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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. 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).

Quality

86%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured read-only diagnostic skill: lean overview, correct one-level-deep reference routing, real executable commands in the bundle, and strong safety gates around evidence confidence and authorization. The main gaps are repeated authorization language, a vague MCP Tools section, and no explicit validate-before-concluding checkpoint in the workflow.

Suggestions

Conciseness: state the authorization rule once (in Host Capability Gate) and reference it from the Workflow and Error Handling sections instead of restating it in step 5, the post-workflow paragraph, and the mutation table row.

Actionability: replace 'Use a fitting host-advertised Azure read or the references' read-only queries' in MCP Tools with the specific commands the references rely on (e.g., kubectl describe pod, az aks nodepool show --query gpuProfile) or name the exact host-read schemas that qualify.

Workflow clarity: add an explicit verification checkpoint before the final report step, e.g., 'Before concluding, confirm each hypothesis is supported by quoted evidence; otherwise mark it unconfirmed' — creating a validate-then-report feedback loop.

DimensionReasoningScore

Conciseness

The ~60-line body assumes AKS/kubectl competence and explains no background concepts, but the authorization caveat ('Require explicit authorization before scaling, cordon/drain, deletion...') is restated across the Host Capability Gate, workflow step 5, the post-workflow paragraph, and the Error Handling table — minor trimmable redundancy that keeps it below the lean anchor 5.

4 / 5

Actionability

Concrete evidence is named (exporter on port 19400, incident-window FB_USED/FB_FREE, gpuProfile, OOMKilled correlation with container limits) and executable commands sit one level deep in the references, matching 'mostly executable guidance'. It falls short of anchor 5 because 'Use a fitting host-advertised Azure read or the references' read-only queries' (MCP Tools) is vague and early steps like 'Bind subscription, cluster, kube context, pool' state what but not how.

4 / 5

Workflow Clarity

Six clearly sequenced steps with checkpoints present ('state which reads ran', 'Report evidence, confidence, missing evidence, and owner handoff') plus an Error Handling table mapping failure conditions to responses. Not anchor 5 because the diagnostic steps lack an explicit validate-before-conclude feedback loop; the destructive-operation cap does not apply since the skill is read-only and mutations are explicitly deferred to authorization.

4 / 5

Progressive Disclosure

The body is a concise overview whose workflow step 3 routes to four labeled references (scheduling, observability, KAITO, scaling); all four exist in references/, are one level deep, hold the executable commands and detail tables, and link only to external docs with no further skill-file nesting — a clear match to the well-signaled one-level-deep anchor.

5 / 5

Total

17

/

20

Passed

Description

87%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: it explicitly states what and when, uses exact trigger error strings an operator would naturally say, and pre-emptively routes every adjacent skill away. The only minor room for improvement is broadening trigger synonyms and the single-verb action vocabulary.

DimensionReasoningScore

Specificity

'Diagnose Day-2 AKS GPU and KAITO incidents using profile-aware, read-only evidence' names a precise domain and method, and the WHEN clause enumerates several concrete incident classes (scheduling, OOM, DCGM/VRAM, KAITO readiness, autoscaling). It fits anchor 4 over 3 because coverage goes beyond 1-2 actions, but not 5 because the action vocabulary is a single 'diagnose' verb with no enumeration of what the skill produces.

4 / 5

Completeness

Both questions are answered explicitly: the what ('Diagnose Day-2 AKS GPU and KAITO incidents using profile-aware, read-only evidence') and the when ('WHEN: ...' with concrete trigger phrases), matching the anchor-5 example structure exactly.

5 / 5

Trigger Term Quality

'Insufficient nvidia.com/gpu', 'GPU pod Pending', 'model-load OOM', 'KAITO Workspace not ready' are exact error strings and natural phrases an operator would say. Good coverage falls short of the comprehensive anchor 5 because compressed forms omit common variations such as 'CUDA out of memory', 'GPU quota exceeded', or 'pod stuck Pending'.

4 / 5

Distinctiveness Conflict Risk

'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...)' explicitly disambiguates every neighboring skill, giving a clear niche with minimal conflict risk.

5 / 5

Total

18

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

microsoft/GitHub-Copilot-for-Azure

Path

plugins/aks-skills/skills/aks-gpu-inference/SKILL.md

Reviewed

Table of Contents

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