/pipeline-manage Skill

Create, run, schedule, and monitor Data Science Pipelines (Kubeflow Pipelines 2.0) on Red Hat OpenShift AI. Handles the full pipeline lifecycle: verifying or setting up the pipeline server (DSPA), submitting pipeline runs from YAML definitions, scheduling recurring runs with cron expressions, monitoring run status with step-level progress, viewing pipeline step logs, and deleting pipeline resources with proper warnings.

Prerequisites

Required MCP Server: rhoai (RHOAI MCP Server)

Required MCP Tools (from rhoai):

• list_data_science_projects - Validate namespace is an RHOAI Data Science Project
• get_pipeline_server - Check pipeline server (DSPA) status in a project
• create_pipeline_server - Create DSPA with S3 storage configuration
• delete_pipeline_server - Delete pipeline server and all pipeline infrastructure
• list_resources - List pipeline resources in a namespace (resource_type="pipelines")
• get_resource - Get pipeline resource details (resource_type="pipeline")
• list_resource_names - Token-efficient pipeline name listing (resource_type="pipelines")
• resource_status - Quick pipeline status check (resource_type="pipeline")
• diagnose_resource - Full diagnostic for a pipeline (resource_type="pipeline")
• list_data_connections - Verify S3 data connections for pipeline artifact storage
• project_summary - Project overview including pipeline status

Required MCP Server: openshift (OpenShift MCP Server)

Required MCP Tools (from openshift):

• resources_create_or_update (from openshift) - Create PipelineRun and ScheduledWorkflow CRs
• resources_list (from openshift) - List PipelineRun resources by apiVersion/kind
• resources_get (from openshift) - Get PipelineRun status and task details
• resources_delete (from openshift) - Delete pipeline run resources
• events_list (from openshift) - Check pipeline pod events for errors
• pods_list (from openshift) - List pipeline step pods
• pods_log (from openshift) - Retrieve pipeline step container logs

Common prerequisites (KUBECONFIG, OpenShift+RHOAI cluster, verification protocol): See skill-conventions.md.

Additional cluster requirements:

• Target namespace is an RHOAI Data Science Project (label: opendatahub.io/dashboard: "true")
• Pipeline server requires S3-compatible object storage (a data connection must exist in the project)
• Data Science Pipelines operator component enabled in the RHOAI DataScienceCluster

When to Use This Skill

Use this skill when you need to:

• Set up or verify the pipeline server in a Data Science Project
• Submit a pipeline run from a YAML or JSON pipeline definition
• Schedule a recurring pipeline run with a cron expression
• Monitor pipeline run status and step-level progress
• View logs from pipeline steps (especially failing ones)
• List existing pipelines and pipeline runs in a project
• Delete a pipeline run or the pipeline server

Do NOT use this skill when:

• You need to create a Data Science Project first (use /ds-project-setup)
• You want to deploy a model for inference (use /model-deploy)
• You need to manage model training jobs (use training skills)
• You want to register models in the Model Registry (use /model-registry)

Workflow

Step 1: Determine Intent

Ask the user what they want to do: Setup server, List pipelines/runs, Run pipeline, Schedule recurring run, Monitor run, View logs, Delete resources.

Ask for target namespace. Validate via list_data_science_projects (from rhoai). If invalid, suggest /ds-project-setup.

Route: Setup -> Step 2, List -> Step 3, Run -> Step 4, Schedule -> Step 5, Monitor -> Step 6, Logs -> Step 7, Delete -> Step 8.

Step 2: Verify / Setup Pipeline Server

Check via get_pipeline_server (from rhoai) with namespace. If healthy, proceed. If unhealthy, offer diagnostics via diagnose_resource. If not exists, offer setup.

For setup: Check data connections via list_data_connections (from rhoai). If none exist, offer to delegate to /ds-project-setup.

Gather: Select from available data connections (the data connection name is the S3 secret name). The bucket, endpoint, and region can be extracted from the data connection secret. Present configuration for review. WAIT for confirmation.

MCP Tool: create_pipeline_server (from rhoai)

Parameters:

• namespace: target namespace - REQUIRED
• object_storage_secret: S3 credentials secret name - REQUIRED
• object_storage_bucket: S3 bucket name - REQUIRED
• object_storage_endpoint: S3 endpoint URL - REQUIRED
• object_storage_region: S3 region - OPTIONAL (default: "us-east-1")

Poll get_pipeline_server until ready or timeout.

