sciences/openscience" that are relevant to the current task. Run `npx skills add "https://github.com/synthetic-sciences/openscience"` and select the relevant skills, then follow their instructions.
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