Kubeflow Pipeline Executor

DevOps & Security

kubeflow-pipeline-executor

Kubeflow Pipeline Executor is an Agent Skill for devops & security published in the a5c-ai/babysitter repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

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

View on GitHub ↗

QUICK START

How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

Open your project in Codex.
Copy and paste the prompt below.
Review files and risks before approving any action.

Prompt to paste

Inspect the Agent Skill at https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/data-science-ml/skills/kubeflow-pipeline-executor/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/kubeflow-pipeline-executor, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

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
        }
      }
    }
  }
}