Alibi explainer

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

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Alibi explainability skill for counterfactual explanations, anchors, and trust scores.

SKILL.md

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alibi-explainer

Overview

Alibi explainability skill for counterfactual explanations, anchors, trust scores, and advanced model interpretation techniques.

Capabilities

Counterfactual instance generation
Anchor explanations (rule-based)
Integrated gradients for deep learning
Kernel SHAP integration
Contrastive Explanation Method (CEM)
Trust scores for prediction confidence
Pertinent positives and negatives
Prototype and criticism selection

Target Processes

Model Interpretability and Explainability Analysis
Model Evaluation and Validation Framework

Tools and Libraries

Alibi
Alibi Detect
TensorFlow/PyTorch
scikit-learn

Input Schema

{
  "type": "object",
  "required": ["modelPath", "explainerType", "instancePath"],
  "properties": {
    "modelPath": {
      "type": "string",
      "description": "Path to the trained model"
    },
    "explainerType": {
      "type": "string",
      "enum": ["counterfactual", "anchor", "integrated_gradients", "cem", "trust_score", "prototype"],
      "description": "Type of Alibi explainer to use"
    },
    "instancePath": {
      "type": "string",
      "description": "Path to instance(s) to explain"
    },
    "counterfactualConfig": {
      "type": "object",
      "properties": {
        "targetClass": { "type": "integer" },
        "maxIterations": { "type": "integer" },
        "lambda": { "type": "number" },
        "featureRange": { "type": "object" }
      }
    },
    "anchorConfig": {
      "type": "object",
      "properties": {
        "threshold": { "type": "number" },
        "coverageSamples": { "type": "integer" },
        "beamSize": { "type": "integer" }
      }
    },
    "cemConfig": {
      "type": "object",
      "properties": {
        "mode": { "type": "string", "enum": ["PP", "PN"] },
        "kappaMin": { "type": "number" },
        "kappaMax": { "type": "number" }
      }
    },
    "trainingDataPath": {
      "type": "string",
      "description": "Path to training data (required for some explainers)"
    }
  }
}

Output Schema

{
  "type": "object",
  "required": ["status", "explanations"],
  "properties": {
    "status": {
      "type": "string",
      "enum": ["success", "error"]
    },
    "explanations": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "instanceId": { "type": "string" },
          "originalPrediction": { "type": "string" },
          "explanation": { "type": "object" }
        }
      }
    },
    "counterfactuals": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "instanceId": { "type": "string" },
          "counterfactual": { "type": "object" },
          "targetClass": { "type": "string" },
          "changedFeatures": { "type": "array" }
        }
      }
    },
    "anchors": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "instanceId": { "type": "string" },
          "rules": { "type": "array", "items": { "type": "string" } },
          "precision": { "type": "number" },
          "coverage": { "type": "number" }
        }
      }
    },
    "trustScores": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "instanceId": { "type": "string" },
          "score": { "type": "number" },
          "closestClass": { "type": "string" }
        }
      }
    }
  }
}

Usage Example

{
  kind: 'skill',
  title: 'Generate counterfactual explanations',
  skill: {
    name: 'alibi-explainer',
    context: {
      modelPath: 'models/loan_classifier.pkl',
      explainerType: 'counterfactual',
      instancePath: 'data/rejected_applications.csv',
      counterfactualConfig: {
        targetClass: 1,
        maxIterations: 1000,
        lambda: 0.1
      },
      trainingDataPath: 'data/train.csv'
    }
  }
}