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scikit-survival skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

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.

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Install the scikit-survival skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/scikit-survival ~/.claude/skills/scikit-survival
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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:

x86-64, macOS x86-64/ARM64, and Windows x86-64.

  • 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

scikit-learn >=1.9.0,<1.10, OSQP >=1.0.2, narwhals >=2.0.1.

  • Runtime bounds: NumPy >=2.0.0, pandas >=2.2.0, SciPy >=1.13.0,

criterion from GradientBoostingSurvivalAnalysis.

  • 0.28 adds pandas/Polars estimator support through narwhals and removes

Create an isolated environment and install the tested snapshot:

uv venv --python 3.11
source .venv/bin/activate
uv pip install \
  "scikit-survival==0.28.0" \
  "scikit-learn==1.9.0" \
  "numpy==2.4.6" \
  "pandas==3.0.5" \
  "scipy==1.17.1" \
  "ecos==2.0.14" \
  "osqp==1.1.3" \
  "joblib==1.5.3" \
  "numexpr==2.14.2" \
  "narwhals==2.24.0"

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

all-event survival, cause-specific hazard, or cause-specific cumulative incidence.

  1. Define the estimand and event coding. Decide whether the target is

boolean event first, observed time second. Competing-risk CIF instead needs a separate integer event vector: 0=censored, 1..K=causes.

  1. Validate outcomes. Standard estimators need a two-field structured array:

feature selectors, or alpha choices on all rows before splitting.

  1. Split before learned preprocessing. Never fit imputers, encoders, scalers,

be handled using training-fold state only.

  1. Fit preprocessing inside a pipeline. Unknown categories and missingness must

cross-validated tuned performance, or reserve a truly untouched final holdout.

  1. Tune without reusing evaluation data. Use nested CV when reporting

and Brier metrics receive survival_train, never a pooled train+test outcome.

  1. Fit censoring distributions on training data. IPCW concordance, dynamic AUC,

follow-up and below the end of training support where the estimated censoring survival remains positive.

  1. Restrict evaluation times. Use a strictly increasing grid inside test

scores. Brier metrics consume survival probabilities with shape (ntest, ntimes), not risk scores or unevaluated step functions.

  1. Match predictions to metrics. Concordance/dynamic AUC consume higher-is-riskier

answer different questions. Never estimate event-specific probability with 1 - Kaplan-Meier while censoring competing events.

  1. Handle competing causes explicitly. Standard survival probabilities and CIFs

and cumulative incidence. None alone establishes decision or clinical utility.

  1. Report limits. Separate discrimination, calibration, prediction error,

Outcome construction

from sksurv.util import Surv

y = Surv.from_arrays(event=event_bool, time=observed_time)
# Equivalent for pandas or Polars:
y = Surv.from_dataframe("event", "time", frame)

The first field is boolean (True=event, False=right-censored); the second is floating-point time. Field names may vary, but field order and meaning may not. Use references/data-handling.md before loading custom or competing-risk data.

Leakage-safe pipeline

from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sksurv.linear_model import CoxPHSurvivalAnalysis

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, stratify=y["event"], random_state=20260723
)

preprocess = ColumnTransformer(
    [
        ("num", make_pipeline(SimpleImputer(strategy="median"), StandardScaler()), numeric),
        (
            "cat",
            make_pipeline(
                SimpleImputer(strategy="most_frequent"),
                OneHotEncoder(handle_unknown="ignore", drop="first", sparse_output=False),
            ),
            categorical,
        ),
    ],
    sparse_threshold=0.0,
)
model = make_pipeline(preprocess, CoxPHSurvivalAnalysis(alpha=0.1, ties="efron"))
model.fit(X_train, y_train)
risk = model.predict(X_test)

The split precedes every learned transformation. For repeated or grouped records, use a group-aware split; for temporal deployment, use a time-respecting split.

Model choice

hazards; alpha is ridge shrinkage and ties is "breslow" or "efron".

  • CoxPHSurvivalAnalysis: interpretable log-hazard coefficients under proportional

l1ratio is in (0, 1]; use fitbaseline_model=True before requesting survival or cumulative-hazard functions.

  • CoxnetSurvivalAnalysis: LASSO/elastic-net path for high-dimensional data.

not a Cox risk score.

  • IPCRidge: IPC-weighted ridge AFT model; prediction is on a time/log-time scale,

hazard predictions; use permutation importance, not impurity importance.

  • RandomSurvivalForest / ExtraSurvivalTrees: nonlinear survival and cumulative

or "ipcwls" loss. criterion was removed in 0.28.

  • GradientBoostingSurvivalAnalysis: tree boosting with "coxph", "squared",

boosting.

  • ComponentwiseGradientBoostingSurvivalAnalysis: sparse linear componentwise

Only rank_ratio=1 directly returns higher-is-riskier scores; SVMs do not yield survival probabilities for Brier metrics.

  • FastSurvivalSVM / FastKernelSurvivalSVM: ranking or regression objectives.

Read the model-specific reference before interpreting coefficients or predictions: references/cox-models.md, references/ensemble-models.md, or references/svm-models.md.

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