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scikit-survival

Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.

Quality

86%

Does it follow best practices?

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Securityby

Passed

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Quality

Content

72%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is highly actionable and well-structured with real reference files, but it is somewhat verbose and its workflows lack explicit validation/error-recovery checkpoints. Tightening redundant explanations and adding verification steps would raise the score.

Suggestions

Trim redundant explanations (e.g., the survival analysis overview and censoring recap) and remove the duplicate 'Quick Reference: Key Imports' section or fold its unique entries into the workflows that use them.

Add explicit validation/error-recovery checkpoints to each workflow (e.g., 'Verify y_train is passed to concordance_index_ipcw; if it raises, check event/time alignment') to support batch and model-comparison operations.

Ensure every workflow is self-contained and executable: import all referenced functions (e.g. as_concordance_index_ipcw_scorer in Workflows 2 and 3) so the snippets run without hidden dependencies.

Dimension	Reasoning	Score
Conciseness	Mostly efficient and code-driven, but several explanatory passages restate concepts Claude already knows (e.g., overview of survival analysis, censoring explanations) and the 'Quick Reference: Key Imports' section duplicates imports already shown in workflows.	3 / 5
Actionability	Fully executable, copy-paste-ready code across four end-to-end workflows plus inline snippets, with specific class names, metric functions, and parameter grids covering the common cases.	5 / 5
Workflow Clarity	Workflows are numbered and sequenced with inline code, but lack explicit validation checkpoints or error-recovery feedback loops, and some workflows reference functions (e.g. as_concordance_index_ipcw_scorer) without importing them.	3 / 5
Progressive Disclosure	Clear overview in SKILL.md with six well-signaled, one-level-deep reference files (all verified to exist) and a dedicated 'Reference Files' index section that makes navigation easy.	5 / 5
	Total	16 / 20 Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is strong: it explicitly states what the skill does and when to use it, with concrete actions and natural trigger terms. Minor room to add a few more colloquial synonyms, but it is comprehensive and clearly differentiated.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions across the domain — 'fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks' — giving comprehensive coverage.	5 / 5
Completeness	Clearly answers both 'what' (comprehensive toolkit for survival analysis and time-to-event modeling) and 'when' with an explicit 'Use this skill when working with...' trigger clause and concrete trigger phrases.	5 / 5
Trigger Term Quality	Strong natural keywords ('survival analysis', 'censored survival data', 'Cox models', 'concordance index', 'Brier score', 'competing risks'), but lacks some common synonyms or shorthand variations a user might say.	4 / 5
Distinctiveness Conflict Risk	A clear niche (scikit-survival survival analysis) with distinct, specialized triggers that are unlikely to conflict with other skills.	5 / 5
	Total	19 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/scikit-survival/SKILL.md
Revision: 5ce2258

Reviewed: 27 days ago

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scikit-survival

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