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.
8.7
Rating
0
Installs
Machine Learning
Category
Exceptional survival analysis skill with comprehensive coverage of scikit-survival library. The description clearly articulates when to use this skill, and SKILL.md provides outstanding task knowledge including model selection decision trees, complete workflows, best practices, and common pitfalls. Structure is excellent with a well-organized main file and logical separation of detailed topics into reference files. The skill addresses a highly specialized domain (survival analysis with censored data) that would be token-intensive and error-prone for a CLI agent to implement from scratch, particularly the nuanced evaluation metrics, competing risks handling, and model selection logic. Minor room for improvement in explicitly detailing a few more edge cases, but overall this is a production-ready, high-value skill.
davila7
Skill Author
Loading SKILL.md…
