Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NVIDIA-TAO/tao-skill-bank --skill tao-train-nvpanoptix3dgit clone --depth 1 https://github.com/NVIDIA-TAO/tao-skill-bankWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-train-nvpanoptix3d)<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-train-nvpanoptix3d"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-train-nvpanoptix3d/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-train-nvpanoptix3d"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-train-nvpanoptix3d.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 38 Potential security issue detected. Manual review is recommended.Fix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium MCP Rug Pull · line 51 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium MCP Rug Pull · line 58 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium MCP Rug Pull · line 65 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium MCP Rug Pull · line 72 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00131 | $0.04429 |
| Opus 5.5 | $0.00052 | $0.01772 |
| Sonnet 5.5 | $0.00026 | $0.00886 |
| Haiku 4.5 | $0.00013 | $0.00443 |
Grade A, and why
tao-train-nvpanoptix3d scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NVPanoptix3D
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the
tao-setupskill first (host preflight, credentials, cross-skill discovery).
NVPanoptix3D for panoptic 3D scene reconstruction from posed RGB images. Produces 3D panoptic segmentation (semantic, instance, and panoptic masks) with occupancy completion. Built on VGGT backbone with Mask2Former-style head and 3D frustum reconstruction.
Uses 2D and 3D stage checkpoints. Set train.checkpoint_2d and train.checkpoint_3d for staged initialization.
Quick Start (docker run)
Docker-native launch — no TAO SDK and no Python on the host. Use the local Docker/platform skill instead when it gives a stricter environment-specific command (non-root UID mapping, cache redirects, remote daemons).
TAO_PYT_IMAGE_DEFAULT=nvcr.io/nvidia/tao/tao-toolkit:7.3.0-pyt # versions-key: images.tao_toolkit.pyt
TAO_PYT_IMAGE="${TAO_PYT_IMAGE:-$TAO_PYT_IMAGE_DEFAULT}"
RUN_ROOT="${RUN_ROOT:-$PWD}"
DOCKER_COMMON=(
--rm --gpus all --ipc=host
--shm-size=8g
--ulimit memlock=-1
--ulimit stack=67108864
-v "$RUN_ROOT/data:/data:ro"
-v "$RUN_ROOT/specs:/specs:ro"
-v "$RUN_ROOT/results:/results"
)
Train:
docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
python -m nvidia_tao_pytorch.cv.nvpanoptix3d.entrypoint.nvpanoptix3d train -e /specs/train.yaml
Evaluate:
docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
python -m nvidia_tao_pytorch.cv.nvpanoptix3d.entrypoint.nvpanoptix3d evaluate -e /specs/evaluate.yaml
Inference:
docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
python -m nvidia_tao_pytorch.cv.nvpanoptix3d.entrypoint.nvpanoptix3d inference -e /specs/inference.yaml
Export:
docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
python -m nvidia_tao_pytorch.cv.nvpanoptix3d.entrypoint.nvpanoptix3d export -e /specs/export.yaml
Every action takes its spec with -e; results_dir is set in the spec or
overridden on the command line. Mount any pretrained-weights directory the spec
references, and keep every in-container path consistent across actions.
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- BENCHMARK.md 4.0 KB
- evals/evals.json 824 B
- references/skill_info.yaml 6.3 KB
- references/spec_template_evaluate.yaml 3.6 KB
- references/spec_template_export.yaml 3.8 KB
- references/spec_template_inference.yaml 3.6 KB
- references/spec_template_train.yaml 3.5 KB
- schemas/evaluate.schema.json 58 KB
- schemas/export.schema.json 60 KB
- schemas/inference.schema.json 59 KB
- schemas/manifest.json 11 KB
- schemas/train.schema.json 56 KB
- skill-card.md 4.1 KB
- skill.oms.sig 6.9 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago Changed 0c4112124125
- 1mo ago First seen · 316 lines · 131 tokens per session scan A d69a020b5a0e
tao-train-nvpanoptix3d is a skill published in the GitHub repository NVIDIA-TAO/tao-skill-bank (97 stars, last pushed 5d ago), licensed Apache-2.0. It adds 131 tokens to every session and 4,429 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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