Roc Curve Plotter - Auto-activating skill for ML Training. Triggers on: roc curve plotter, roc curve plotter Part of the ML Training skill category.
35
3%
Does it follow best practices?
Impact
94%
1.04xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/07-ml-training/roc-curve-plotter/SKILL.mdsklearn ROC curve computation
Uses sklearn roc_curve
100%
100%
Uses sklearn AUC metric
100%
100%
Uses pip for installs
100%
100%
Python implementation
100%
100%
ROC plot saved
100%
100%
AUC in results.txt
100%
100%
Production code structure
0%
100%
AUC value reasonable
100%
100%
Multi-model ROC comparison pipeline
sklearn roc_curve used
100%
100%
sklearn AUC metric
100%
100%
sklearn classifiers
100%
100%
Python script produced
100%
100%
pip used for installs
100%
100%
Production code quality
0%
0%
Comparison plot saved
100%
100%
Report contains AUC values
100%
100%
AUC values reasonable
100%
100%
End-to-end ML training and evaluation
sklearn roc_curve
100%
100%
sklearn AUC metric
100%
100%
Data preparation step
100%
100%
Hyperparameter exploration
100%
100%
Experiment tracking
100%
100%
Best model ROC plot
100%
100%
Python implementation
100%
100%
pip used for installs
100%
100%
Production code structure
0%
20%
AUC value in valid range
100%
100%
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Table of Contents
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.