/explaining-machine-learning-models

Explain trained machine learning models through feature attribution, local explanations, and behavior summaries. Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.

foryourhealth111-pixel@foryourhealth111-pixel

GitHub

From plugin

vibe-skills

2.7k200 skills8 agents3 commands

Install

$ npx -y skills add foryourhealth111-pixel/Vibe-Skills --skill explaining-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/explaining-machine-learning-models

Context preview

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

Explain trained machine learning models through feature attribution, local explanations, and behavior summaries. Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.

SKILL.md

explaining-machine-learning-models.SKILL.md

name: explaining-machine-learning-models
description: |
  Explain trained machine learning models through feature attribution, local explanations, and behavior summaries.
  Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.
allowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version: 1.0.0
author: Jeremy Longshore <jeremy@intentsolutions.io>
license: MIT

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`