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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).

Quality

87%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

70%Weight 40%Scale 1-5

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

A well-designed router skill with strong workflow sequencing, explicit validation gates, and genuinely executable az CLI checks. Its weaknesses are internal redundancy (routing logic and sub-skill listings each appear twice) and a bundle mismatch: every referenced sub-skill file is missing while the bundled scripts are orphaned.

Suggestions

Merge the Intent Detection ASCII tree and the Routing Rules table into a single routing artifact — they encode the same signals, and one table with a Mode column plus example prompts would cut ~30 lines.

Fix the bundle mismatch: either include preset/, customize/, and capacity/SKILL.md so the referenced paths resolve, or restructure the skill to be self-contained; also reference the existing scripts/generate_deployment_url.sh and .ps1 from the body (e.g., in the post-deployment flow) since they are currently orphaned.

Collapse the Quick Reference table and the closing Sub-Skills section into one listing — both enumerate the same three sub-skills with 'when to use' descriptions.

DimensionReasoningScore

Conciseness

Mostly efficient tables and commands, but there is real duplication: the Intent Detection ASCII tree restates nearly every signal in the Routing Rules table (e.g., capacity-number + 'best region' routing appears in both), and the Quick Reference table and Sub-Skills section list the same three sub-skills twice. This goes beyond 'minor instances that could be trimmed'.

3 / 5

Actionability

Concrete, copy-pasteable commands with real syntax ('az cognitiveservices model list --location <region> --subscription <sub-id> -o json'), a concrete quota-matching pattern ('OpenAI.<SKU>.<model-name>') with 'available = limit - currentValue', and exact confirmation dialog text. The gap: the actual deployment command — the skill's core action — is not shown here, so the common case is not executable from this file alone.

4 / 5

Workflow Clarity

Clear sequences with explicit validation checkpoints and error-recovery loops: numbered project Resolution Order, a required confirmation step before any deployment, pre-deployment validation of both SKU support and quota with 'Only present options that pass both checks', and recovery paths ('If user picks option 2, show top 5 projects'; zero-quota SKUs shown as non-selectable ❌). This matches the anchor with feedback loops and checkpoints.

5 / 5

Progressive Disclosure

The design is right — a router overview with clearly signaled one-level-deep references (Quick Reference table, Sub-Skills section) — but the referenced files (preset/SKILL.md, customize/SKILL.md, capacity/SKILL.md) do not exist in the bundle, so navigation dead-ends, while the two files that do exist (scripts/generate_deployment_url.sh and .ps1) are never referenced from the body.

3 / 5

Total

15

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An exemplary description: concrete capability listing with parameters, explicit USE FOR trigger phrases with natural synonyms, third-person voice, and an exclusion list that routes adjacent tasks to the correct tools. The only nit is the mild buzzword 'intelligent intent-based routing', which costs nothing in clarity.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions with comprehensive coverage: 'Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects.' Each capability is named with its concrete parameters. The only fluff is 'intelligent intent-based routing', a minor buzzword that does not obscure the concrete actions.

5 / 5

Completeness

Explicitly answers both: what ('Handles quick preset deployments, fully customized deployments... and capacity discovery') and when ('USE FOR:' followed by concrete trigger phrases), plus a DO NOT USE FOR exclusion list — stronger than the anchor-5 example.

5 / 5

Trigger Term Quality

'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' provides comprehensive natural-phrase coverage including synonyms and question forms users would actually say.

5 / 5

Distinctiveness Conflict Risk

A clear niche (Azure OpenAI model deployment) with distinct triggers, and the DO NOT USE FOR clause actively disambiguates adjacent skills by naming alternatives: 'listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create)'.

5 / 5

Total

20

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

relative_links

Relative link issues: 6 missing, 2 suspicious

Warning

Total

15

/

16

Passed

Repository

microsoft/azure-skills

Path

.github/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model/SKILL.md

Reviewed

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