ReddRadar
Agent skill
kubeflow-pipeline-executor
Kubeflow Pipelines skill for ML workflow orchestration, component management, and Kubernetes-native ML.
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/data-science-ml/skills/kubeflow-pipeline-executor
SKILL.md
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": {
"type": "string",
"enum": ["pending", "running", "succeeded", "failed", "skipped"]
},
"artifacts": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": { "type": "string" },
"uri": { "type": "string" },
"type": { "type": "string" }
}
}
},
"dashboardUrl": {
"type": "string"
}
}
}
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
}
}
}
}
}
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