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).

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

Quality

Content

77%

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

A well-organized routing entry point with concrete validation commands, a clear sequenced workflow, and explicit confirmation checkpoints. Its main weaknesses are redundant restatements of the routing logic and progressive-disclosure gaps where referenced sub-skills do not exist in the bundle while existing scripts go unreferenced.

Suggestions

Collapse the overlapping routing logic — keep either the 'Intent Detection' tree or the 'Routing Rules' table, and fold 'Multi-Mode Chaining' into the survivor rather than restating the same decisions three ways.

Ship or stub the referenced sub-skills (preset/SKILL.md, customize/SKILL.md, capacity/SKILL.md) so the signaled one-level-deep navigation resolves, or note their absence explicitly.

Reference the existing scripts/generate_deployment_url.sh and .ps1 from the body (e.g., when surfacing a post-deployment portal link) so the bundle files are not orphaned.

Dimension	Reasoning	Score
Conciseness	The body is mostly lean and assumes Claude's competence (no basic-concept explanation), but the routing logic is restated three times — the ASCII 'Intent Detection' tree, the 'Routing Rules' table, and the 'Multi-Mode Chaining' section — so it could be tightened; not a 1 because there is no padded concept explanation, and not a 3 because of the redundant decision-logic representations.	2 / 3
Actionability	Provides copy-paste-ready executable commands ('az cognitiveservices model list ...', 'az cognitiveservices usage list ...') plus concrete keyword-based routing rules and a literal confirmation dialog template; not a 2 because the guidance is executable and specific rather than pseudocode or abstract.	3 / 3
Workflow Clarity	Sequences project resolution (env var → prompt → query), a mandatory confirmation step with user feedback, and pre-deployment validation (SKU support + quota) with explicit 'Only present options that pass both checks' checkpoints; not a 2 because validation and the feedback loop are explicit rather than implied.	3 / 3
Progressive Disclosure	The overview is well structured with one-level-deep, clearly signaled sub-skill references (preset/SKILL.md, customize/SKILL.md, capacity/SKILL.md) in a Quick Reference table, but those referenced directories are absent from the bundle and the existing scripts/ files (generate_deployment_url.sh/.ps1) are not referenced anywhere; not a 3 because the signaled navigation targets are missing and present bundle files are orphaned.	2 / 3
	Total	10 / 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.

A high-quality, third-person description that concretely enumerates capabilities, supplies natural trigger terms, explicitly states when to use it, and proactively distinguishes itself from adjacent skills via a DO NOT USE FOR block. It is concise and free of vague fluff.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects' — matching the anchor for several specific concrete actions; not a 2 because it goes beyond naming the domain into enumerated, parameterized actions.	3 / 3
Completeness	Explicitly answers what ('Handles quick preset deployments...capacity discovery') and when via the 'USE FOR:' trigger clause plus a 'DO NOT USE FOR:' negative-trigger block, satisfying the both-what-and-when anchor; not a 2 because the when is explicit, not merely implied.	3 / 3
Trigger Term Quality	The 'USE FOR:' list (deploy model, deploy gpt, create deployment, set up model, find capacity, where can I deploy, best region for model, capacity analysis) gives broad coverage of phrasings a user would naturally say; not a 2 because common variations are well represented rather than partial.	3 / 3
Distinctiveness Conflict Risk	A clear Azure OpenAI model-deployment niche with distinct triggers and a 'DO NOT USE FOR:' block redirecting overlapping tasks (listing deployments to the MCP tool, agent/project creation to their own skills), making wrong-skill conflicts unlikely; not a 2 because the negative triggers explicitly carve out adjacent skills.	3 / 3
	Total	12 / 12 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
relative_links	Relative link issues: 6 missing, 2 suspicious	Warning

	Total	15 / 16 Passed

Repository: microsoft/azure-skills
Commit: 2cd48ca

Reviewed: about 9 hours ago

Table of Contents

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