Shap explainer

Skill a5c-ai/babysitter/library/specializations/data-science-ml/skills/shap-explainer

Babysitter enforces obedience on agentic workforces and enables them to manage extremely complex tasks and workflows through deterministic, hallucination-free self-orchestration

Install

npx -y skills add a5c-ai/babysitter --skill shap-explainer

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What its author says it does

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

SKILL.md

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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/'
      }
    }
  }
}