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).
94
92%
Does it follow best practices?
Impact
96%
2.66xAverage 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 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 pattern for reducing routing conflicts.
| 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 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 with neighboring skills. | 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 routing skill that effectively serves as a hub for three deployment sub-skills. Its strengths are strong actionability with real CLI commands, clear workflow sequencing with validation checkpoints, and excellent progressive disclosure. The main weakness is moderate verbosity — the intent detection section presents the same routing logic in three overlapping formats (decision tree, routing rules table, and multi-mode chaining section) which could be consolidated.
Suggestions
Consolidate the intent detection decision tree and routing rules table into a single representation to reduce redundancy and improve conciseness.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is reasonably well-structured but includes some verbose sections that could be tightened. The intent detection decision tree and routing rules table have overlap, and the project selection confirmation step includes UI mockups that add bulk. However, most content is genuinely instructive rather than explaining things Claude already knows. | 2 / 3 |
Actionability | Provides concrete, executable Azure CLI commands for model catalog queries and quota checks, specific environment variable names, clear routing logic with exact keyword triggers, and step-by-step confirmation flows. The guidance is specific enough to act on directly. | 3 / 3 |
Workflow Clarity | Multi-step processes are clearly sequenced with explicit validation checkpoints: project resolution has a defined order (env var → prompt → query), pre-deployment validation requires both SKU support and quota checks before proceeding, and multi-mode chaining is clearly documented with a capacity → deploy pattern including user confirmation gates. | 3 / 3 |
Progressive Disclosure | Excellent hub-and-spoke structure: the SKILL.md serves as a clear routing overview with a quick reference table pointing to three sub-skills (preset, customize, capacity) via one-level-deep references. Cross-references to the quota skill are also well-signaled. Content is appropriately split between the overview and sub-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
- microsoft/github-copilot-for-azure
- Commit
771a666
- Reviewed
Table of Contents
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