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deploy-model

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

89

2.66x
Quality

85%

Does it follow best practices?

Impact

96%

2.66x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

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 capabilities, comprehensive natural trigger terms, explicit use/don't-use guidance, and clear boundaries against related skills. The DO NOT USE FOR section with specific alternative skill references is a particularly strong differentiator.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: quick preset deployments, fully customized deployments with specific parameters (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects.

3 / 3

Completeness

Clearly answers both 'what' (handles preset deployments, customized deployments, capacity discovery) and 'when' (explicit USE FOR and DO NOT USE FOR clauses with specific trigger phrases and boundary conditions).

3 / 3

Trigger Term Quality

Excellent coverage of natural trigger terms users would say: 'deploy model', 'deploy gpt', 'create deployment', 'find capacity', 'check model availability', 'where can I deploy', 'best region for model'. These are highly natural phrases.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive with clear boundaries. The DO NOT USE FOR section explicitly differentiates from listing deployments, deleting deployments, agent creation, and project creation skills, significantly reducing conflict risk.

3 / 3

Total

12

/

12

Passed

Implementation

70%

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 routing/orchestration skill that effectively serves as an entry point for model deployment workflows. Its strengths are excellent progressive disclosure with clear sub-skill references, strong workflow clarity with explicit validation and confirmation checkpoints, and practical intent detection logic. Its main weaknesses are some redundancy between the flowchart and routing rules table, and the fact that actionability is partially deferred to sub-skills that aren't available for evaluation.

Suggestions

Consolidate the intent detection flowchart and routing rules table into a single representation to reduce redundancy and improve conciseness.

Trim the project selection mock UI dialogs — Claude can generate appropriate confirmation prompts without the exact template being specified.

DimensionReasoningScore

Conciseness

The skill is reasonably efficient but includes some unnecessary verbosity — the routing rules table partially duplicates the intent detection flowchart, and the project selection section with its mock UI dialogs is somewhat padded. However, most content is genuinely instructive and not explaining things Claude already knows.

2 / 3

Actionability

The skill provides concrete CLI commands for validation checks (az cognitiveservices model list, az cognitiveservices usage list) and clear routing logic, but it's primarily a routing/orchestration skill that delegates actual deployment to sub-skills. The confirmation step templates and validation commands are actionable, but the core deployment instructions are deferred elsewhere.

2 / 3

Workflow Clarity

The workflow is clearly sequenced: intent detection → project selection → confirmation → pre-deployment validation → route to sub-skill. Multi-mode chaining is explicitly documented with a clear pattern. Validation checkpoints are explicit (model SKU support check, quota check), and there's a required confirmation step before any deployment to prevent accidental deployments.

3 / 3

Progressive Disclosure

Excellent progressive disclosure structure. The SKILL.md serves as a clear routing overview with a quick reference table linking to three sub-skills (preset, customize, capacity). References are one level deep, clearly signaled with relative paths, and the content appropriately keeps routing logic inline while deferring implementation details to sub-skills. Cross-references to quota skill are also well-placed.

3 / 3

Total

10

/

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
jonathan-vella/azure-agentic-infraops
Reviewed

Table of Contents

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