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 add-workbench-toolgit 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/add-workbench-tool)<a href="https://agentmods.dev/skills/nebius/nebius-physical-ai/add-workbench-tool"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/add-workbench-tool/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/add-workbench-tool"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/add-workbench-tool.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 | $0.00049 | $0.02604 |
| Opus 5 | $0.00024 | $0.01302 |
| Sonnet 5 | $0.00010 | $0.00521 |
| Haiku 4.5 | $0.00005 | $0.00260 |
Grade A, and why
add-workbench-tool 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 4d 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add A Workbench Tool
Adding a tool is not one edit. A minimal tool touches ~18 files across six phases, and ~12 separate gates fail if you skip one. Work the phases in order: each phase's gate can only pass once the previous phase exists.
CONTRIBUTING.md is the prose rationale for this layer and
docs/architecture/contributor-context.md the architecture. This skill is the
ordered execution path.
Decide The Archetype First
The archetype decides which phases apply. Getting this wrong means building a container nothing routes to, or a GPU stage that lands on the default pod.
| Archetype | Runs in | Phases | Examples |
|---|---|---|---|
| In-process / CPU | default npa workflow pod | A, B, D, E | insights, dataset, scenario_gen |
| Containerized service | own image, deployed to workbench namespace |
A–E | detection_training, lancedb |
| Containerized GPU stage | own image, SkyPilot task pod | A–E + image routing | cosmos3, nurec |
In-process tools need no Dockerfile and no image-routing entry. Do not add a
container "for symmetry" — a container that no toolRef routes to is dead
weight the packaging and security gates still police.
The One Architectural Rule
One implementation, three thin clients. Put behavior in
npa/src/npa/workbench/<tool_snake>/; the FastAPI service, the CLI, and the SDK
all call into it. Never implement training, inference, ingest, or status logic
twice.
The cleanest reference to copy is detection-training:
npa/src/npa/workbench/detection_training/service.pynpa/src/npa/workbench/detection_training/schemas.pynpa/src/npa/cli/workbench/detection_training.pynpa/src/npa/sdk/workbench/detection_training.py
LeRobot (npa/src/npa/cli/workbench/lerobot.py) is the richest tool but keeps
much of its logic as CLI orchestration. Read it for CLI surface breadth, not as
the layering model to copy.
Naming
Pick the name once; it derives everything else. <tool> is kebab-case,
<tool_snake> is snake_case.
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.
- 4d ago Changed 415cd77c3c6f
- 8d ago First seen · 198 lines · 49 tokens per session scan A 82add7707c2b
add-workbench-tool is a skill published in the GitHub repository nebius/nebius-physical-ai (29 stars, last pushed today), licensed Apache-2.0. It adds 49 tokens to every session and 2,604 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 2026-09-03.
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