Automated summary

Computes SHAP values for a trained model and produces feature-attribution plots (summary, bar, waterfall, force, dependence, interaction).

Strengths

Supports multiple explainer families: TreeExplainer, DeepExplainer, KernelExplainer, linear and gradient
Generates a configurable set of plots (summary, bar, waterfall, force, dependence, interaction) with a max-features cap
Returns structured output: SHAP values path, expected value, ranked feature importance and top interactions
Includes cohort-based analysis and sampling controls (numSamples, backgroundSamples) to bound compute cost

Limitations

Assumes the model and data already exist locally (modelPath, dataPath); it does not train models
Does not detail handling of models unsupported by the chosen explainer or the exact serialization formats required
KernelExplainer is expensive on large datasets, and explanation quality depends heavily on the background sample choice

When to use it

Use it when a trained model needs feature-level explanations with shareable plots for a review or report.

When not to use it

Avoid it if you need to train, tune or deploy a model, or if you require advanced fairness metrics that this skill does not provide.

Examples

Explain an XGBoost model

Use the shap-explainer skill on models/xgboost_model.pkl with data/test.csv, explainerType 'tree', numSamples 1000, backgroundSamples 100, and generate summary, bar and dependence plots into explanations/ with maxFeatures 20.

Model-agnostic explanations

Run shap-explainer with explainerType 'kernel' on my pickled model at artifacts/model.pkl and features at data/holdout.csv; give me the top 15 features by importance and a waterfall plot for the first 5 rows.

Feature interactions analysis

Compute SHAP interaction values for models/lgbm.txt on data/val.csv using TreeExplainer and report the top feature interactions plus a summary plot.

name: shap-explainer description: SHAP-based model explainability skill for feature attribution, summary plots, and interaction analysis. allowed-tools:

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Grep graph: domains: [domain:data-science] specializations: [specialization:data-science-ml] skillAreas: [skill-area:explainability-interpretation, skill-area:model-explainability-testing] roles: [role:data-scientist, role:ml-engineer] workflows: [workflow:ml-model-lifecycle]

shap-explainer

Overview

SHAP-based model explainability skill for feature attribution, summary plots, interaction analysis, and model interpretation.

Capabilities

TreeExplainer for tree-based models (XGBoost, LightGBM, Random Forest)
DeepExplainer for neural networks
KernelExplainer for model-agnostic explanations
Summary, dependence, and force plots
Interaction value computation
Cohort-based analysis
Waterfall and bar plots
Expected value analysis

Target Processes

Model Interpretability and Explainability Analysis
Model Evaluation and Validation Framework
A/B Testing Framework for ML Models

Tools and Libraries

SHAP
matplotlib
numpy

Input Schema

{
  "type": "object",
  "required": ["modelPath", "dataPath", "explainerType"],
  "properties": {
    "modelPath": {
      "type": "string",
      "description": "Path to the trained model"
    },
    "dataPath": {
      "type": "string",
      "description": "Path to data for explanation"
    },
    "explainerType": {
      "type": "string",
      "enum": ["tree", "deep", "kernel", "linear", "gradient"],
      "description": "Type of SHAP explainer to use"
    },
    "analysisConfig": {
      "type": "object",
      "properties": {
        "numSamples": { "type": "integer" },
        "backgroundSamples": { "type": "integer" },
        "featureNames": { "type": "array", "items": { "type": "string" } },
        "outputIndex": { "type": "integer" }
      }
    },
    "plotConfig": {
      "type": "object",
      "properties": {
        "plotTypes": {
          "type": "array",
          "items": { "type": "string", "enum": ["summary", "bar", "waterfall", "force", "dependence", "interaction"] }
        },
        "maxFeatures": { "type": "integer" },
        "outputDir": { "type": "string" }
      }
    }
  }
}

Output Schema

{
  "type": "object",
  "required": ["status", "shapValues"],
  "properties": {
    "status": {
      "type": "string",
      "enum": ["success", "error"]
    },
    "shapValues": {
      "type": "string",
      "description": "Path to SHAP values file"
    },
    "expectedValue": {
      "type": "number"
    },
    "featureImportance": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "feature": { "type": "string" },
          "importance": { "type": "number" },
          "rank": { "type": "integer" }
        }
      }
    },
    "plots": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": { "type": "string" },
          "path": { "type": "string" }
        }
      }
    },
    "interactions": {
      "type": "object",
      "description": "Top feature interactions"
    }
  }
}

Usage Example

{
  kind: 'skill',
  title: 'Generate SHAP explanations',
  skill: {
    name: 'shap-explainer',
    context: {
      modelPath: 'models/xgboost_model.pkl',
      dataPath: 'data/test.csv',
      explainerType: 'tree',
      analysisConfig: {
        numSamples: 1000,
        backgroundSamples: 100
      },
      plotConfig: {
        plotTypes: ['summary', 'bar', 'dependence'],
        maxFeatures: 20,
        outputDir: 'explanations/'
      }
    }
  }
}

SHAP Model Explainability Skill

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