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SHAP Model Explainer

Explain why a machine-learning model made its predictions, with validated SHAP feature attributions and plots.

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
10 files

What it does

This skill teaches an AI agent how to use SHAP, a standard method for showing which input features pushed a model's prediction up or down. Once installed, the agent follows a careful workflow: it states exactly what output is being explained, picks a suitable explainer and background dataset, checks that the numbers add up to the model's actual output, and then produces summary and per-prediction charts. It also includes a script that builds a sample tabular report using a built-in dataset.

Great for

  • Data scientists who need to explain a trained classifier or regressor to colleagues or stakeholders
  • Students learning how feature attribution works on tabular models
  • Analysts auditing whether a fitted model relies on sensible inputs, using held-out rows

Skip it if

  • Proving causation or fairness: the skill itself states SHAP describes model behaviour under a chosen background, not causality, fairness, or scientific mechanism
  • Explaining a model you have not validated or cannot reproduce, since the skill requires a fixed, evaluated model
  • Loading untrusted pickle, joblib or model files, which the skill itself warns can execute code

Try asking

  • “Explain the predictions of my random forest on held-out test rows and make a beeswarm plot of the top features”
  • “Show a waterfall chart for one patient's prediction and check that SHAP values add up to the predicted probability”
  • “Build a SHAP tabular report for my classifier using the included template script”
Step-by-step guide: how to use SHAP Model ExplainerInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 10 original files, the MIT licence and an attribution note.

    shap.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip shap.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip shap.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Always name the explained output (probability vs raw margin) and the background rows before reading any plot.
  • Use held-out rows for explanations and background rows from the training population; do not treat SHAP as proof of cause.
  • Run the bundled additivity check whenever you change preprocessing, model version or output index, and do not silence failures.

What's inside

  • references/data-maskers.md
  • references/explainers.md
  • references/migration.md
  • references/modalities.md
  • references/plots.md
  • references/theory.md
  • references/troubleshooting.md
  • references/workflows.md
  • scripts/tabular_report.py
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.