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 detection-traininggit 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/detection-training)<a href="https://agentmods.dev/skills/nebius/nebius-physical-ai/detection-training"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/detection-training/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/detection-training"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/detection-training.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 | $0.00050 | $0.02107 |
| Opus 5 | $0.00025 | $0.01053 |
| Sonnet 5 | $0.00010 | $0.00421 |
| Haiku 4.5 | $0.00005 | $0.00211 |
Grade A, and why
detection-training 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 yesterday.
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 — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Detection training (LanceDB view → Faster R-CNN)
This tool trains an object detector on a slice of a dataset rather than the whole thing. The input is a LanceDB materialized view, which is what makes the failure-mode workflow possible: curate a view of nighttime frames, or distant objects, or riders, then train and evaluate only on that slice.
Upstream is skills/tools/lancedb/SKILL.md — create the view with
npa workbench lancedb create-mv / refresh-mv before anything here. The end-to-end
pipeline is workflows/testing/bdd100k-pipeline.yaml, with a
walkthrough in docs/workbench/cookbooks/bdd100k-pipeline.md.
Two execution modes
Direct (the default) runs the work from your CLI invocation. Service mode calls a deployed Kubernetes endpoint:
npa workbench detection-training train --view <mv-name> --output-uri s3://<bucket>/runs/<id>/
npa workbench detection-training train --view <mv-name> --service --endpoint <url>
Deploy the service only when you want a persistent endpoint several runs share:
npa workbench detection-training deploy \
--project <alias> --cluster-name <name> \
--input-path s3://<bucket>/lancedb/<db>/ \
--output-path s3://<bucket>/detection/ \
--gpu-type h100 --namespace default \
--dry-run # prints the manifest without applying
npa workbench detection-training deploy --project <alias> --destroy
--gpu-type accepts h100, b200, l40s, and rtxpro6000; verify actual cluster labels and reservation placement before deploying. Auth defaults to token (the token comes from
the variable named by --token-env, default DETECTION_TRAINING_TOKEN);
--insecure-no-auth exists but should not be used. Official NPA GHCR images
pull anonymously, so --image-pull-secret defaults to empty. For an optional
operator-controlled private registry, pass the name of an existing standard
Kubernetes pull secret explicitly. Always --dry-run first and read the
manifest.
Train
npa workbench detection-training train \
--view <materialized-view> \
--output-uri s3://<bucket>/detection/runs/<id>/ \
--label-map '{"person":0,"rider":1,"car":2}' \
--epochs 10 --batch-size 8 --learning-rate 0.005 \
--wait --poll-seconds 30 --timeout-seconds 21600 \
--output json
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.
- yesterday Changed · +44 lines 8051a8b3ac33
- 9d ago First seen · 134 lines · 50 tokens per session scan A a52e395e3ce1
detection-training is a skill published in the GitHub repository nebius/nebius-physical-ai (27 stars, last pushed today), licensed Apache-2.0. It adds 50 tokens to every session and 2,107 once invoked, about $0.0003 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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