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 covers all key 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 conflict with adjacent skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: discovers available capacity, analyzes quota limits, compares availability, recommends optimal deployment locations. Also specifies scope boundaries (what NOT to use it for). | 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 realistic phrases users would naturally 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 |
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 capacity discovery skill with excellent workflow clarity, strong actionability through concrete commands and scripts, and good progressive disclosure via clear hand-offs to related skills. The main weakness is moderate verbosity — dual PowerShell/Bash examples throughout and the lengthy inline Phase 3.5 quota validation code could be tightened or moved to a script, but overall the content is high quality and operationally sound.
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.
| 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 — script invocations with clear parameter patterns, az CLI commands for validation, and specific jq/PowerShell patterns for quota checking. The example output table with specific values makes expected output unambiguous. | 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 handling table with resolutions, and clear hand-off points. The feedback loop for quota failures (hand off to quota skill) and the option to re-run Phase 2 are well-defined. | 3 / 3 |
Progressive Disclosure | Clean overview structure with well-signaled references to related skills (preset, customize, quota, parent router). Scripts are referenced by path in a table rather than inlined. Content is appropriately split — the skill focuses on discovery and delegates deployment to other skills. | 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
- jonathan-vella/azure-agentic-infraops
- Commit
ec7b8ff
- Reviewed
Table of Contents
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