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.

1.35x

Quality

—

Does it follow best practices?

Impact

88%

1.35x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Quality

Content

85%

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

The content is highly actionable and well-structured with a clear validated workflow and properly disclosed bundle references. The only weakness is moderate verbosity from inline dual-language (PowerShell/bash) quota blocks and explanatory prose that could be condensed.

Suggestions

Condense Phase 3.5 by moving the full PowerShell/bash quota-validation snippets into a bundled script (like the other phases) and keeping only the invocation and the annotated result table inline.

Trim explanatory sentences such as 'Model capacity (from Phase 3) shows what the platform can support, but subscription quota limits what this specific user can deploy' to a brief parenthetical, since the surrounding context already conveys this.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with tables and code, but Phase 3.5 duplicates a full PowerShell and bash quota-validation block inline and includes explanatory prose ('Model capacity ... shows what the platform can support, but subscription quota limits ...') that pads rather than instructs; it could be tightened.	2 / 3
Actionability	Provides copy-paste-ready, executable guidance throughout — az CLI commands, parameterized script invocations (e.g. ./scripts/discover_and_rank.sh <model-name> <version> <min-capacity>), and concrete jq/ConvertFrom-Json snippets for quota extraction.	3 / 3
Workflow Clarity	A clearly sequenced 5-phase workflow with explicit validation checkpoints (Phase 1 prerequisites, Phase 3.5 quota validation with ❌ marking and a feedback path to the quota skill) and an error-handling table with resolutions, matching the score-3 anchor.	3 / 3
Progressive Disclosure	The body is a well-organized overview with one-level-deep references to real bundle scripts (verified scripts/discover_and_rank.ps1, .sh, query_capacity.ps1, .sh) and clearly signaled links to sibling skills (preset, customize, quota), with no nested reference chains.	3 / 3
	Total	11 / 12 Passed

Description

100%

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

The description is strong across all dimensions: third-person voice, concrete actions, an explicit USE FOR trigger list, and clear DO NOT USE FOR boundaries that distinguish it from deployment and quota skills. No improvements needed.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions in third person — 'Discovers available Azure OpenAI model capacity', 'Analyzes quota limits, compares availability', and 'recommends optimal deployment locations' — matching the score-3 anchor.	3 / 3
Completeness	It explicitly answers both what (discover/analyze/recommend capacity) and when, with an explicit 'USE FOR' trigger list and a 'DO NOT USE FOR' boundary, satisfying the score-3 anchor for both what AND when.	3 / 3
Trigger Term Quality	The 'USE FOR' clause covers natural user phrasings such as 'find capacity', 'check quota', 'where can I deploy', 'best region for capacity', and 'check TPM availability', giving good coverage of terms users would actually say.	3 / 3
Distinctiveness Conflict Risk	The read-only capacity-discovery niche is distinct, and the 'DO NOT USE FOR' clause explicitly fences off deployment (preset/customize) and quota-increase requests, making conflicts with sibling skills unlikely.	3 / 3
	Total	12 / 12 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
relative_links	Relative link issues: 10 suspicious	Warning

	Total	15 / 16 Passed

Repository: microsoft/azure-skills
Commit: 2cd48ca

Reviewed: about 8 hours ago

Table of Contents

Discovery Implementation Validation

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