---
name: alibi-explainer-3ecc0458cc47
description: Alibi explainability skill for counterfactual explanations, anchors, and trust scores.
allowed-tools:
  - Read
  - Write
  - Bash
  - Glob
  - Grep
graph:
  domains: [domain:data-science]
  specializations: [specialization:data-science-ml]
  skillAreas: [skill-area:explainability-interpretation, skill-area:model-robustness-testing]
  roles: [role:data-scientist, role:ml-engineer]
  workflows: [workflow:ml-model-lifecycle]

---

# 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

```json
{
  "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

```json
{
  "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

```javascript
{
  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'
    }
  }
}
```


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering
Roles: Data Analyst, Data Scientist, ML / AI Engineer
Specialties: API
Tools: Alibi, Pytorch, Scikit Learn, Tensorflow
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: e72f151836be4ef00cda499803b590891bed4337c03e5a068531f6d08d794b04