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.
8.3
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0
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Machine Learning
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Excellent survival analysis skill with comprehensive coverage of scikit-survival capabilities. The description is crystal clear, enabling easy invocation for survival analysis tasks. The skill provides extensive task knowledge including model selection decision trees, complete workflows, integration patterns, and best practices. Structure is strong with a well-organized main file and logical separation of detailed content into reference files. The skill demonstrates high novelty - survival analysis with censored data, model comparisons, and proper evaluation metrics would require significant tokens and domain expertise from a CLI agent alone. Minor areas for improvement: could include more specific code examples for edge cases and slightly more explicit cross-references to the reference files in workflows. Overall, this is a well-crafted, production-ready skill that meaningfully reduces the complexity and cost of performing survival analysis tasks.
K-Dense-AI
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