Survival analysis and time-to-event modeling in Python with scikit-survival. Use when working with censored survival data, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating predictions with concordance index or Brier score, handling competing risks, or implementing any time-to-event workflow. Part of the AlterLab Academic Skills suite.
Install
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-scikit-survivalnpx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-scikit-survival -a claude-code -gnpx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-scikit-survival -a codex -ggemini skills install https://github.com/AlterLab-IEU/AlterLab-Academic-Skills.git --path skills/data-science/alterlab-scikit-survivalgh skill install AlterLab-IEU/AlterLab-Academic-Skills alterlab-scikit-survivalgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-Skills /tmp/AlterLab-IEU__AlterLab-Academic-Skills
mkdir -p ~/.claude/skills ~/.agents/skills
cp -R /tmp/AlterLab-IEU__AlterLab-Academic-Skills/skills/data-science/alterlab-scikit-survival ~/.claude/skills/alterlab-scikit-survival # Claude Code
cp -R /tmp/AlterLab-IEU__AlterLab-Academic-Skills/skills/data-science/alterlab-scikit-survival ~/.agents/skills/alterlab-scikit-survival # Codex, Cursor, Gemini CLI, Copilot, OpenCode