Scikit Survival

This is your go-to when you need survival analysis in Python and have censored time-to-event data. It wraps scikit-survival with clear guidance on choosing between Cox models, Random Survival Forests, Gradient Boosting, and SVMs depending on your dataset size and whether you need interpretability. The skill includes a helpful decision tree for model selection and covers the metrics that actually matter for censored data, like Uno's C-index for high censoring rates and time-dependent AUC. It also handles competing risks when you have multiple mutually exclusive events. The references break down Cox models, ensemble methods, SVMs, and evaluation metrics separately, which is useful when you're knee-deep in a specific modeling approach.

skills.sh installs: 2.1K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill scikit-survival --agent claude-code

Installs into .claude/skills of the current project.

Files

scikit-survival

Scope

Use this skill for scikit-survival 0.28.0 workflows involving:

right-censored structured outcomes;
Cox PH, Coxnet, IPC ridge, survival trees, forests, boosting, and SVMs;
discrimination, prediction error, calibration-oriented checks, and time-dependent prediction;
nonparametric cumulative incidence with competing risks;
scikit-learn pipelines, nested model selection, and reproducible reports.

scikit-survival primarily models right-censored outcomes. Its built-in competing-risk support is nonparametric cumulative incidence; it does not provide Fine-Gray regression. Do not present model output as clinical advice, causal evidence, or proof of clinical utility.

Python snippets using study-defined variables such as X_train, y_train, frame, or times are illustrative integration templates. The native regression tests and local CLI smoke runs use small synthetic fixtures; they do not validate an unprovided study, clinical dataset, or every optional dataframe backend.