Babysitter kubeflow-pipeline-executor

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

install

source · Clone the upstream repo

git clone https://github.com/a5c-ai/babysitter

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/data-science-ml/skills/kubeflow-pipeline-executor" ~/.claude/skills/a5c-ai-babysitter-kubeflow-pipeline-executor && rm -rf "$T"

manifest: 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
        }
      }
    }
  }
}