CubeStudio

Use this repo skill for CubeStudio platform tasks: deployment, backend customization, notebooks and image catalogs, pipeline/job-template authoring, data and SQLLab workflows, and model serving / AIHub / chat operations.

Start here

1. Read references/platform-overview.md for the repo-wide architecture and route map.

2. Read references/configuration-and-catalogs.md for overlay behavior, runtime configuration, and seed catalogs.

3. Read references/troubleshooting.md for cross-cutting install/import/config/runtime failures.

4. If the checkout looks stale, compare it with references/repo-provenance.md.

5. If you want a safe static inventory of a CubeStudio checkout, run the bundled helper:

   python scripts/cube_studio_static_check.py --help
   python scripts/cube_studio_static_check.py /path/to/cube-studio
   

Setup note

CubeStudio is a platform checkout, not a normal pip-installable package. For a public inspection environment, use Python 3.9 and install the documented runtime dependencies before running the bundled static helpers:

python -m pip install -r install/docker/requirements.txt
python scripts/cube_studio_static_check.py .

Use the deployment and backend sub-skills for Docker Compose, Kubernetes, and runtime overlay setup rather than trying to install the repository as a library.

Route map

• deploy-and-operate — local Docker Compose development, Kubernetes install order, offline/private registry prep, manifest inventory, overlays, and deployment triage.
• backend-and-configuration — Flask AppBuilder startup, runtime overlays, auth/RBAC, backend views/APIs, Celery/watchers, and frontend build/proxy customization.
• compute-notebooks-and-images — project/resource groups, notebook lifecycle, GPU resource strings, registry/image catalog, and monitoring/resource views.
• pipelines-and-job-templates — pipeline DAGs, job-template registration, Argo workflow generation, template args schema, and NNI/HPO templates.
• data-metadata-and-sqllab — datasets, metadata and dimension tables, SQLLab, ETL pipelines, and data-transfer templates.
• serving-aihub-and-llm — model registry, inference services, AIHub cards, chat scenarios, and LLM gateway configuration.

When to use this repo skill

• The user names CubeStudio, Kubeflow Dashboard, AIHub, notebook, pipeline, job template, inference service, SQLLab, or the platform's Kubernetes/Docker install stack.
• The user needs the platform's own runtime guidance, not a generic Flask, Kubernetes, or image-serving answer.
• The user wants to understand how a record in one CubeStudio area becomes another record or runtime object, such as training model → inference service or job template → pipeline task.

What not to do here

• Do not treat this as a generic repository-maintenance skill unless the request is explicitly about editing the CubeStudio source tree.
• Do not point future agents to the original checkout for runtime steps when the answer can be bundled into a reference or helper.
• Do not run cluster-mutating, Docker-building, or service-starting commands as part of skill drafting.

Safe first checks

• Inspect the selected sub-skill first when the request is clearly domain-specific.
• Use the repo-level static helper for a fast, read-only inventory of a checkout.
• Use the sub-skill references for detailed APIs, workflows, and troubleshooting.

Shared guidance

• The checked-in myapp/config.py and myapp/project.py are placeholders; runtime overlays provide the real configuration.
• Pipeline, serving, and notebook tasks often depend on the same project, resource, and image registry assumptions, so cross-link to the sibling sub-skill when the question spans domains.
• If a task mixes installation, backend customization, and runtime deployment, start with the deployment or backend sub-skill and then follow the route map above.