Agent skill · research science · k-dense-ai
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.
Why this skill is useful
Provides domain-specific knowledge and workflows for implementing survival analysis with scikit-survival that the AI wouldn't reliably generate on its own.
What it needs
Requires python, uv installed locally. About 6k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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. Create an isolated environment and install the tested snapshot: Binary wheels are preferred. A source build requires a C/C++ compiler; OSQP may also require CMake. This skill is MIT-licensed; the upstream scikit-survival package is GPL-3.0-or-later, so review upstream licensing before redistribution. Non-negotiable workflow 1. Define the estimand and event coding. Decide whether the target is all-event survival, cause-specific hazard, or cause-specific cumulative incidence. 2. Validate outcomes. Standard estimators need a two-field structured array: boolean event first, observed time second. Competing-risk CIF instead needs a separate integer event vector: 0=censored, 1..K=causes. 3. Split before learned preprocessing. Never fit imputers, encoders, scalers, feature selectors, or alpha choices on all rows before splitting. 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/scikit-survival