Skills scikit-survival

📦

scikit-survival

Name: scikit-survival
Author: davila7

Content revision r2 Safe ⚡ Contains scripts⚙️ External commands🌐 Network access

Build Survival Models with scikit-survival

Censored time-to-event data requires specialized preparation, modeling, and evaluation. This skill guides practical scikit-survival workflows from outcome encoding through model comparison.

Supports: Claude Codex Code(CC)

📊 76 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "scikit-survival" from https://skillstore.io/skills/davila7-scikit-survival.md and its manifest at https://skillstore.io/api/skills/davila7-scikit-survival/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/davila7-scikit-survival.md Signed manifest GET /api/skills/davila7-scikit-survival/manifest Signed lockfile GET /api/skills/davila7-scikit-survival/lockfile

Test it

Using "scikit-survival". Which model should I use for 800 records, 25 features, moderate censoring, and interpretable effects?

Expected outcome:

Start with a Cox proportional hazards model. Check proportional hazards, preprocess features consistently, and compare performance with a regularized Cox alternative.

Using "scikit-survival". How should I evaluate predictions when 55 percent of observations are censored?

Expected outcome:

Use Uno's concordance index for ranking performance.
Add time-dependent AUC at clinically meaningful horizons.
Report integrated Brier score to assess calibration and discrimination.

Using "scikit-survival". My study records relapse and death before relapse. How should I analyze relapse probability?

Expected outcome:

Treat death before relapse as a competing event. Estimate cumulative incidence for relapse and avoid interpreting ordinary Kaplan-Meier estimates as event probability.

Security Audit

Safe

v9 • 7/23/2026 Open versioned report

All 90 static findings are false positives caused by Markdown syntax, ordinary Python imports, parameter-grid terminology, the CIF acronym, and documentation links. The reviewed files contain educational scikit-survival guidance without command execution, dynamic code construction, reconnaissance, prompt injection, or suspicious network behavior.

Files scanned

2,589

Lines analyzed

Review items

False positives ignored

Risk Factors

⚡ Contains scripts (6)

references/competing-risks.md:19 references/competing-risks.md:21 references/data-handling.md:44-50 references/evaluation-metrics.md:307-312 SKILL.md:369-377 SKILL.md:380-384

⚙️ External commands (50)

🌐 Network access (3)

SKILL.md:354 SKILL.md:355 SKILL.md:357

No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.

Audited by: codex View Audit History →

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Academic citations (APA · BibTeX · CFF)

APA citation

davila7. (2026). scikit-survival security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-scikit-survival/audits/9

BibTeX citation

@techreport{davila7-davila7-scikit-survival-2026,
  author = {davila7},
  title = {scikit-survival security audit report (audit version 9)},
  institution = {Skillstore},
  year = {2026},
  number = {9},
  url = {https://skillstore.io/skills/davila7-scikit-survival/audits/9},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "scikit-survival security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
  - name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-scikit-survival/audits/9"
identifiers:
  - type: other
    value: "skillstore:davila7-scikit-survival:audit:9"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

davila7 Recommended Current

davila7-scikit-survival

Skillstore Score 76

Evidence Confidence High

Skillstore usage 8

Updated

2026-08-21

K-Dense-AI

k-dense-ai-scikit-survival

Skillstore Score 67

Evidence Confidence High

Skillstore usage 10

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Analyze Clinical Follow-up Data

Prepare censored outcomes, fit interpretable Cox models, and evaluate discrimination under censoring.

Compare Survival Algorithms

Benchmark Cox, forest, boosting, and SVM approaches with suitable survival metrics.

Teach Time-to-Event Modeling

Create structured examples covering preprocessing, model assumptions, validation, and competing risks.

Try These Prompts

Choose a First Model

Recommend a scikit-survival model for my dataset. I have [rows], [features], [event rate], and [censoring rate]. Explain the choice.

Prepare Survival Data

Design a preprocessing workflow for [dataset description]. Include outcome encoding, missing values, categorical features, scaling, splitting, and validation checks.

Evaluate Competing Models

Plan a fair comparison of [model list] using my training and test data. Select censoring-aware metrics and explain each metric.

Design a Competing-Risks Study

Design an advanced competing-risks analysis for [event types]. Address cumulative incidence, cause-specific modeling, validation, assumptions, and interpretation limits.

Best Practices

Validate event indicators, time values, event counts, and censoring rates before fitting models.
Keep preprocessing inside cross-validation and apply training transformations unchanged to test data.
Report multiple censoring-aware metrics with assumptions, time horizons, and uncertainty.

Avoid

Do not treat competing events as ordinary censoring when estimating event-specific probabilities.
Do not select models using the test set or leak preprocessing across validation folds.
Do not use Harrell's concordance index alone when censoring is high.