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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.

92

1.35x
Quality

92%

Does it follow best practices?

Impact

88%

1.35x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

85%

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

This is a well-structured capacity discovery skill with strong workflow clarity and actionability. The phased approach with explicit validation (especially Phase 3.5 quota checking) and clear handoff points demonstrates thoughtful design. The main weakness is moderate verbosity — dual PowerShell/bash examples inline and some redundancy between the Quick Reference table and workflow content add token cost that could be reduced.

Suggestions

Consider consolidating PowerShell and bash examples by showing only one inline and noting the other variant exists with the same parameters, or use a single generic syntax.

DimensionReasoningScore

Conciseness

Generally efficient but has some redundancy: the Quick Reference table repeats information found in the workflow, the 'When to Use' section is somewhat verbose for Claude, and both PowerShell and bash variants are shown inline which doubles code blocks. The Phase 3.5 quota validation section is quite lengthy with inline code that could be in a script.

2 / 3

Actionability

Provides concrete, executable commands for every phase — script invocations with clear parameter patterns, az CLI commands for validation, and specific jq/PowerShell patterns for quota checking. The scripts are clearly named with their purposes, and the workflow gives copy-paste-ready commands.

3 / 3

Workflow Clarity

Excellent multi-phase workflow with clear sequencing (Phases 1-5), explicit validation checkpoints (Phase 3.5 quota validation with annotated results), error recovery table, and a clear decision tree at Phase 4 for next steps. The feedback loop of re-running Phase 2 for another model is explicitly offered.

3 / 3

Progressive Disclosure

Well-structured with a quick reference table up front, clear section hierarchy, and appropriate cross-references to related skills (preset, customize, quota, parent router). References are one level deep and clearly signaled. The Phase 3.5 inline code is borderline but justified since it's a validation step not covered by the scripts.

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.

This is an excellent skill description that covers all dimensions well. It provides specific concrete actions, comprehensive natural trigger terms, explicit use/don't-use guidance, and clear boundaries that distinguish it from related skills. The inclusion of a DO NOT USE FOR section is particularly effective for reducing false positive skill selection.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: discovers available capacity, analyzes quota limits, compares availability, recommends optimal deployment locations. Also specifies scope boundaries (what it does NOT do).

3 / 3

Completeness

Clearly answers both 'what' (discovers capacity, analyzes quotas, compares availability, recommends deployment locations) and 'when' (explicit USE FOR and DO NOT USE FOR clauses with specific trigger scenarios).

3 / 3

Trigger Term Quality

Excellent coverage of natural trigger terms users would say: 'find capacity', 'check quota', 'where can I deploy', 'best region for capacity', 'check TPM availability', 'model availability', 'region comparison'. These are highly natural phrases a user would use.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive with a clear niche (Azure OpenAI capacity discovery). The DO NOT USE FOR section explicitly delineates boundaries against related skills like deployment or quota increase requests, minimizing conflict risk.

3 / 3

Total

12

/

12

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.

Validation11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository
microsoft/github-copilot-for-azure
Reviewed

Table of Contents

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