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 agentmods add skills/nebius/nebius-physical-ai/gpu-allocation-fallbacknpx skills add nebius/nebius-physical-ai --skill gpu-allocation-fallbackgit 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/gpu-allocation-fallback)<a href="https://agentmods.dev/skills/nebius/nebius-physical-ai/gpu-allocation-fallback"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/gpu-allocation-fallback.svg" alt="Measured on agentmods" 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 | $0.00057 | $0.00396 |
| Opus 5 | $0.00028 | $0.00198 |
| Sonnet 5 | $0.00011 | $0.00079 |
| Haiku 4.5 | $0.00006 | $0.00040 |
Grade A, and why
gpu-allocation-fallback 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 5d 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
GPU Allocation Fallback
Send typed results to POST /api/agent/gpu-allocation/attempt with a stable
logical_allocation, the requested compatibility/execution/disk invariants,
typed failure evidence, and the identical compatible preemptible candidate.
The grounded route uses zero model tokens.
Only quota/capacity exhaustion, insufficient GPU or Unschedulable, and no compatible product/affinity count. Auth, RBAC, network, image-pull, checkpoint, application, runtime, cancellation, and timeout failures never count. The default prompt occurs on the third qualifying failure, or immediately when deterministic preflight proves on-demand cannot succeed and compatible preemptible capacity is available.
Never switch automatically. Present the returned question and proposed action.
Accept through POST /api/agent/gpu-allocation/consent with its single-use,
action-digest-bound confirm_token; decline with accept: false. A decline
keeps on-demand and suppresses the same evidence until materially new evidence
arrives.
Preserve GPU family/product/count, image/digest, SM and RT-core requirements, backend, model, workload tier, execution mode, and boot-disk count/bytes. The state record contains only redacted digests, classification, selected pool, and consent outcome. Success or a changed logical allocation resets attempt state.
Verify changes with:
npa/.venv/bin/python -m pytest \
npa/tests/cli/test_agent_gpu_allocation_fallback.py \
npa/tests/cli/test_agent_backend_render.py -q
What ships with it
1 file 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.
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.
- 5d ago First seen · 38 lines · 57 tokens per session scan A dbcad04f2406
gpu-allocation-fallback is a skill published in the GitHub repository nebius/nebius-physical-ai (27 stars, last pushed today), licensed Apache-2.0. It adds 57 tokens to every session and 396 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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