# Yeloo Vibe Skills Evaluating Machine Learning Models > Model Evaluation Suite - Skill: `tomevault-io/yeloo-vibe-skills-evaluating-machine-learning-models` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/yeloo-vibe-skills-evaluating-machine-learning-models` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/yeloo-vibe-skills-evaluating-machine-learning-models/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/yeloo-vibe-skills-evaluating-machine-learning-models --- # 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 `training-machine-learning-models` - Engineering features: use `engineering-features-for-machine-learning` - 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 - `confusion-matrix-generator` for class-level error breakdowns - `scientific-reporting` when the evaluation must become a deliverable --- > Source: [yeloo/Vibe-Skills](https://github.com/yeloo/Vibe-Skills) — distributed by [TomeVault](https://tomevault.io).