scikit-survival
This skill provides comprehensive guidance for survival analysis using scikit-survival in Python. It covers Cox models, ensemble methods, SVMs, and competing risks analysis with practical code examples. The documentation includes model selection flowcharts, data preparation steps, and performance evaluation metrics for time-to-event data.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install microck-ordinary-claude-skills-scikit-survival
Repository
Skill path: skills_all/claude-scientific-skills/scientific-skills/scikit-survival
This skill provides comprehensive guidance for survival analysis using scikit-survival in Python. It covers Cox models, ensemble methods, SVMs, and competing risks analysis with practical code examples. The documentation includes model selection flowcharts, data preparation steps, and performance evaluation metrics for time-to-event data.
Open repositoryBest for
Primary workflow: Research & Ops.
Technical facets: Data / AI.
Target audience: AI/ML teams looking for install-ready agent workflows..
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: Microck.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install scikit-survival into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/Microck/ordinary-claude-skills before adding scikit-survival to shared team environments
- Use scikit-survival for ai/ml workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.