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
92%
Does it follow best practices?
Impact
88%
1.35xAverage score across 3 eval scenarios
Passed
No known issues
Quality
Discovery
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 clearly defines its scope, provides abundant natural trigger terms, and explicitly addresses both when to use and when not to use the skill. The inclusion of a DO NOT USE FOR section is particularly effective for reducing conflict with related skills. The description is comprehensive yet focused, making it easy for Claude to select appropriately from a large skill set.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: discovers available capacity across regions/projects, analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. | 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). The DO NOT USE FOR section adds extra clarity on boundaries. | 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', 'multi-project capacity search', 'quota analysis', 'model availability', 'region comparison', 'check TPM availability'. These are highly natural phrases. | 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 |
Implementation
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, highly actionable skill with excellent workflow clarity and progressive disclosure. The phased approach with explicit validation (especially Phase 3.5 quota checking) and clear hand-off points demonstrates strong design. The main weakness is moderate verbosity — dual PowerShell/Bash code blocks and the lengthy inline quota validation code in Phase 3.5 could potentially be tightened or moved to a script.
Suggestions
Consider moving the Phase 3.5 quota validation logic into a dedicated script (e.g., scripts/validate_quota.ps1/.sh) to reduce inline code duplication and improve conciseness, since the other phases already use pre-built scripts.
| Dimension | Reasoning | Score |
|---|---|---|
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 — az CLI commands, script invocations with specific parameters, and jq/PowerShell patterns for quota checking. The error handling table maps specific errors to specific resolutions. Code is copy-paste ready with clear parameter placeholders. | 3 / 3 |
Workflow Clarity | Excellent multi-phase workflow with clear sequencing (Phases 1-5), explicit validation checkpoints (Phase 3.5 validates quota after discovery), feedback loops (re-run Phase 2 if needed, hand off to quota skill if no capacity), and a clear decision tree in Phase 4 with numbered options. The annotated table example makes the expected output unambiguous. | 3 / 3 |
Progressive Disclosure | Well-structured with clear navigation: overview table up front, phased workflow for detail, and well-signaled one-level-deep references to related skills (preset, customize, quota, parent router). Scripts are referenced by path without inlining their contents. The error handling table provides quick lookup without burying details in prose. | 3 / 3 |
Total | 11 / 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.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- microsoft/azure-skills
- Commit
d02fd24
- Reviewed
Table of Contents
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