/evaluating-machine-learning-models

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.

foryourhealth111-pixel@foryourhealth111-pixel

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vibe-skills

2.7k200 skills8 agents3 commands

Install

$ npx -y skills add foryourhealth111-pixel/Vibe-Skills --skill evaluating-machine-learning-models --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/evaluating-machine-learning-models

Context preview

The summary Claude sees to decide when to auto-load this 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.

SKILL.md

evaluating-machine-learning-models.SKILL.md

name: evaluating-machine-learning-models
description: |
  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.
allowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version: 1.0.0
author: Jeremy Longshore <jeremy@intentsolutions.io>
license: MIT

Model 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-learn` for classical modeling or `ml-pipeline-workflow` for end-to-end workflow ownership
Engineering features: use `preprocessing-data-with-automated-pipelines`
Checking train/test contamination: use `ml-data-leakage-guard`