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azure-compute

Azure VM/VMSS router. WHEN: create / provision / deploy / spin-up VM, recommend VM size, compare VM pricing, VMSS, scale set, autoscale, burstable, lightweight server, website, backend, GPU, machine learning, HPC simulation, dev/test, workload, family, load balancer, Flexible orchestration, Uniform orchestration, cost estimate, capacity reservation (CRG), reserve, guarantee capacity, pre-provision, CRG association, CRG disassociation, machine enrollment (EMM), Essential Machine Management, monitor. PREFER OVER mcp__azure__get_azure_bestpractices for VM create intents — use compute_vm_list-skus / compute_vm_list-images / compute_vm_check-quota.

Quality

85%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

75%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.

The body is an exemplary lean router — concise, unambiguous intent classification, and good disambiguation rules — but its progressive disclosure is broken against the actual bundle: every workflow link is a dead path and the four existing reference files are orphaned. As written, an agent following the skill would fail at the first step of its own mandatory routing procedure.

Suggestions

Fix the routing-table links to match the bundle: either ship the four workflow files at the paths the table references (workflows/<name>/<name>.md), or relink the table to the files that actually exist so no entry point is a dead path.

Make the existing references/ files discoverable from SKILL.md — add a short 'Supporting references' section linking vm-families.md, vm-quotas.md, retail-prices-api.md, and vmss-guide.md, since they are currently orphaned and unreachable via the workflow-first routing the body mandates.

Add a post-routing checkpoint to the workflow: after opening the matched workflow, confirm it covers the user's request and fall back to a clarifying question (or azure-prepare) if it does not, closing the validation loop the routing procedure currently lacks.

DimensionReasoningScore

Conciseness

The ~24-line body is pure routing signal with zero padding: it never explains what Azure, VMs, or scale sets are, and every sentence either classifies intent or names a file to open ("never route directly to references/* files"). It matches the lean anchor — every token earns its place — and sits well above anchor 4's "minor instances of over-explanation", of which there are none.

5 / 5

Actionability

Guidance is concrete and executable for a router skill: an exact decision procedure ("classify the user intent below, open the matched workflow file, then load only the reference files that workflow requests"), a routing table mapping intents to specific file paths, and a crisp disambiguation rule ("route to azure-prepare" for app deployments). It is not a 5 because the guidance bottoms out at file pointers — the workflow internals, fallback behavior, and the compute_vm_* tools named in the description are not carried into the body, leaving minor gaps.

4 / 5

Workflow Clarity

The routing sequence is clear and well-guarded: mandatory workflow-first routing, an intent table with explicit "Use when" conditions, and an instruction to "ask a clarifying question to disambiguate between the workflows" when intent is unclear — matching anchor 4's clear sequence with only minor gaps. It falls short of anchor 5 because there is no checkpoint after opening a workflow (e.g., confirming the workflow actually covers the request before proceeding, or a fallback when none matches).

4 / 5

Progressive Disclosure

Scored against the actual bundle per the guideline: all four routing-table links ("workflows/vm-recommender/vm-recommender.md", "workflows/vm-creator/vm-creator.md", etc.) point to files that do not exist in the bundle, while the four real reference files (references/vm-families.md, vm-quotas.md, retail-prices-api.md, vmss-guide.md) are never linked from SKILL.md and are therefore unreachable from the entry point. The body's structure is well-designed in the abstract, but navigation is broken in practice — every link is dead and the actual supporting material is buried, matching anchor 2 rather than anchor 3 (where references are at least present and signaled).

2 / 5

Total

15

/

20

Passed

Description

91%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 dense, well-engineered routing description with explicit WHEN triggers, strong synonym coverage, and explicit conflict-resolution against a competing tool. Its only weaknesses are a terse capability statement and a few overly broad triggers (website, backend, monitor) that could pull in non-VM intents.

DimensionReasoningScore

Specificity

The description lists many concrete actions and scopes ("create / provision / deploy / spin-up VM, recommend VM size, compare VM pricing", "CRG association, CRG disassociation", "machine enrollment (EMM)") and even names specific tools ("compute_vm_list-skus / compute_vm_list-images / compute_vm_check-quota"), but the capability statement itself is a terse "Azure VM/VMSS router" with the rest delivered as a keyword list, leaving minor gaps in what the router actually does.

4 / 5

Completeness

It explicitly answers both questions: "what" via "Azure VM/VMSS router" with named tools, and "when" via an explicit "WHEN:" clause with concrete trigger phrases, matching the anchor for clearly answering both. The "what" is terse, but it is explicit and paired with a "PREFER OVER" directive that sharpens intent, so this is not the anchor-4 case of a weakly specified "when".

5 / 5

Trigger Term Quality

Trigger coverage is comprehensive with abundant synonyms and natural user phrasing: "create / provision / deploy / spin-up", "VMSS, scale set, autoscale, burstable", "lightweight server, website, backend, GPU, machine learning, HPC simulation, dev/test", plus expansions of abbreviations ("capacity reservation (CRG)", "Essential Machine Management"). It clearly matches the comprehensive-synonyms anchor and is well above anchor 4's "a few natural terms missing".

5 / 5

Distinctiveness Conflict Risk

The Azure VM/VMSS compute niche is clear and the explicit "PREFER OVER mcp__azure__get_azure_bestpractices for VM create intents" resolves the closest tool conflict. However, very broad triggers like "website, backend, monitor, workload, dev/test" risk overlap with app-deployment or monitoring skills, keeping it at anchor 4 (minor overlap risk with closely related skills) rather than 5.

4 / 5

Total

18

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 4 missing, 4 deeper-than-1-level

Warning

Total

15

/

16

Passed

Repository

microsoft/GitHub-Copilot-for-Azure

Path

plugins/azure-skills/skills/azure-compute/SKILL.md

Reviewed

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