Lime explainer

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

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LIME-based local explanation skill for individual predictions across tabular, text, and image data.

SKILL.md

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lime-explainer

Overview

LIME-based local explanation skill for individual predictions across tabular, text, and image data using Local Interpretable Model-agnostic Explanations.

Capabilities

Tabular data explanations
Text classification explanations
Image classification explanations
Submodular pick for representative samples
Custom distance metrics
Kernel width tuning
Feature discretization
Local surrogate model analysis

Target Processes

Model Interpretability and Explainability Analysis
Model Evaluation and Validation Framework

Tools and Libraries

LIME
scikit-learn
numpy
PIL/Pillow (for images)

Input Schema

{
  "type": "object",
  "required": ["modelPath", "dataType", "instancePath"],
  "properties": {
    "modelPath": {
      "type": "string",
      "description": "Path to the trained model or prediction function"
    },
    "dataType": {
      "type": "string",
      "enum": ["tabular", "text", "image"],
      "description": "Type of data to explain"
    },
    "instancePath": {
      "type": "string",
      "description": "Path to instance(s) to explain"
    },
    "tabularConfig": {
      "type": "object",
      "properties": {
        "trainingDataPath": { "type": "string" },
        "featureNames": { "type": "array", "items": { "type": "string" } },
        "categoricalFeatures": { "type": "array", "items": { "type": "integer" } },
        "classNames": { "type": "array", "items": { "type": "string" } }
      }
    },
    "textConfig": {
      "type": "object",
      "properties": {
        "classNames": { "type": "array", "items": { "type": "string" } },
        "splitExpression": { "type": "string" }
      }
    },
    "imageConfig": {
      "type": "object",
      "properties": {
        "segmenter": { "type": "string", "enum": ["quickshift", "slic", "felzenszwalb"] },
        "hideColor": { "type": "string" },
        "numSamples": { "type": "integer" }
      }
    },
    "explainerConfig": {
      "type": "object",
      "properties": {
        "numFeatures": { "type": "integer" },
        "numSamples": { "type": "integer" },
        "kernelWidth": { "type": "number" }
      }
    }
  }
}

Output Schema

{
  "type": "object",
  "required": ["status", "explanations"],
  "properties": {
    "status": {
      "type": "string",
      "enum": ["success", "error"]
    },
    "explanations": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "instanceId": { "type": "string" },
          "predictedClass": { "type": "string" },
          "predictionProbability": { "type": "number" },
          "features": {
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "feature": { "type": "string" },
                "weight": { "type": "number" },
                "contribution": { "type": "string" }
              }
            }
          },
          "localAccuracy": { "type": "number" }
        }
      }
    },
    "visualizations": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "instanceId": { "type": "string" },
          "plotPath": { "type": "string" }
        }
      }
    }
  }
}

Usage Example

{
  kind: 'skill',
  title: 'Generate LIME explanations for predictions',
  skill: {
    name: 'lime-explainer',
    context: {
      modelPath: 'models/classifier.pkl',
      dataType: 'tabular',
      instancePath: 'data/instances_to_explain.csv',
      tabularConfig: {
        trainingDataPath: 'data/train.csv',
        featureNames: ['age', 'income', 'credit_score'],
        categoricalFeatures: [0, 2],
        classNames: ['reject', 'approve']
      },
      explainerConfig: {
        numFeatures: 10,
        numSamples: 5000
      }
    }
  }
}