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scikit-survival Survival Analysis Workflow

An expert skill for building, tuning, evaluating, and reporting right-censored and competing-risk survival models with scikit-survival, without data leakage.

Data & AnalyticsAdvanced★ 33,030⑂ 3,248AI score 8/10Last updated: Aug 9, 2026

What it does

  • Enforces correct outcome construction: structured arrays via Surv.from_arrays (boolean event first, float time second), plus integer status coding (0=censored, 1..K=causes) for competing risks.
  • Provides a leakage-safe workflow: split before any learned transform, keep imputers/encoders/scalers inside a Pipeline, and tune with nested CV or a truly untouched holdout.
  • Model selection guidance across Cox PH, Coxnet (LASSO/elastic-net), IPCRidge (AFT), Random Survival Forest / ExtraSurvivalTrees, Gradient Boosting, and Survival SVMs — including what each predict actually returns.
  • Explicit metric contracts: Uno's C and cumulative/dynamic AUC take higher-is-riskier scores; Brier/IBS take survival probabilities shaped (n_test, n_times); censoring distributions are fit on training outcomes only.
  • Competing-risk CIF computation and a warning against the common 1 - Kaplan-Meier mistake.
  • Bundled offline CLIs for CSV validation, training, metric evaluation, CIF, and Markdown report generation.

Who it's for

  • Data scientists and biostatisticians working with clinical, epidemiological, or trial data.
  • ML engineers modeling time-to-event outcomes such as churn, hardware failure, or relapse.
  • Anyone who must document a defensible, reproducible evaluation protocol for review or publication.

Example uses

  1. "Train a Cox PH model on this patient CSV and produce a report with Uno's C, dynamic AUC, and IBS" → validate → train/tune → evaluate → model-report.md.
  2. "Use Coxnet for feature selection on high-dimensional omics data and plot survival curves" → set fit_baseline_model=True and tune l1_ratio/alpha with nested CV.
  3. "Compute cause-specific cumulative incidence when patients die of multiple competing causes" → integer status coding, then cumulative_incidence_competing_risks.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/scikit-survival/SKILL.md

What I want: (describe your task here)

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↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on AI Skill Mart.
Copy the skills/scikit-survival folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/scikit-survival/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git && mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/scikit-survival ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal and go to your working directory.
  2. Clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Create the skills folder: mkdir -p ~/.claude/skills
  4. Copy the skill: cp -r scientific-agent-skills/skills/scikit-survival ~/.claude/skills/
  5. Set up Python (requires uv and Python 3.11+): uv venv --python 3.11 && source .venv/bin/activate
  6. Install the verified stack: uv pip install "scikit-survival==0.28.0" "scikit-learn==1.9.0" (see SKILL.md for the full pinned list).
  7. Restart Claude Code and ask for a "survival analysis with scikit-survival" task to confirm the skill activates.
  8. Note: upstream scikit-survival is GPL-3.0-or-later; review licensing before redistribution.
View source on GitHub ↗License: MIT