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 trigger terms that match natural user language, explicit use/don't-use guidance for routing, and clear boundaries to avoid conflicts with related skills. The DO NOT USE FOR section is particularly valuable for disambiguation 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 | Very clearly distinguished from related skills with explicit DO NOT USE FOR boundaries (listing deployments, deleting deployments, agent creation, project creation). The focus on Azure OpenAI model deployment and capacity discovery is a clear niche. | 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 clearly defines intent detection, project selection, and pre-deployment validation as shared concerns, then delegates to focused sub-skills. The actionability is strong with concrete CLI commands and explicit validation requirements. Minor verbosity in the routing rules (table + flowchart overlap) and project confirmation UI mock-ups could be tightened, but overall the content is effective and well-organized.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient but includes some redundancy — the routing rules table largely duplicates the intent detection flowchart, and the project selection confirmation step is somewhat verbose with its mock UI. However, most content is genuinely instructive and not explaining things Claude already knows. | 2 / 3 |
Actionability | Provides concrete, executable CLI commands for model catalog and quota validation, specific routing logic with clear decision criteria, and actionable confirmation flows. The guidance is specific enough that Claude can follow it without guessing. | 3 / 3 |
Workflow Clarity | Multi-step processes are clearly sequenced: intent detection → project selection → pre-deployment validation → route to sub-skill. Validation checkpoints are explicit (model SKU support check, quota check), with clear rules about not proceeding without confirmation. The multi-mode chaining pattern includes a feedback loop asking the user how to proceed. | 3 / 3 |
Progressive Disclosure | Excellent structure as a routing/overview document that delegates detailed implementation to three sub-skills (preset, customize, capacity) with clear one-level-deep references. The quick reference table at the top provides immediate navigation. Cross-references to the quota skill are appropriately scoped. | 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
915f809
- Reviewed
Table of Contents
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