rhoai-project-workflows

name	rhoai-project-workflows
metadata	{"author":"rhoai3-demo","version":"1.0.0","platform-family":"rhoai","platform-baseline":"repo","ocp-baseline":"repo","skill-group":"RHOAI Platform"}
description	Use when documenting, reviewing, or operating Red Hat OpenShift AI project workflows from the Working on projects guide: creating, updating, and deleting data science projects; creating, starting, updating, and deleting project workbenches; adding, updating, and deleting project connections; using connection annotations with InferenceService and LLMInferenceService; adding, updating, migrating, and deleting project cluster storage; granting, updating, and removing project access; and handing off to project-scoped resources for workbench images, hardware profiles, and KServe serving runtimes. Do NOT use for global user/group access (use rhoai-users-groups-access), connection type template administration (use rhoai-connection-types), S3 data access from notebooks (use rhoai-s3-object-storage-data), data science IDE workflows inside a running workbench (use rhoai-data-science-ide-workflows), storage class administration (use rhoai-storage-classes), project-scoped resource schema details (use rhoai-project-scoped-resources), or live cluster changes without the OpenShift safety guard.

RHOAI Project Workflows

Use this skill for OpenShift AI user workflows that organize data science work inside projects on the active product baseline in docs/PLATFORM_BASELINE.md.

Source Grounding

Read references/source-capture.md before using product workflow details. Official Red Hat documentation is product authority. This skill adapts the official Working on projects guide to this repo's demo workflow and GitOps review model.

Scope

This skill covers:

• data science project lifecycle: create, update, and delete
• project workbench lifecycle: create, start, update, and delete
• workbench image and IDE selection boundaries
• project connections and connection API workload annotations
• project cluster storage lifecycle and storage migration workflow
• project-level access for users and groups
• handoff to project-scoped resources for project-local workbench images, hardware profiles, and KServe serving runtime templates

Use other skills for adjacent work:

• rhoai-users-groups-access and rhoai-access-group-selection for global OpenShift AI user/admin access
• rhoai-connection-types for administrator-managed connection type templates
• rhoai-s3-object-storage-data for Boto3 and S3-compatible object storage data operations from workbenches
• rhoai-data-science-ide-workflows for JupyterLab, code-server, Git, Python package, extension, and user-facing IDE troubleshooting workflows
• rhoai-storage-classes and rhoai-cluster-pvc-size for OpenShift AI storage administration and cluster default PVC size
• rhoai-project-scoped-resources for detailed project-scoped resource manifests and review rules
• rhoai-workbenches-custom-images, rhoai-workbench-image-import, and rhoai-workbench-gateway-api-migration for custom workbench images
• rhoai-model-serving-platform for KServe and vLLM serving details
• rhoai-ai-pipelines for AI Pipelines server, definition, experiment, run, schedule, log, and Elyra workflows
• rhoai-mlflow for MLflow workspace mapping, SDK authentication, RBAC, and project-specific artifact storage overrides
• rhoai-kfp-pipeline-authoring for repo-specific KFP Python and runner code

Demo Policy

For this repo:

• Treat an OpenShift AI project as the clean boundary for a demo team, persona, or capability stage.
• Choose stable project and workbench resource names up front. Resource names are immutable after creation; only display names and descriptions can be edited later.
• Do not hard-code credentials in READMEs, notebooks, scripts, or manifests. Use project connections and Kubernetes secrets with least-privilege scope.
• Prefer ReadWriteOnce cluster storage for individual workbenches. Use ReadWriteMany only for explicit collaboration scenarios with documented data integrity, security, and backup expectations.
• Treat project deletion and cluster-storage deletion as destructive. They remove associated resources and data that cannot be recovered.
• Keep Technology Preview posture visible when selecting RStudio workbench images or hardware profiles.
• Do not claim that creating a project alone deploys pipelines, models, workbenches, or storage. Those capabilities require explicit resources.

Workflow

1. Confirm the active baseline in docs/PLATFORM_BASELINE.md.
2. Read references/source-capture.md and references/official-doc-extraction.md.
3. Decide whether the task is:
   • project lifecycle
   • workbench lifecycle
   • connection lifecycle or connection API annotations
   • cluster storage lifecycle or migration
   • project access management
   • project-scoped resource handoff
4. Use examples/project-workflow-patterns.md for review and authoring patterns.
5. For live cluster work, follow the OpenShift safety guard in AGENTS.md.
6. Validate with references/validation-checklist.md.

References

• references/source-capture.md
• references/official-doc-extraction.md
• references/validation-checklist.md
• examples/project-workflow-patterns.md