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.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill scikit-survivalOverall
score
91%
Does it follow best practices?
Validation for skill structure
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that hits all the marks. It provides specific capabilities (model types, evaluation metrics), uses natural domain terminology that practitioners would search for, explicitly states when to use it, and occupies a clear niche that won't conflict with general data science or machine learning skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks.' These are precise, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both what ('Comprehensive toolkit for survival analysis and time-to-event modeling') AND when with explicit 'Use this skill when...' clause covering multiple trigger scenarios including data types, model types, and evaluation metrics. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'survival analysis', 'time-to-event', 'censored survival data', 'Cox models', 'Random Survival Forests', 'concordance index', 'Brier score', 'competing risks', 'scikit-survival'. These are the exact terms domain practitioners use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche - survival analysis is a specialized statistical domain. The specific mention of 'scikit-survival library', model types (Cox, RSF, Survival SVM), and metrics (concordance index, Brier score) make it clearly distinguishable from general ML or statistics skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured, highly actionable skill for survival analysis with scikit-survival. Its strengths include comprehensive executable code examples, clear workflow sequences, and excellent progressive disclosure through reference files. The main weakness is moderate verbosity in introductory sections that explain concepts Claude already knows, though this doesn't significantly detract from the skill's utility.
Suggestions
Remove or condense the 'Overview' paragraph explaining what survival analysis is - Claude already knows this concept
Trim the 'When to Use This Skill' section significantly as it largely restates obvious use cases that can be inferred from the skill title and description
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill contains some unnecessary explanations (e.g., 'Survival analysis aims to establish connections between covariates...') and the 'When to Use This Skill' section largely duplicates information Claude could infer. However, the code examples and reference structure are reasonably efficient. | 2 / 3 |
Actionability | Provides fully executable, copy-paste ready code examples throughout. All workflows include complete imports, data loading, model fitting, and evaluation steps. The quick reference imports section is particularly actionable. | 3 / 3 |
Workflow Clarity | Multi-step workflows are clearly numbered and sequenced. The model selection decision tree provides explicit guidance. Workflows include validation steps (evaluation metrics) and the 'Common Pitfalls to Avoid' section addresses error prevention. | 3 / 3 |
Progressive Disclosure | Excellent structure with clear overview content and well-signaled one-level-deep references to detailed files (cox-models.md, ensemble-models.md, etc.). Content is appropriately split between the main skill and reference files with clear navigation. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 13 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
metadata_version | 'metadata.version' is missing | Warning |
body_output_format | No obvious output/return/format terms detected; consider specifying expected outputs | Warning |
Total | 13 / 16 Passed | |
- Reviewed
Table of Contents
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