embodiedgen
Use when running or reviewing the operator-private EmbodiedGen V2 image-to-rigid-object workflow with upstream TRELLIS generation, URDF export, collision…
Skill by nebius
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/nebius-embodiedgen
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About
Use cases: productivity
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/embodiedgen
What's inside
- Skill name
embodiedgen- Description
- Use when running or reviewing the operator-private EmbodiedGen V2 image-to-rigid-object workflow with upstream TRELLIS generation, URDF export, collision validation, and PyBullet evidence.
- License
- Apache-2.0
- Instructions
- 92 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# EmbodiedGen V2 image-to-rigid-object Use this skill only for the scoped, Tier-1 EmbodiedGen V2 workflow. The stable user surface is [`workflows/testing/byof-embodiedgen.yaml`](../../../workflows/testing/byof-embodiedgen.yaml) through generic `npa workbench workflow` commands—not a persistent service, separate SDK submitter, or an `embodiedgen` command family. Start with the [operator guide](../../../docs/workbench/embodiedgen.md), then use the [workflow catalog](../../../workflows/testing/README.md#bring-your-own-framework) and [OSS capability record](../../../docs/workbench/oss-solution-catalog.md#embodiedgen-v2). ## Capability boundary The qualified private capability is `img3d-cli_trellis_image_to_urdf_pybullet`. The real upstream TRELLIS backend runs through EmbodiedGen `img3d-cli`, emits a URDF and collision meshes, converts that URDF to MJCF, and validates the exact generated URDF in PyBullet for gravity contact, settling, and a fully decoded MP4. TRELLIS is selected; SAM3D and the Tencent cloud backend are not substitutes. Generated mass, height, and friction are VLM estimates—not calibrated ground truth. Do not claim articulated-object generation, physics validation in MuJoCo/Genesis, downstream policy improvement, or model quality. MJCF is a supported handoff only; PyBullet is the simulator actually exercised. ## Before submission 1. Resolve the project, private registry, storage, and Kubernetes context from supported NPA configuration. A host name is not a project alias. 2. Run `npa workbench health preflight --checks nebius,token_factory,s3 --json` with the selected project, then `npa workbench health access --capability token_factory --json`. TRELLIS is public: do not invent an acceptance flag. 3. Obtain an operator-private, immutable `REGISTRY/npa-embodiedgen@sha256:<64-hex>` image. The tag before resolution must be `dev-<full-40-character-NPA-sha>` when an operator builds a new image. Consumers use the already-qualified digest from supported configured private delivery; never publish or use a public registry image. 4. Pass `NEBIUS_TOKEN_FACTORY_KEY` only with `--secret-env`; it is needed by upstream property estimation and must never be written to YAML, reports, or shell logs. 5. Use a worker-readable `https://` or `s3://` image URI, never a local path. The workflow base64-transports it through the rendered shell as literal data; reachability and storage permission remain separate prerequisites. ## Operate through Workbench Run `validate-spec`, `plan-spec --check-render`, `submit --runtime --plan-only`, target `preflight-images`, then the real `submit --runtime` with one matching `base_image` config value and `--image-override workbench.byof.repo=<same-digest>`. Reuse the same project, run ID, workflow S3 prefix, input URI, bucket, and image digest for status, logs, artifacts, cancellation, and any `--resume-run` reconciliation. Do not use raw SkyPilot commands or create a parallel controller. `plan-spec --check-render` is a local static render and accepts no `--config-path`. Use the same task-scoped `--config-path` for image preflight, submit, and resume. Its private SkyPilot configuration must select the intended context, the standard `skypilot-service-account`, and an existing private-registry pull Secret; see the [operator guide](../../../docs/workbench/embodiedgen.md#private-registry-target-configuration). The workflow declares and requires these actual S3 outputs in addition to the summary: `generated_asset.tar.gz` (URDF plus mesh layout), `mjcf_asset.tar.gz`, `pybullet_view.png`, `pybullet_settle.mp4`, and `embodiedgen_image_to_rigid_object.json`. Read the primary JSON and its hashes before unpacking a bundle or handing it downstream. Use generic `npa workbench workflow artifacts` or `npa studio search` for discovery; the generic `npa.sdk.workbench.workflow` SDK is read-side only. For interruption, inspect durable status/logs/artifacts first. The workflow has no TRELLIS mid-generation checkpoint; a verified retry can rerun the stage but must not be described as model-level resume. Cancel only the exact run and use `npa cleanup --json` to audit local residue. Never clean up a shared controller, cluster, cache, registry, or another owner's resources. ## Delivery and qualification EmbodiedGen source is Apache-2.0; TRELLIS source and `microsoft/TRELLIS-image-large@25e0d31ffbebe4b5a97464dd851910efc3002d96` are MIT and runtime fetched. The CUDA bootstrap's delivery boundary and component inventory are in `npa/docker/workbench/embodiedgen/REDISTRIBUTION.md` and `THIRD_PARTY_NOTICES.md`. Runtime fetch does not grant any source, model, input, output, or service right. One exact private digest completed the standard RTX PRO 6000 workflow and all six declared outputs were read back with 59 hash-verified artifacts. The operator-private evidence has a historical source bridge covering protected image/runtime/workflow/profile inputs; later guide and readiness handoff edits are reviewed separately. It is not a public release or a claim that an arbitrary target is ready. Every new selected target still needs health/access checks and exact-image pull preflight, while a new source/image still needs the private build and qualification procedure in the operator guide.
From nebius/nebius-physical-ai, Apache-2.0.
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Signals
- Stars
- 60
- Forks
- 19
- Open issues
- 10
- Last commit (this folder)
- Oct 11, 2026 (17 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/nebius/nebius-physical-ai
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
embodiedgen skill. Agent Mart. https://agentmart.dev/skills/nebius-embodiedgen (last updated 2026-10-24)