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ClaudeWave
Skill210 repo starsupdated 3d ago

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).

Install in Claude Code
Copy
git clone --depth 1 https://github.com/jonathan-vella/apex /tmp/deploy-model && cp -r /tmp/deploy-model/.archive/_archived_skills/microsoft-foundry/models/deploy-model ~/.claude/skills/deploy-model
Then start a new Claude Code session; the skill loads automatically.

SKILL.md

# Deploy Model

Unified entry point for all Azure OpenAI model deployment workflows. Analyzes user intent and routes to the appropriate deployment mode.

## Quick Reference

| Mode                   | When to Use                                      | Sub-Skill                                |
| ---------------------- | ------------------------------------------------ | ---------------------------------------- |
| **Preset**             | Quick deployment, no customization needed        | [preset/SKILL.md](preset/SKILL.md)       |
| **Customize**          | Full control: version, SKU, capacity, RAI policy | [customize/SKILL.md](customize/SKILL.md) |
| **Capacity Discovery** | Find where you can deploy with specific capacity | [capacity/SKILL.md](capacity/SKILL.md)   |

## Intent Detection

Analyze the user's prompt and route to the correct mode:

```
User Prompt
    │
    ├─ Simple deployment (no modifiers)
    │  "deploy gpt-4o", "set up a model"
    │  └─> PRESET mode
    │
    ├─ Customization keywords present
    │  "custom settings", "choose version", "select SKU",
    │  "set capacity to X", "configure content filter",
    │  "PTU deployment", "with specific quota"
    │  └─> CUSTOMIZE mode
    │
    ├─ Capacity/availability query
    │  "find where I can deploy", "check capacity",
    │  "which region has X capacity", "best region for 10K TPM",
    │  "where is this model available"
    │  └─> CAPACITY DISCOVERY mode
    │
    └─ Ambiguous (has capacity target + deploy intent)
       "deploy gpt-4o with 10K capacity to best region"
       └─> CAPACITY DISCOVERY first → then PRESET or CUSTOMIZE
```

### Routing Rules

| Signal in Prompt                                      | Route To                 | Reason                                    |
| ----------------------------------------------------- | ------------------------ | ----------------------------------------- |
| Just model name, no options                           | **Preset**               | User wants quick deployment               |
| "custom", "configure", "choose", "select"             | **Customize**            | User wants control                        |
| "find", "check", "where", "which region", "available" | **Capacity**             | User wants discovery                      |
| Specific capacity number + "best region"              | **Capacity → Preset**    | Discover then deploy quickly              |
| Specific capacity number + "custom" keywords          | **Capacity → Customize** | Discover then deploy with options         |
| "PTU", "provisioned throughput"                       | **Customize**            | PTU requires SKU selection                |
| "optimal region", "best region" (no capacity target)  | **Preset**               | Region optimization is preset's specialty |

### Multi-Mode Chaining

Some prompts require two modes in sequence:

**Pattern: Capacity → Deploy**
When a user specifies a capacity requirement AND wants deployment:

1. Run **Capacity Discovery** to find regions/projects with sufficient quota
2. Present findings to user
3. Ask: "Would you like to deploy with **quick defaults** or **customize settings**?"
4. Route to **Preset** or **Customize** based on answer

> 💡 **Tip:** If unsure which mode the user wants, default to **Preset** (quick deployment). Users who want customization will typically use explicit keywords like "custom", "configure", or "with specific settings".

## Project Selection (All Modes)

Before any deployment, resolve which project to deploy to. This applies to **all** modes (preset, customize, and after capacity discovery).

### Resolution Order

1. **Check `PROJECT_RESOURCE_ID` env var** — if set, use it as the default
2. **Check user prompt** — if user named a specific project or region, use that
3. **If neither** — query the user's projects and suggest the current one

### Confirmation Step (Required)

**Always confirm the target before deploying.** Show the user what will be used and give them a chance to change it:

```
Deploying to:
  Project:  <project-name>
  Region:   <region>
  Resource: <resource-group>

Is this correct? Or choose a different project:
  1. ✅ Yes, deploy here (default)
  2. 📋 Show me other projects in this region
  3. 🌍 Choose a different region
```

If user picks option 2, show top 5 projects in that region:

```
Projects in <region>:
  1. project-alpha (rg-alpha)
  2. project-beta (rg-beta)
  3. project-gamma (rg-gamma)
  ...
```

> ⚠️ **Never deploy without showing the user which project will be used.** This prevents accidental deployments to the wrong resource.

## Pre-Deployment Validation (All Modes)

Before presenting any deployment options (SKU, capacity), always validate both of these:

1. **Model supports the SKU** — query the model catalog to confirm the selected model+version supports the target SKU:

   ```bash
   az cognitiveservices model list --location <region> --subscription <sub-id> -o json
   ```

   Filter for the model, extract `.model.skus[].name` to get supported SKUs.

2. **Subscription has available quota** — check that the user's subscription has unallocated quota for the SKU+model combination:
   ```bash
   az cognitiveservices usage list --location <region> --subscription <sub-id> -o json
   ```
   Match by usage name pattern `OpenAI.<SKU>.<model-name>` (e.g., `OpenAI.GlobalStandard.gpt-4o`). Compute `available = limit - currentValue`.

> ⚠️ **Warning:** Only present options that pass both checks. Do NOT show hardcoded SKU lists — always query dynamically. SKUs with 0 available quota should be shown as ❌ informational items, not selectable options.

> 💡 **Quota management:** For quota increase requests, usage monitoring, and troubleshooting quota errors, defer to the [quota skill](../../quota/quota.md) instead of duplicating that guidance inline.

## Prerequisites

All deployment modes require:

- Azure CLI installed and authenticated (`az login`)
- Active Azure subscription with deployment permis
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