# Foryourhealth111 Pixel Vibe Skills Explaining Machine Learning Models > Model Explainability Tool - Skill: `tomevault-io/foryourhealth111-pixel-vibe-skills-explaining-machine-learni` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/foryourhealth111-pixel-vibe-skills-explaining-machine-learni` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/foryourhealth111-pixel-vibe-skills-explaining-machine-learni/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/foryourhealth111-pixel-vibe-skills-explaining-machine-learni --- # Model Explainability Tool ## Positioning Treat this skill as an explicit/manual helper for interpretability work. ## When to Use Use this skill when: - Understand why a machine learning model made a specific prediction. - Identify the most important features influencing a model's output. - Debug model performance issues by identifying unexpected feature interactions. - Communicate model insights to non-technical stakeholders. - Ensure fairness and transparency in model predictions. ## Not For / Boundaries - Model training and hyperparameter search: use `scikit-learn` - Benchmark comparison and threshold selection: use `evaluating-machine-learning-models` - Leakage or prediction-time audits: use `ml-data-leakage-guard` ## Typical Outputs - Feature importance or attribution summaries - Local explanation workflow for a concrete prediction - Notes on caveats, instability, or misleading explanations ## Related Skills - `shap` for SHAP-specific workflows - `evaluating-machine-learning-models` when the question is whether the model is good enough --- > Source: [foryourhealth111-pixel/Vibe-Skills](https://github.com/foryourhealth111-pixel/Vibe-Skills) — distributed by [TomeVault](https://tomevault.io).