rhoai-model-catalog-workflows

name	rhoai-model-catalog-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 data scientist and AI engineer workflows in the Red Hat OpenShift AI model catalog: discovering generative AI models, using catalog categories, searching and filtering by task, provider, license, language, and tensor type, viewing model cards and provider metadata, evaluating Red Hat AI validated model performance, using Model performance view, interpreting latency and throughput filters, comparing tensor variants, registering a catalog model into a model registry, and deploying a catalog model through the model serving wizard. Do NOT use for administrator catalog-source governance, model registry provisioning, general model-serving platform configuration, deployment wizard details after the catalog handoff (use rhoai-model-deployment), or live cluster changes without the OpenShift safety guard.

RHOAI Model Catalog Workflows

Use this skill for OpenShift AI model catalog workflows performed by data scientists and AI engineers: discover, evaluate, register, and deploy catalog models.

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 with the model catalog guide to this repo's demo workflow and governance review model.

Scope

This skill covers:

• discovering available generative AI models in AI hub -> Models -> Catalog
• model catalog categories such as All models, Red Hat AI models, Red Hat AI validated models, Other models, and administrator-defined labeled categories
• searching by model name, description, or provider
• filtering by task, provider, license, language, and tensor type
• reading model cards, intended use, limitations, training details, and evaluation results
• using Performance Insights for validated models
• using Model performance view and workload/hardware constraints
• interpreting validated-model performance metrics and tensor variants
• registering a catalog model into an available model registry
• deploying a catalog model through the model serving wizard

Use other skills for adjacent work:

• rhoai-model-catalog-sources for administrator control over which catalog sources and models are visible
• rhoai-model-registry for registry provisioning, database choices, and permissions
• rhoai-model-registry-workflows for registry-side model and version lifecycle after catalog registration
• rhoai-model-serving-platform for serving runtime prerequisites, automatic runtime selection prerequisites, and runtime platform configuration
• rhoai-model-deployment for deployment wizard details, model storage choices, runtime auto-selection behavior, routes, token authentication, deployment strategies, and inference endpoint smoke tests
• rhoai-gen-ai-playground for testing catalog-deployed generative models after they are added as AI asset endpoints
• rhoai-model-management-monitoring for operating deployed catalog models
• rhoai-api-tiers for API support posture when automating catalog-related workflows

Demo Policy

For this repo:

• Treat the model catalog as the governed discovery and evaluation entry point for model selection.
• Prefer Red Hat AI and Red Hat AI validated categories for default demo narratives.
• Treat Red Hat AI validated performance data as deployment-selection evidence, not a replacement for the demo's own validation.
• Preserve catalog model card limitations and intended-use notes in README or presentation claims.
• Use Performance Insights and tensor variant comparisons when explaining hardware, latency, throughput, or cost trade-offs.
• Do not register catalog models on s390x; the official workflow says this registration path is not supported there.
• Do not claim broad catalog support on s390x; the official guide states only granite-3.3-8b-instruct is supported there.
• For catalog deployments, remember that model-serving deployments use the global cluster pull secret to pull OCI-compliant ModelCar models from the catalog.
• Hand off model-serving runtime choices and advanced deployment settings to rhoai-model-serving-platform.
• Label s390x advanced deployment settings as unsupported.

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 discovery, performance evaluation, tensor variant assessment, catalog registration, or catalog deployment.
4. Use the dashboard paths and constraints in examples/model-catalog-workflow-patterns.md.
5. For registry registration, confirm access to an available model registry.
6. For deployment, confirm model serving prerequisites and hand off deployment workflow details to rhoai-model-deployment.
7. Validate with references/validation-checklist.md.

References

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