Arize observability

Skill a5c-ai/babysitter/library/specializations/data-science-ml/skills/arize-observability

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 arize-observability

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What its author says it does

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Arize AI skill for production ML monitoring, embedding drift, and performance analysis.

SKILL.md

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arize-observability

Overview

Arize AI skill for production ML monitoring, embedding drift detection, and comprehensive performance analysis.

Capabilities

Production data logging
Embedding drift detection for NLP/CV models
Performance monitoring dashboards
Root cause analysis
Slice and dice analysis for segments
Bias monitoring
A/B test monitoring
Custom metrics and monitors

Target Processes

Model Performance Monitoring and Drift Detection
ML System Observability and Incident Response
Model Evaluation and Validation Framework

Tools and Libraries

Arize AI SDK
pandas
numpy

Input Schema

{
  "type": "object",
  "required": ["action"],
  "properties": {
    "action": {
      "type": "string",
      "enum": ["log", "monitor", "analyze", "alert-config", "compare"],
      "description": "Arize action to perform"
    },
    "logConfig": {
      "type": "object",
      "properties": {
        "modelId": { "type": "string" },
        "modelVersion": { "type": "string" },
        "modelType": { "type": "string", "enum": ["score_categorical", "regression", "ranking"] },
        "environment": { "type": "string", "enum": ["training", "validation", "production"] },
        "dataPath": { "type": "string" },
        "predictionIdColumn": { "type": "string" },
        "timestampColumn": { "type": "string" },
        "featureColumns": { "type": "array", "items": { "type": "string" } },
        "embeddingColumns": { "type": "array", "items": { "type": "string" } },
        "predictionColumn": { "type": "string" },
        "actualColumn": { "type": "string" }
      }
    },
    "monitorConfig": {
      "type": "object",
      "properties": {
        "metrics": { "type": "array", "items": { "type": "string" } },
        "thresholds": { "type": "object" },
        "schedule": { "type": "string" }
      }
    },
    "analysisConfig": {
      "type": "object",
      "properties": {
        "analysisType": { "type": "string", "enum": ["drift", "performance", "fairness", "data_quality"] },
        "timeRange": { "type": "object" },
        "segments": { "type": "array", "items": { "type": "string" } }
      }
    }
  }
}

Output Schema

{
  "type": "object",
  "required": ["status", "action"],
  "properties": {
    "status": {
      "type": "string",
      "enum": ["success", "error"]
    },
    "action": {
      "type": "string"
    },
    "logId": {
      "type": "string"
    },
    "dashboardUrl": {
      "type": "string"
    },
    "analysis": {
      "type": "object",
      "properties": {
        "overallScore": { "type": "number" },
        "driftMetrics": { "type": "object" },
        "performanceMetrics": { "type": "object" },
        "topIssues": { "type": "array" },
        "recommendations": { "type": "array", "items": { "type": "string" } }
      }
    },
    "alerts": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": { "type": "string" },
          "severity": { "type": "string" },
          "triggered": { "type": "boolean" }
        }
      }
    }
  }
}

Usage Example

{
  kind: 'skill',
  title: 'Log production predictions to Arize',
  skill: {
    name: 'arize-observability',
    context: {
      action: 'log',
      logConfig: {
        modelId: 'fraud-detector',
        modelVersion: '2.0.0',
        modelType: 'score_categorical',
        environment: 'production',
        dataPath: 'data/production_predictions.parquet',
        predictionIdColumn: 'request_id',
        timestampColumn: 'timestamp',
        featureColumns: ['amount', 'merchant_category', 'hour'],
        predictionColumn: 'fraud_probability',
        actualColumn: 'is_fraud'
      }
    }
  }
}