scikit-survival
Build, evaluate, and audit right-censored or competing-risk survival workflows with scikit-survival, including leakage-safe preprocessing, model selection, probability prediction, and censoring-aware metrics.
npx skills add K-Dense-AI/scientific-agent-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 ## 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. ## Current release and installation Verified 2026-07-23: - Latest stable: **scikit-survival 0.28.0**, released 2026-07-05. - Python: **3.11 or later**; PyPI wheels cover CPython 3.11-3.14 on Linux x86-64, macOS x86-64/ARM64, and Windows x86-64. - Runtime bounds: NumPy >=2.0.0, pandas >=2.2.0, SciPy >=1.13.0, scikit-learn >=1.9.0,<1.10, OSQP >=1.0.2, narwhals >=2.0.1. - 0.28 adds pandas/Polars estimator support through narwhals and removes `criterion` from `GradientBoostingSurvivalAnalysis`. C
- Scope
- Current release and installation
- Non-negotiable workflow
- Outcome construction
- Leakage-safe pipeline
- Model choice
- Prediction and metric contracts
- Pipelines, metadata routing, and tuning
- Competing risks
- Bundled local CLIs
- Security triage
- Reference files
- Dated sources
uv venv --python 3.11 source .venv/bin/activate uv pip install \ python skills/scikit-survival/scripts/validate_survival_csv.py --help python skills/scikit-survival/scripts/train_survival_model.py --help python skills/scikit-survival/scripts/evaluate_survival_metrics.py --help python skills/scikit-survival/scripts/competing_risk_cif.py --help python skills/scikit-survival/scripts/model_report.py --help python skills/scikit-survival/scripts/validate_survival_csv.py \ python skills/scikit-survival/scripts/train_survival_model.py \
What does the scikit-survival skill do?
Build, evaluate, and audit right-censored or competing-risk survival workflows with scikit-survival, including leakage-safe preprocessing, model selection, probability prediction, and censoring-aware metrics.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-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 K-Dense-AI/scientific-agent-skills, a repository with 32,619 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.