Error Handling:

• If S3 secret not found -> Suggest creating via /ds-project-setup
• If S3 endpoint unreachable -> Advise verifying URL and cluster network access

Step 3: List Pipelines and Runs

Use list_resources (from rhoai) with resource_type="pipelines", namespace for detailed listing, or list_resource_names for a quick name-only view.

For specific run status: resource_status (from rhoai) with resource_type="pipeline", name, namespace.

For project-wide overview: project_summary (from rhoai) with namespace.

If pipeline server not configured, suggest setup via Step 2.

Step 4: Submit a Pipeline Run

Verify pipeline server is ready via get_pipeline_server (from rhoai). If not ready, offer Step 2.

Gather from user: pipeline definition (file path or inline YAML/JSON), run name (DNS-compatible), pipeline parameters (key-value pairs), service account (default: pipeline-runner-dspa).

Read pipeline definition using the Read tool if a file path is provided. Present configuration for review. WAIT for confirmation.

MCP Tool: resources_create_or_update (from openshift)

Parameters:

• resource: PipelineRun CR (apiVersion: tekton.dev/v1, kind: PipelineRun) with spec.pipelineSpec or spec.pipelineRef, spec.params, spec.serviceAccountName - REQUIRED

If the pipeline uses KFP v2 compiled format (Argo-based), adapt apiVersion/kind accordingly.

Proceed to Step 6 to monitor the run.

Step 5: Schedule a Recurring Pipeline Run

Verify pipeline server is ready (same as Step 4).

Gather from user: pipeline reference (name or YAML), schedule (cron expression or natural language), pipeline parameters, max concurrent runs (default: 1), optional start/end time.

Convert natural language to cron if needed. Present schedule configuration for review. WAIT for confirmation.

MCP Tool: resources_create_or_update (from openshift)

Parameters:

• resource: ScheduledWorkflow CR (apiVersion: scheduledworkflows.kubeflow.org/v1beta1, kind: ScheduledWorkflow) with spec.enabled, spec.maxConcurrency, spec.trigger.cronSchedule.cron, spec.trigger.cronSchedule.startTime/endTime, spec.workflow.spec.params - REQUIRED

Error Handling:

• If ScheduledWorkflow CRD not available -> DSP operator may not support scheduling in this version

Step 6: Monitor Pipeline Run

Get run status via get_resource (from rhoai) with resource_type="pipeline", name, namespace, verbosity="full".

For deeper diagnostics: diagnose_resource (from rhoai) with resource_type="pipeline", name, namespace.

Track step-level progress via resources_get (from openshift) with apiVersion tekton.dev/v1, kind PipelineRun. Extract task statuses from .status.childReferences or .status.taskRuns.

List pipeline pods via pods_list (from openshift) with namespace and labelSelector="tekton.dev/pipelineRun=<run-name>".

Present step progress table:

Step	Status	Duration	Message
[step-name]	[Running/Succeeded/Failed]	[duration]	[message]

On failure: Present options: (1) View step logs, (2) Check events, (3) Run diagnostics, (4) Retry. WAIT for user decision. NEVER auto-retry or auto-delete failed runs.

Error Handling:

• If run not found -> List available runs and ask user to select
• If run still pending -> Check events for scheduling issues

Step 7: View Pipeline Step Logs

From the PipelineRun status (Step 6), identify the pod name for the failing or target step. If user did not specify a step, list available steps and ask which one to inspect.

MCP Tool: pods_log (from openshift)

Parameters:

• namespace: target namespace - REQUIRED
• name: pod name of the pipeline step - REQUIRED
• container: container name - OPTIONAL (specify if multiple containers)

Also check events_list (from openshift) filtered by namespace for scheduling/resource issues.

Suggest fixes based on common log patterns: OOMKilled -> increase memory limits; ImagePullBackOff -> verify image reference; AccessDenied on S3 -> check data connection via /ds-project-setup; Permission denied -> verify ServiceAccount permissions.

