Azure/AKS-Skills is a collection of agent skills for operating Azure Kubernetes Service clusters after they are provisioned. It helps compatible coding agents investigate incidents, control costs, assess AKS Automatic readiness, run GPU and inference workloads, capture packet-level evidence, and design clusters. The catalogue entries provide the skills, MCP integration, and plugin configuration for these AKS operations.
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 Azure/AKS-Skills --skill aks-gpu-inferencegit clone --depth 1 https://github.com/Azure/AKS-SkillsWrote 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/azure/aks-skills/aks-gpu-inference)<a href="https://agentmods.dev/skills/azure/aks-skills/aks-gpu-inference"><img src="https://agentmods.dev/badge/skills/azure/aks-skills/aks-gpu-inference/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/azure/aks-skills/aks-gpu-inference"><img src="https://agentmods.dev/badge/skills/azure/aks-skills/aks-gpu-inference.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 | $0.00206 | $0.01335 |
| Opus 5 | $0.00103 | $0.00668 |
| Sonnet 5 | $0.00041 | $0.00267 |
| Haiku 4.5 | $0.00021 | $0.00134 |
Grade A, and why
aks-gpu-inference scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **DCGM metrics port differs by install model.** AKS-**managed** GPU pools expose the DCGM exporter on **port 19400**; NVIDIA upstream / GPU-Operator use **9400**. A `curl localhost:9400/metrics` against a managed pool How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AKS GPU & Inference (Day-2)
Operate and troubleshoot GPU and model-serving workloads on AKS after they exist. This is the failure → check → fix loop, not a setup guide — for enabling GPUs, KAITO, or a model runway, use azure-skills airunway-aks-setup.
Operating rules
- Read-only by default. Diagnose and propose; do not scale, cordon, delete node pools, or delete KAITO Workspaces unless the user explicitly asks — deleting a Workspace has a cost trap (below).
- Evidence before conclusion. GPU symptoms have layered causes (quota → capacity → scheduling → driver). Quote the event, node capacity, or condition that supports the root cause.
The four walls (check in this order)
Most "my GPU workload won't run" incidents are one of these, and they stack — clearing one exposes the next:
- GPU quota is 0 by default. Every N-series VM family starts at 0 vCPUs in every region, and quota is measured in vCPUs, not GPUs. Check:
az vm list-usage --location <region> -o tableand look for the family row (e.g.Standard NC Family vCPUs,Standard NCADSA100v4 Family vCPUs). Error signature: "Insufficient vCPU quota for Standard NCASv3_T4 Family…". - Quota ≠ capacity ≠ SKU-eligibility. Even with quota, a region/zone can lack physical GPU capacity, and AKS gates some GPU SKUs behind a separate eligibility ticket. Check what's actually creatable:
az vm list-skus --location <region> --resource-type virtualMachines -o table. - The taint/toleration/nodeSelector triad. GPU pools use the convention taint
sku=gpu:NoSchedule. A GPU pod must carry the matching toleration (key=sku, value=gpu, effect=NoSchedule), requestnvidia.com/gpu: 1, and (by convention) selectaccelerator: nvidia. A missing toleration is the most common Pending cause after quota. - No
nvidia.com/gpuadvertised on the node → a driver / device-plugin model mismatch. See the driver decision matrix in references/gpu-scheduling.md.
What ships with it
4 files 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.
- 9d ago First seen · 65 lines · 206 tokens per session scan A cdfb9caaf115
aks-gpu-inference is a skill published in the GitHub repository Azure/AKS-Skills (4 stars, last pushed yesterday), licensed MIT. It adds 206 tokens to every session and 1,335 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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