Evaluating Machine Learning Models Agent Skill
| Evaluate trained machine learning models with the right metrics and comparison logic. Use for benchmark review, threshold selection, calibration, validation, and model comparison; not for feature engineering or leakage auditing.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/foryourhealth111-pixel/Vibe-Skills --skill evaluating-machine-learning-modelsWhat comes with it
15 466 bytes besides the instructionWhat it tells the agent to use
found in the instruction textThe instruction itself
6 sections, as written by the authorModel Evaluation Suite
Use this skill when the model exists and the question is whether it is good enough.
Overview
This skill focuses on choosing and interpreting the right evaluation metrics for the problem, then comparing candidate models or thresholds.
When to Use This Skill
- Comparing candidate models with consistent metrics
- Reviewing precision/recall/F1/AUC, regression error, calibration, or ranking quality
- Stress-testing validation strategy before deployment or publication
Not For / Boundaries
- Building the training pipeline itself: use
scikit-learnfor classical modeling orml-pipeline-workflowfor end-to-end workflow ownership - Engineering features: use
preprocessing-data-with-automated-pipelines - Checking train/test contamination: use
ml-data-leakage-guard
Typical Outputs
- Metric suite recommendations
- Model comparison tables
- Notes on threshold tradeoffs, calibration, and validation weaknesses
Related Skills
scikit-learnfor class-level error breakdowns and confusion matricesscientific-reportingwhen the evaluation must become a deliverable
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How to use it
Take foryourhealth111-pixel/evaluating-machine-learning-models from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.