kubeflow-pipeline-executor

name	kubeflow-pipeline-executor
description	Kubeflow Pipelines skill for ML workflow orchestration, component management, and Kubernetes-native ML.
allowed-tools	["Read","Write","Bash","Glob","Grep"]
graph	{"domains":["domain:data-science"],"specializations":["specialization:data-science-ml"],"skillAreas":["skill-area:ci-cd-ml-pipelines","skill-area:machine-learning-frameworks"],"roles":["role:ml-ops-engineer","role:ml-engineer"],"workflows":["workflow:ml-model-lifecycle"]}

kubeflow-pipeline-executor

Overview

Kubeflow Pipelines skill for ML workflow orchestration, component management, and Kubernetes-native ML operations.

Capabilities

• Pipeline definition and compilation
• Component creation and reuse
• Pipeline versioning
• Artifact tracking and lineage
• Kubernetes resource management
• Pipeline scheduling and triggering
• Caching for component outputs
• Visualization of pipeline runs

Target Processes

• Model Training Pipeline
• Distributed Training Orchestration
• Model Deployment Pipeline
• ML Model Retraining Pipeline

Tools and Libraries

• Kubeflow Pipelines
• KFP SDK (v2)
• Kubernetes
• Argo Workflows

Input Schema

{
  "type": "object",
  "required": ["action"],
  "properties": {
    "action": {
      "type": "string",
      "enum": ["compile", "run", "schedule", "list", "get-run", "delete"],
      "description": "KFP action to perform"
    },
    "pipelinePath": {
      "type": "string",
      "description": "Path to pipeline definition file"
    },
    "pipelineConfig": {
      "type": "object",
      "properties": {
        "name": { "type": "string" },
        "description": { "type": "string" },
        "parameters": { "type": "object" }
      }
    },
    "runConfig": {
      "type": "object",
      "properties": {
        "experimentName": { "type": "string" },
        "runName": { "type": "string" },
        "arguments": { "type": "object" }
      }
    },
    "scheduleConfig": {
      "type": "object",
      "properties": {
        "cron": { "type": "string" },
        "maxConcurrency": { "type": "integer" },
        "enabled": { "type": "boolean" }
      }
    }
  }
}

Output Schema

{
  "type": "object",
  "required": ["status", "action"],
  "properties": {
    "status": {
      "type": "string",
      "enum": ["success", "error", "running"]
    },
    "action": {
      "type": "string"
    },
    "pipelineId": {
      "type": "string"
    },
    "runId": {
      "type": "string"
    },
    "runStatus" 
       
           
    
     
       
       
         
         
              
              
              
        
      
    
     
       
    
  

Usage Example

{
  kind: 'skill',
  title: 'Run ML training pipeline',
  skill: {
    name: 'kubeflow-pipeline-executor',
    context: {
      action: 'run',
      pipelinePath: 'pipelines/training_pipeline.py',
      runConfig: {
        experimentName: 'model-training',
        runName: 'training-run-v1',
        arguments: {
          dataPath: 'gs://bucket/data',
          modelPath: 'gs://bucket/models',
          epochs: 100
        }
      }
    }
  }
}