preset
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
83
75%
Does it follow best practices?
Impact
96%
4.17xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.github/skills/microsoft-foundry/models/deploy-model/preset/SKILL.mdQuality
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 a strong skill description that excels across all dimensions. It provides specific capabilities, comprehensive trigger terms, explicit use/don't-use guidance, and clear boundaries against related skills. The DO NOT USE FOR clause is particularly effective at reducing conflict risk with the companion 'customize' skill.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: deploys Azure OpenAI models, analyzes capacity across regions, checks current region first, shows alternatives. These are clear, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both 'what' (deploys Azure OpenAI models to optimal regions by analyzing capacity) and 'when' (explicit USE FOR and DO NOT USE FOR clauses with specific trigger scenarios and boundary conditions). | 3 / 3 |
Trigger Term Quality | Includes strong natural trigger terms users would say: 'quick deployment', 'optimal region', 'best region', 'automatic region selection', 'fast setup', 'multi-region capacity check', 'high availability deployment', 'deploy to best location'. Also includes negative triggers to reduce conflicts. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with explicit DO NOT USE FOR clauses that delineate boundaries against a 'customize' skill. The focus on automatic/optimal region selection creates a clear niche distinct from manual configuration skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides a well-organized overview of an Azure OpenAI deployment workflow with clear phase breakdowns and error handling. Its main weaknesses are the lack of fully executable command examples for the core workflow, missing validation/feedback loops for a multi-step cloud operation, and some content redundancy between sections. The progressive disclosure intent is present but unverifiable without bundle files.
Suggestions
Add complete, executable command examples for the core deployment path with realistic parameter placeholders (e.g., a full `az cognitiveservices account deployment create` command with all required flags).
Add explicit validation checkpoints in the workflow, such as verifying deployment provisioning state after creation and a retry/rollback step if deployment fails.
Remove the 'What This Skill Does' numbered list since it duplicates the Quick Workflow and Deployment Phases sections, improving conciseness.
Ensure the referenced 'references/workflow.md' file exists in the bundle and move the detailed phases table content there, keeping SKILL.md as a leaner overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is reasonably structured but includes some redundancy—the 'What This Skill Does' numbered list largely duplicates the Quick Workflow and Deployment Phases sections. The Notes section and some table entries could be tightened. However, it doesn't over-explain basic concepts. | 2 / 3 |
Actionability | Key Azure CLI commands are listed in the phases table and error handling section, and the Advanced Usage section has executable snippets. However, the core deployment workflow lacks complete, copy-paste-ready command sequences—the phases table shows command fragments rather than full executable examples with parameters filled in. | 2 / 3 |
Workflow Clarity | The two workflow paths (fast path and alternative region path) are clearly diagrammed, and the phases table provides good sequencing. However, there are no explicit validation checkpoints or feedback loops (e.g., verify deployment succeeded before proceeding, validate capacity response before deploying), which is important for a multi-step cloud deployment operation. | 2 / 3 |
Progressive Disclosure | The skill references a workflow detail file at 'references/workflow.md' and links to a quota skill, which is good progressive disclosure structure. However, no bundle files were provided, so we cannot verify these references exist. The main file itself contains substantial detail (phases table, error table) that could arguably live in reference files, making the overview heavier than ideal. | 2 / 3 |
Total | 8 / 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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