Error Handling:

• If pod not found -> Pod may have been cleaned up; check events for historical data
• If multiple containers -> List container names and ask user to select

Step 8: Delete Pipeline Resources

Delete a pipeline run:

Get run details via get_resource (from rhoai) with resource_type="pipeline", name, namespace. Display run details (name, status, created, step count).

Ask: "Delete pipeline run <name>? This will remove the run record and associated pods. (yes/no)" WAIT for confirmation.

MCP Tool: resources_delete (from openshift) with apiVersion tekton.dev/v1, kind PipelineRun, name, namespace.

---

Delete the pipeline server (DESTRUCTIVE):

Display warning: deleting the DSPA removes all pipeline runs, history, API/UI endpoints, and terminates running pipelines. S3 data is preserved.

Ask: "Type the namespace name <namespace> to confirm deletion:" WAIT for typed confirmation. Verify exact match.

MCP Tool: delete_pipeline_server (from rhoai) with namespace, confirm=true.

Verify via get_pipeline_server (from rhoai). Confirm removal.

Error Handling:

• If typed confirmation mismatch -> Cancel deletion
• If RBAC error -> Report insufficient permissions
• If pipeline server not found -> Report no server in namespace

Common Issues

For common issues (GPU scheduling, OOMKilled, image pull errors, RBAC), see common-issues.md.

Issue 1: Pipeline Server Not Ready

Cause: Invalid S3 credentials, unreachable S3 endpoint, or database connectivity issues. Solution: Use diagnose_resource for automated diagnostics. Check data connection credentials via list_data_connections. Verify S3 bucket accessibility. Check DSPA pod logs for specific errors.

Issue 2: Pipeline Run Stuck in Pending

Cause: Insufficient resources, missing ServiceAccount, unbound PVC, or scheduling issues. Solution: Check events for scheduling errors. Verify ServiceAccount pipeline-runner-dspa exists. Check ResourceQuota/LimitRange. For GPU pipelines, verify availability via /ai-observability.

Issue 3: Step OOMKilled

Cause: Step container exceeded memory limit. Solution: View step logs before OOM kill. Increase resources.limits.memory in the pipeline YAML. Consider streaming data for data-intensive steps.

Issue 4: S3 Artifact Upload Fails

Cause: Expired credentials, missing bucket, or unreachable endpoint. Solution: Verify data connection via list_data_connections. Check S3 bucket exists. Re-create data connection if credentials rotated.

Issue 5: Schedule Not Triggering

Cause: Invalid cron, ScheduledWorkflow controller not running, or schedule disabled. Solution: Verify ScheduledWorkflow CR spec.enabled is true. Validate cron format. Check controller pod logs. Verify startTime/endTime constraints.

Dependencies

MCP Tools

See Prerequisites for the complete list of required MCP tools.

Related Skills

• /ds-project-setup - Create a Data Science Project with data connections (prerequisite for pipeline server)
• /model-deploy - Deploy a model produced by a pipeline
• /model-registry - Register models produced by pipeline runs
• /ai-observability - Check GPU/cluster resources before running resource-intensive pipelines
• /debug-inference - Debug models deployed from pipeline outputs

Reference Documentation

• skill-conventions.md - Shared prerequisite, HITL, and security conventions

Critical: Human-in-the-Loop Requirements

See skill-conventions.md for general HITL and security conventions.

Skill-specific checkpoints:

• Before creating pipeline server (Step 2): display S3 storage configuration, confirm
• Before submitting pipeline run (Step 4): display run configuration table, confirm
• Before scheduling recurring run (Step 5): display cron schedule and parameters, confirm
• On pipeline failure (Step 6): present diagnostic options, wait for user decision
• Before deleting pipeline run (Step 8): display run details, confirm
• Before deleting pipeline server (Step 8): display destructive warning, require typed namespace confirmation
• NEVER auto-submit pipeline runs without confirmation
• NEVER auto-delete pipeline servers or auto-retry failed runs
• NEVER display S3 credentials or secret values in output

Example Usage

User: "Set up the pipeline server in my ml-training project and run a data preprocessing pipeline"

Skill response: Validates project, sets up pipeline server with S3 data connection after confirmation, monitors readiness, then gathers pipeline YAML, presents run config for confirmation, submits PipelineRun, and monitors step-level progress.