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Alibi explainability skill for counterfactual explanations, anchors, and trust scores.
---
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'
}
}
}
```
---
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'
}
}
}
```
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.