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

92%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

Quality

Content

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 routing skill that clearly defines intent detection, project selection, and pre-deployment validation before delegating to appropriate sub-skills. Its strengths are excellent progressive disclosure, concrete CLI commands for validation, and clear workflow sequencing with validation checkpoints. The main weakness is moderate verbosity — the routing tables and confirmation flow templates could be tightened without losing clarity.

Dimension	Reasoning	Score
Conciseness	The skill is reasonably efficient but includes some verbose sections — the ASCII decision tree, multiple routing tables, and the detailed project selection confirmation flow add bulk. The scope callout at the top is lengthy. However, most content is instructional rather than explaining things Claude already knows.	2 / 3
Actionability	Provides concrete CLI commands for validation (az cognitiveservices model list, az cognitiveservices usage list), specific filter patterns (OpenAI.<SKU>.<model-name>), clear routing logic with explicit keyword triggers, and a well-defined confirmation step with exact output format. The skill delegates execution details to sub-skills appropriately.	3 / 3
Workflow Clarity	The multi-step workflow is clearly sequenced: intent detection → project selection → pre-deployment validation → route to sub-skill. Validation checkpoints are explicit (model SKU support check, quota availability check), with clear rules about not proceeding without confirmation. The multi-mode chaining pattern includes a feedback step (present findings, then ask user before proceeding).	3 / 3
Progressive Disclosure	Excellent structure: the SKILL.md serves as a routing overview with a quick reference table, then delegates to three clearly-signaled sub-skills (preset, customize, capacity) via one-level-deep references. Cross-references to quota skill and foundry-agent create skill are well-placed. Content is appropriately split between overview and sub-skills.	3 / 3
	Total	11 / 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.

This is an excellent skill description that covers all key dimensions well. It provides specific capabilities, comprehensive natural trigger terms, explicit 'use when' and 'do not use when' guidance, and clear boundaries to avoid conflicts with related skills. The DO NOT USE FOR section is a particularly strong addition for routing accuracy in a multi-skill environment.

Dimension	Reasoning	Score
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. Very detailed about what it does.	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 scenarios). The DO NOT USE FOR section adds extra clarity for routing.	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 natural phrases a user would actually type.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive with explicit boundary-setting via the DO NOT USE FOR clause, which differentiates it from listing deployments, deleting deployments, agent creation, and project creation skills. The Azure OpenAI deployment niche is clearly carved out.	3 / 3
	Total	12 / 12 Passed

Validation

100%

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 — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: microsoft/github-copilot-for-azure
Commit: d4563f6

Reviewed: 3 days ago

Table of Contents

Discovery Implementation Validation

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