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 retargetinggit 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/retargeting)<a href="https://agentmods.dev/skills/nebius/nebius-physical-ai/retargeting"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/retargeting/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/retargeting"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/retargeting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00362 |
| Opus 5 | $0.00019 | $0.00181 |
| Sonnet 5 | $0.00008 | $0.00072 |
| Haiku 4.5 | $0.00004 | $0.00036 |
Grade A, and why
retargeting 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 2d 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.
What it actually says
Retargeting
Retargeting converts already-retargeted SOMA/G1/Bones motion artifacts into the real motion-lib PKL schema consumed by SONIC locomotion training.
Interfaces
CLI:
npa workbench sonic retargeting run
npa workbench sonic retargeting workflow
npa workbench sonic retargeting status
npa workbench sonic retargeting list
SkyPilot YAML:
workflows/testing/retargeting.yamlworkflows/testing/sonic-locomotion-finetuning.yaml
Routing And Data Flow
Retargeting is CPU-only by default.
Inputs and outputs use S3 paths:
--input-path: source motion prefix or object.--output-path: retargeted motion output prefix.--retarget-map: optional map artifact.
The result artifacts are real .pkl motion-lib files plus
retargeting_result.json metadata. Do not replace this with a manifest-only
shim.
Raw BVH inputs can be converted to upstream SONIC SOMA skeleton PKLs with
extract_soma_joints_from_bvh.py, but upstream SONIC does not bundle a raw
BVH-to-G1 robot retargeter. Use external SOMA Retargeter/GMR before the final
SONIC motion-lib conversion when starting from raw BVH.
Workflow Constraint
Keep orchestration logic in SkyPilot YAML. Do not add a Python runner script for the SONIC locomotion fine-tuning path.
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.
- 2d ago Changed 79e4c6eb8d2a
- 10d ago First seen · 50 lines · 38 tokens per session scan A 8a16b6fcf945
retargeting is a skill published in the GitHub repository nebius/nebius-physical-ai (27 stars, last pushed today), licensed Apache-2.0. It adds 38 tokens to every session and 362 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
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