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capacity

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

88

1.35x
Quality

88%

Does it follow best practices?

Impact

88%

1.35x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

81%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, highly actionable workflow with clear sequencing and validation checkpoints. The main flaws are redundancy — Phase 3.5's inline quota code duplicates functionality already in the bundled discovery script — and a Quick Reference table that restates the intro.

Suggestions

Remove or drastically shorten Phase 3.5: discover_and_rank.sh already queries subscription quota per region and outputs a quota column, so the inline dual-shell az/JQ code duplicates the script and contradicts the 'use scripts instead of constructing commands manually' directive — replace it with a one-line note to read the script's quota column and mark zero-quota regions ❌.

Merge the Quick Reference table with the opening paragraph (Purpose/Scope/Output/Action largely repeat the intro and the When to Use list) to reclaim tokens.

If per-SKU quota checking (beyond the script's GlobalStandard default) is genuinely needed, move that code into query_capacity.ps1/.sh as a parameter rather than inlining it in SKILL.md.

DimensionReasoningScore

Conciseness

The body is mostly lean (tables, terse phases, no explanations of known concepts), but the ~40-line Phase 3.5 manual quota code duplicates quota logic the bundled discover_and_rank.sh already performs, and the Quick Reference table repeats the intro paragraph. This goes beyond the 'minor instances' of the level-4 anchor, though the rest is too tight for level 2.

3 / 5

Actionability

Fully executable guidance throughout: copy-paste-ready `az account show` validation, parameterized script invocations in both PowerShell and bash, working quota-check code, a concrete annotated example table, and an error/cause/resolution table — matching the 'fully executable, covers common cases' anchor.

5 / 5

Workflow Clarity

Phases 1–5 are clearly sequenced with explicit validation checkpoints (prerequisite validation in Phase 1, quota validation with a ❌-marking rule in Phase 3.5, mandatory project confirmation in Phase 5) and an error-recovery table with feedback loops. The skill is read-only, so the destructive-operation cap does not apply.

5 / 5

Progressive Disclosure

Good structure: a scripts table with purposes, all four referenced scripts verified present in the bundle, and one-level-deep external references. However, Phase 3.5 inlines ~25 lines of dual-shell quota code that belongs in the scripts — contradicting its own directive "Use these instead of constructing commands manually" — a minor organization gap that keeps it below the top anchor.

4 / 5

Total

17

/

20

Passed

Description

92%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: concrete third-person capabilities, explicit trigger phrases, and clear negative boundaries that distinguish it from sibling deployment/quota skills. The only weakness is a handful of missing natural trigger phrasings.

DimensionReasoningScore

Specificity

Names the domain and multiple concrete third-person actions — "Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations" — with no meaningful coverage gaps, matching the comprehensive anchor.

5 / 5

Completeness

Explicitly answers what the skill does (three concrete capability sentences) and when to use it (USE FOR trigger phrases), plus negative scope (DO NOT USE FOR deployment, quota increases, listing deployments) — clearly matching the top anchor.

5 / 5

Trigger Term Quality

The USE FOR list has strong natural coverage including synonyms and user utterances ("where can I deploy", "check TPM availability", "region comparison"), but a few natural phrasings are missing (e.g., "quota limits", "which region", "where to deploy model X"), fitting the 'good coverage, a few terms missing' anchor rather than the comprehensive one.

4 / 5

Distinctiveness Conflict Risk

Clear niche with explicit boundaries and handoffs to the nearest sibling skills ("DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests"), giving minimal conflict risk with related skills.

5 / 5

Total

19

/

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: 10 suspicious

Warning

Total

15

/

16

Passed

Repository

microsoft/azure-skills

Path

.github/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model/capacity/SKILL.md

Reviewed

Table of Contents

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