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aks-gpu-inference

Diagnose Day-2 AKS GPU and KAITO incidents using profile-aware, read-only evidence. WHEN: 'Insufficient nvidia.com/gpu', GPU pod Pending, model-load OOM, DCGM/VRAM, KAITO Workspace not ready, or GPU autoscaling.

Install / Use

npx skills add microsoft/skills --skill aks-gpu-inference

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

87/100

Supported Platforms

Universal

Our assessment of aks-gpu-inference

aks-gpu-inference scores 87/100 on our quality scale, 2217th of 4,597 Development & Engineering skills we index (top 49%).

Its SKILL.md is 3.0 KB long, split into 7 sections and no code examples: a solid amount of guidance for an agent.

With 3,051 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
11/20
Description
15/15
Adoption
15/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 17 days ago, so aks-gpu-inference is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

aks-gpu-inference compared with similar skills

All 4 of these similar skills score higher than aks-gpu-inference; compare them before choosing.

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aks-gpu-inference (this skill)by microsoft873.1k17d agoSKILL.md
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algorithmic-artby anthropics100177.9k18d agoSKILL.md
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Frequently asked questions

How do I install aks-gpu-inference?
Run npx skills add microsoft/skills --skill aks-gpu-inference. The install tabs above show the steps for each supported agent.
Which AI agents does aks-gpu-inference work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is aks-gpu-inference safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is aks-gpu-inference still maintained?
The repository was last updated 17 days ago, so aks-gpu-inference is actively maintained.

name: aks-gpu-inference description: "Diagnose Day-2 AKS GPU and KAITO incidents using profile-aware, read-only evidence. WHEN: 'Insufficient nvidia.com/gpu', GPU pod Pending, model-load OOM, DCGM/VRAM, KAITO Workspace not ready, or GPU autoscaling. DO NOT USE FOR: setup (airunway-aks-setup), non-GPU incidents (aks-troubleshooting), standalone VM quota (azure-quotas), or generic cost (cost-analysis or cost-optimization from the optional azure-cost plugin)." license: MIT metadata: author: Microsoft version: "1.1.1"

AKS GPU Inference Day-2

Quick Reference

| Property | Value | |---|---| | Best for | AKS GPU/KAITO failures | | Evidence | Bound target, events/status, gpuProfile, DCGM |

When to Use This Skill

Use for scheduling, VRAM/OOM, KAITO readiness, and workload scaling. Route exclusions as described above.

MCP Tools

Use a fitting host-advertised Azure read or the references' read-only queries.

Host Capability Gate

Before executing any referenced pipeline, verify that the host authorizes the required shell and kubectl/az commands against the bound target. File-backed collection also requires approved artifact storage and access to any bundled files it uses. A governed Azure CLI tool alone does not establish these capabilities. Equivalent host reads may replace commands only where their advertised schemas provide the required evidence.

If execution is unavailable or prohibited, say so and analyze supplied or redacted events, status, logs, and metrics, or give the operator a scoped collection plan. State which reads did not run and leave conclusions requiring missing evidence unconfirmed. Never route kubectl through Azure MCP or bypass host policy. The authorization requirements below still apply.

Workflow

  1. Bind subscription, cluster, kube context, pool, and affected resource.
  2. Capture exact events/status and observed gpuProfile; state which reads ran.
  3. Route to scheduling, observability, KAITO, or scaling.
  4. Separate container/host OOM from device-allocation failures. For OOMKilled, correlate container memory limits/usage and node conditions. For Managed+Install device-memory evidence, use the exporter on port 19400 and incident-window FB_USED/FB_FREE; sizing tables do not establish a cause.
  5. For KAITO not-ready, warn that Workspace deletion leaves its GPU pools; cleanup is separate and needs explicit authorization.
  6. Report evidence, confidence, missing evidence, and owner handoff.

Require explicit authorization before scaling, cordon/drain, deletion, add-on enablement, or monitoring mutation.

Error Handling

| Condition | Response | |---|---| | Missing/contradictory evidence | Mark unconfirmed; request the owner/read | | Unknown profile | Preserve evidence; do not prescribe stack repair | | Mutation required | Propose separately and wait for authorization |

Related Skills

View on GitHub
GitHub Stars3.1k
CategoryDevelopment
Updated17d ago
Forks349

Languages

TypeScript

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions