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RHEcosystemAppEng
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pipeline-manage

Automate creation, submission, scheduling, monitoring, and deletion of Kubeflow Pipelines 2.0 on OpenShift AI.

48|31|Updated Feb 2, 2026
Search Tags:#data processing#pipeline management#openshift#kubeflow#rhoai#data science pipelines
One-click install
npx skills add https://github.com/RHEcosystemAppEng/agentic-plugins --skill pipeline-manage
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pipeline-manage
Source: https://github.com/RHEcosystemAppEng/agentic-plugins/tree/main/rh-ai-engineer/skills/pipeline-manage
Command: npx skills add https://github.com/RHEcosystemAppEng/agentic-plugins --skill pipeline-manage

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires resources_create_or_update, resources_list, resources_get, resources_delete, events_list, pods_list, pods_log, list_data_science_projects, get_pipeline_server, delete_pipeline_server, list_resources, get_resource, list_resource_names, resource_status, diagnose_resource, list_data_connections, project_summary, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the creation, execution, and monitoring of Data Science Pipelines on OpenShift AI, enabling efficient lifecycle management without manual oversight.

Core Features & Use Cases

  • Pipeline Creation and Management: Setup, submit, schedule, monitor, and delete data science pipelines.
  • Integration: Seamlessly integrates with OpenShift AI and RHOAI Data Science Projects.
  • Use Case: Imagine you have a complex data science pipeline with multiple steps. This Skill allows you to automate the process of running and managing the pipeline, from submission to deletion, while providing detailed monitoring and logging.

Quick Start

Use the /pipeline-manage skill to run a pipeline defined in 'pipeline.yaml'.

Frequently Asked Questions about pipeline-manage

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate Kubeflow Pipelines 2.0 execution and monitoring on OpenShift AI?▼

Automate Kubeflow Pipelines 2.0 execution by submitting and scheduling data science pipelines directly on OpenShift AI, enabling lifecycle management and detailed monitoring without manual oversight.

What is data science pipeline lifecycle management on RHOAI?▼

Data science pipeline lifecycle management on RHOAI involves the automated creation, submission, scheduling, monitoring, and deletion of pipelines to run complex multi-step data processing efficiently.

Do I need a Data Science Project to manage pipelines on OpenShift AI?▼

Yes, you need an active RHOAI Data Science Project and access to OpenShift resources. The pipeline management process validates your data science project environment before configuring the pipeline server.

Can I delete a pipeline server and related resources on OpenShift AI?▼

Yes, you can delete a pipeline server and related resources. The lifecycle management process includes tools to delete pipeline servers and clean up OpenShift resources used during data processing.

How do I diagnose pipeline pods not working in OpenShift AI?▼

To diagnose pipeline pods not working in OpenShift AI, you can retrieve pod logs and resource statuses to troubleshoot data science pipeline execution issues and identify failures within the pipeline server environment.

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