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 nebius/nebius-physical-ai --skill seedvr2git clone --depth 1 https://github.com/nebius/nebius-physical-aiWrote 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/nebius/nebius-physical-ai/seedvr2)<a href="https://agentmods.dev/skills/nebius/nebius-physical-ai/seedvr2"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/seedvr2/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/nebius/nebius-physical-ai/seedvr2"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/seedvr2.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00038 | $0.01667 |
| Opus 5.5 | $0.00015 | $0.00667 |
| Sonnet 5.5 | $0.00008 | $0.00333 |
| Haiku 4.5 | $0.00004 | $0.00167 |
Grade A, and why
seedvr2 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 10d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SeedVR2 video restoration
Use this skill for the first-class npa-seedvr2 batch tool and its evidence.
Read these files before changing behavior:
npa/src/npa/workbench/seedvr2/npa/docker/workbench/seedvr2/REDISTRIBUTION.mdworkflows/testing/seedvr2-video-restoration.yamldocs/workbench/seedvr2.md
Also load npa-cli-conventions, toolref-argv-contract,
author-npa-workflow, real-components, solution-licensing,
secure-image-build, gpu-selection, testing-conventions, and
agent-visual-feedback when their surfaces are involved.
Ground truth
- Source is
ByteDance-Seed/SeedVRate4de8c24441a67e1b7df56abea10645059bb1185, Apache-2.0. - Model is
ByteDance-Seed/SeedVR2-3Bat37255ff8cccfb01071b87f635a5948ca8d53117c, Apache-2.0 and public. - Runtime requires all four payloads and verifies the fixed sizes and SHA-256
values in
npa.workbench.seedvr2.schemas.MODEL_FILES. - The real entrypoint is upstream
projects/inference_seedvr2_3b.pyunder one-processtorchrun, seed 666 and sequence-parallel size 1. - The candidate hardware contract is exactly one full-memory non-MIG H100 (
sm_90, default) or explicitly selected B200 (sm_100with qualified native extensions), a digest-boundNPA_TASK_IMAGE, and the full NPA source revision baked into that image. A caller-supplied revision, tag, compatible image, MIG slice, or successful import is not capability evidence. - Model weights, customer video, outputs, credentials, and populated caches are runtime-only. No per-run model terms prompt is required for these public Apache-2.0 weights.
Do not silently replace upstream inference with interpolation, sharpening, another restoration model, or a synthetic stub. The bicubic image exists only as an explicit review baseline.
Operate
npa workbench workflow validate-spec \
workflows/testing/seedvr2-video-restoration.yaml
npa workbench workflow plan-spec \
workflows/testing/seedvr2-video-restoration.yaml --run-id seedvr2-plan
npa workbench workflow submit \
workflows/testing/seedvr2-video-restoration.yaml \
--var bucket="<your-bucket>" \
--var seedvr2_input_uri="s3://<your-bucket>/inputs/low-resolution.mp4"
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.
- 10d ago First seen · 145 lines · 38 tokens per session scan A 98ab367cd2d8
seedvr2 is a skill published in the GitHub repository nebius/nebius-physical-ai (50 stars, last pushed today), licensed Apache-2.0. It adds 38 tokens to every session and 1,667 once invoked, about $0.0002 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 2026-10-12.
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