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.
npx skills add LeonChaoX/qinyan-academic-skills --skill scikit-survival --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# scikit-survival: Survival Analysis in Python ## Overview scikit-survival is a Python library for survival analysis built on top of scikit-learn. It provides specialized tools for time-to-event analysis, handling the unique challenge of censored data where some observations are only partially known. Survival analysis aims to establish connections between covariates and the time of an event, accounting for censored records (particularly right-censored data from studies where participants don't experience events during observation periods). ## When to Use This Skill Use this skill when: - Performing survival analysis or time-to-event modeling - Working with censored data (right-censored, left-censored, or interval-censored) - Fitting Cox proportional hazards models (standard or penalized) - Building ensemble survival models (Random Survival Forests, Gradient Boosting) - Training Survival Support Vector Machines - Evaluating survival model performance (concordance index, Brier score, time-dependent AUC) - Estimating Kaplan-Meier or Nelson-Aalen curves - Analyzing competing risks - Preprocessing survival data or handling missing values in survival datasets - Conducting any analysis us
- Overview
- When to Use This Skill
- Core Capabilities
- 1. Model Types and Selection
- 2. Data Preparation and Preprocessing
- 3. Model Evaluation
- 4. Competing Risks Analysis
- 5. Non-parametric Estimation
- Typical Workflows
- Workflow 1: Standard Survival Analysis
- Workflow 2: High-Dimensional Data with Feature Selection
- Workflow 3: Ensemble Method for Maximum Performance
- Workflow 4: Comprehensive Model Comparison
- Integration with scikit-learn
What does the scikit-survival skill do?
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.
How do I install it?
Run `npx skills add LeonChaoX/qinyan-academic-skills --skill scikit-survival --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From LeonChaoX/qinyan-academic-skills, a repository with 759 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.