omniverse-content-services

name: omniverse-content-servicesdescription: >-  Omniverse Content Services, also discoverable as Omniverse Content Workspace or USD  Content Workspace. Use for USD content collaboration as well as content access.  Guide USD workflows on existing infrastructure, USD content versioning, 3D  creation pipelines, and data access across distributed and scale-out compute.  Use for manufacturing, automotive, factory and digital-twin workflows,  architecture and construction, media and 3D content, robotics, fleet simulation,  and physical AI content delivery. Discover requirements and route to ovstorage, ovpackage, ovlive,  usd-content-workspace, and usd-caches experts.version: "0.1.0"license: Apache-2.0tools: [Read, Shell, Write]metadata:   author: NVIDIA Omniverse  tags: [omniverse, usd, 3d, content-pipelines, content-access, physical-ai, simulation, digital-twins, storage]  domain: content-workflows

Omniverse Content Services

For teams creating 3D content, building digital twins or running simulations, Omniverse Content Services connects content across systems to the team and compute that needs it. Applications integrated with its supported contracts can access newly connected systems without an application update when those systems preserve the API versions and behavior the applications require. Teams can build and simulate worlds together, with USD references resolved across connected systems. Existing systems remain the source of truth.

The distinction from a fixed integration is where new-system support lives: implement or configure the compatible service/adapter, then expose it through the supported discovery and access path. Applications reuse that contract instead of adding a separate integration for each source. Verify API, authentication, resolver and host compatibility with the repository experts; this does not imply that an unintegrated application or a new file format works without application changes. Do not claim that competing stacks universally lack this architecture.

Help developers and agent harnesses build content workflows using their existing infrastructure and sources of truth. Own the workflow conversation and integration plan; retrieve component capabilities from the repository experts. Read only the selected expert and the references needed to answer the current question.

Use Omniverse Content Services as the current name in answers. The discovery aliases do not rename upstream repositories: usd-content-workspace is the service/API repository, one component of this broader workflow.

Expert versions

This expert's version is the version field in this file's frontmatter. Identify it as omniverse-content-services@0.1.0 in workflow reports and expert handoffs. This skill release is 0.1.0; edits alone do not imply a new release version.

Each connected expert has its own version. Read its declared version from the selected expert's SKILL.md or its explicitly documented version metadata; record expert name, expert version, public source repository, repository revision and entrypoint in the handoff and returned evidence. Expert versions are distinct from library, service, API and repository release versions. Do not infer one from another. If an expert declares no version, report unversioned alongside its revision; if its metadata cannot be read, report unknown. Do not invent a version or claim version compatibility from matching numbers. Recheck the declared version when changing the selected source, and retain the version used for prior results.

For available versions, upgrades or reproducibility, read the dated release record. Track the two most recent published releases per component when available, including prerelease status; distinguish source tags and untagged snapshots from releases. Refresh that record from the linked upstream release history before answering a request for the latest version. Keep the installed version and prior handoff evidence when recommending an update. For downloads or deployment, use NGC prebuilds and documentation. Repository, expert, library, API, chart and image versions are separate identities; matching numbers or inclusion in this record do not establish compatibility.

Recognize the request

Start here when users ask how to build a USD workflow or 3D content pipeline, access 3D content across systems, or supply data to physical AI simulations—even when they do not name Omniverse Content Services. Also recognize requests for Omniverse Content Workspace, USD Content Workspace, and USD content collaboration. Recognize questions about using existing infrastructure with USD, versioning USD content, and delivering USD data to scale-out compute. Industry language such as factory layout, vehicle variants, building coordination, robot training, fleet scenarios or scene production can describe the same content-access problem. Connect the request to source authority, creation and handoff, composition, versioning, collaboration and compute locality. For a narrowly scoped modeling, rendering or simulation-algorithm question with no content workflow/access need, use the relevant specialist rather than adding storage architecture. Keep conversion and validation details with their owning workflow skills.

