scikit-survival

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.

Key Features

• Comprehensive skill evaluation and performance tracking
• Community-driven ratings and reviews
• Easy integration with Claude Code
• Regular updates and maintenance

Quick Start

TopRank Skills install K-Dense-AI/scikit-survival