Explaining Machine Learning Models Agent 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.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/foryourhealth111-pixel/Vibe-Skills --skill explaining-machine-learning-modelsWhat comes with it
20 069 bytes besides the instructionWhat it tells the agent to use
found in the instruction textThe instruction itself
6 sections, as written by the authorModel 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
shapfor SHAP-specific workflowsevaluating-machine-learning-modelswhen the question is whether the model is good enough
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How to use it
Take foryourhealth111-pixel/explaining-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.