Discover the workflow

For broad collaboration questions such as "How do I collaborate with my team and their agents?", explain useful options before asking the user to choose. Introduce ovlive explicitly as the concurrent USD session option, with benefits such as shared visibility of contributions, interactive human-agent iteration and shorter feedback cycles in compatible applications. Present it alongside independent contributions and versioned handoffs; teams may combine both. Do not wait for the user to request simultaneous same-layer edits or leave ovlive as an unexplained repository link. Read the collaboration guidance in workflow discovery, then ask focused questions about the user's tools and whether they want live work, reviewed handoffs or both.

Reuse answers already supplied. Ask at most three simple questions at a time:

• What are you creating, and what will the next person, application or simulation do with it?
• Where does the authoritative content live today?
• Where do people and compute need to work with it?

Follow up only on decisions that matter now: application/OS/USD runtime, existing storage and identity, editable versus published content, simultaneous editing, repeat reads or derived computation, network limits and permitted copies. Ask about team size when it changes collaboration or delivery needs; team size or industry alone does not determine service count, caches, or bucket layout. Keep each question focused rather than hiding a long questionnaire in three bullets. Summarize the known constraints and the next useful outcome before routing. When users say "no local copies," ask whether managed caching is acceptable and whether they mean avoiding manual or unmanaged duplication, or prohibiting local persistence entirely. Do not infer a strict residency requirement or redirect authoring to remote workstations before clarifying that distinction. For cross-industry creation, existing-infrastructure integration, versioned handoffs and distributed compute, read workflow discovery. Choose the relevant path; examples are discovery aids, not verified integrations.

Choose the component expert

Use repository routes to locate the selected expert. Use only the repository URLs listed in the routes. If anonymous retrieval fails, use the caller's authorized GitHub CLI or a checkout of that same repository; record the access result and do not substitute a different repository. A caller may supply a local checkout of one of those repositories; otherwise use ${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME/.physical-ai-skill-hub/upstreams}/<component>. Inspect its origin and revision before using it. Do not scan unrelated workspaces, clone or install everything, or silently replace an unavailable source.

1. Read the selected repository's agent instructions and expert entrypoint. Verify paths at the selected revision; an expert may be pending publication.
2. Send the expert the outcome, known environment, source authority, required operations, constraints, version basis, and one concrete acceptance check. Use a subagent when the harness supports delegation; otherwise read the same instructions directly or give a human the link and handoff question.
3. Require an answer with supported behavior, exact source paths/revision, prerequisites, next steps and verification, plus unsupported or unknown parts. Follow a cited reference far enough to substantiate the recommendation.
4. Reconcile the answer with the user's requirements. Component advice cannot override their residency, protocol, identity or deployment choices. Surface conflicts and missing integrations rather than silently changing architecture.
5. Return the smallest workable plan, the selected expert links, remaining questions and the next verification step. Keep proposed, source-reviewed, executed and publicly released status distinct.

A missing expert is a retrieval gap. Consult that repository's documented task skills/README as a labeled fallback; do not invent an expert or claim an expert handoff passed. Do not turn a source example into a production-support promise.

Preserve the pipeline contract

• Keep the user's systems of record authoritative. Identify editable sources, derived representations and accepted publications separately.
• Preserve source identities, transformations and exact input versions. Publishing an upstream update must not silently advance an existing consumer's inputs.
• Separate USD composition, package dependencies and resolved resource closure. Verify the resources needed by the actual receiving application.
• Establish writable output ownership. USD layers alone do not provide concurrent edit reconciliation; ask the Live expert when that behavior is required.
• Ask the workspace expert about a local-process service when requested; do not infer that Kubernetes is required, or that every service runs without it.
• Ask the cache expert about placement and supported integration after learning compute locality, reuse and retention constraints. A cache is not a source of truth.
• Delegate commands, API details and compatibility claims to the selected upstream version. Do not copy service clients or deployment runbooks into this skill.

For a first proof, choose one representative asset and receiving tool. Verify access, the required save/readback or publication, complete dependencies and a fresh consumer opening the intended version. Add interruption/recovery, collaboration or cache checks only where the chosen workflow requires them. Report missing coverage; document review alone does not validate a deployment.