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@04d245b
by microsoftmicrosoft/skills3.1k stars
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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).

Use this Skill: https://skilld.dev/gh/microsoft/skills/deploy-model

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presetreferencesworkflow.md

≈1.4k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Preset Deployment Workflow — Step-by-Step

Condensed implementation reference for preset (optimal region) model deployment. See SKILL.md for overview.

Table of Contents: Phase 1: Verify Authentication · Phase 2: Get Current Project · Phase 3: Get Model Name · Phase 4: Check Current Region Capacity · Phase 5: Query Multi-Region Capacity · Phase 6: Select Region and Project · Phase 7: Deploy Model


Phase 1: Verify Authentication

az account show --query "{Subscription:name, User:user.name}" -o table

If not logged in: az login

Switch subscription:

az account list --query "[].[name,id,state]" -o table
az account set --subscription <subscription-id>

Phase 2: Get Current Project

Read PROJECT_RESOURCE_ID from env or prompt user. Format: /subscriptions/{sub-id}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}/projects/{project}

Parse ARM ID components:

SUBSCRIPTION_ID=$(echo "$PROJECT_RESOURCE_ID" | sed -n 's|.*/subscriptions/\([^/]*\).*|\1|p')
RESOURCE_GROUP=$(echo "$PROJECT_RESOURCE_ID" | sed -n 's|.*/resourceGroups/\([^/]*\).*|\1|p')
ACCOUNT_NAME=$(echo "$PROJECT_RESOURCE_ID" | sed -n 's|.*/accounts/\([^/]*\)/projects.*|\1|p')
PROJECT_NAME=$(echo "$PROJECT_RESOURCE_ID" | sed -n 's|.*/projects/\([^/?]*\).*|\1|p')

Verify project exists and get region:

az account set --subscription "$SUBSCRIPTION_ID"

PROJECT_REGION=$(az cognitiveservices account show \
  --name "$PROJECT_NAME" \
  --resource-group "$RESOURCE_GROUP" \
  --query location -o tsv)

Phase 3: Get Model Name

If model not provided as parameter, list available models:

az cognitiveservices account list-models \
  --name "$PROJECT_NAME" \
  --resource-group "$RESOURCE_GROUP" \
  --query "[].name" -o tsv | sort -u

Get versions for selected model:

az cognitiveservices account list-models \
  --name "$PROJECT_NAME" \
  --resource-group "$RESOURCE_GROUP" \
  --query "[?name=='$MODEL_NAME'].{Name:name, Version:version, Format:format}" \
  -o table

Phase 4: Check Current Region Capacity

CAPACITY_JSON=$(az rest --method GET \
  --url "https://management.azure.com/subscriptions/$SUBSCRIPTION_ID/providers/Microsoft.CognitiveServices/locations/$PROJECT_REGION/modelCapacities?api-version=2024-10-01&modelFormat=OpenAI&modelName=$MODEL_NAME&modelVersion=$MODEL_VERSION")

CURRENT_CAPACITY=$(echo "$CAPACITY_JSON" | jq -r '.value[] | select(.properties.skuName=="GlobalStandard") | .properties.availableCapacity')

If CURRENT_CAPACITY > 0 → skip to Phase 7. Otherwise continue to Phase 5.


Phase 5: Query Multi-Region Capacity

ALL_REGIONS_JSON=$(az rest --method GET \
  --url "https://management.azure.com/subscriptions/$SUBSCRIPTION_ID/providers/Microsoft.CognitiveServices/modelCapacities?api-version=2024-10-01&modelFormat=OpenAI&modelName=$MODEL_NAME&modelVersion=$MODEL_VERSION")

Extract available regions (capacity > 0):

AVAILABLE_REGIONS=$(echo "$ALL_REGIONS_JSON" | jq -r '.value[] | select(.properties.skuName=="GlobalStandard" and .properties.availableCapacity > 0) | "\(.location)|\(.properties.availableCapacity)"')

Extract unavailable regions:

UNAVAILABLE_REGIONS=$(echo "$ALL_REGIONS_JSON" | jq -r '.value[] | select(.properties.skuName=="GlobalStandard" and (.properties.availableCapacity == 0 or .properties.availableCapacity == null)) | "\(.location)|0"')

If no regions have capacity, defer to the quota skill for increase requests. Suggest checking existing deployments or trying alternative models like gpt-4o-mini.


Phase 6: Select Region and Project

Present available regions to user. Store selection as SELECTED_REGION.

Find projects in selected region:

PROJECTS_IN_REGION=$(az cognitiveservices account list \
  --query "[?kind=='AIProject' && location=='$SELECTED_REGION'].{Name:name, ResourceGroup:resourceGroup}" \
  --output json)

If no projects exist — create new:

az cognitiveservices account create \
  --name "$HUB_NAME" \
  --resource-group "$RESOURCE_GROUP" \
  --location "$SELECTED_REGION" \
  --kind "AIServices" \
  --sku "S0" --yes

az cognitiveservices account create \
  --name "$NEW_PROJECT_NAME" \
  --resource-group "$RESOURCE_GROUP" \
  --location "$SELECTED_REGION" \
  --kind "AIProject" \
  --sku "S0" --yes

Phase 7: Deploy Model

Generate unique deployment name using scripts/generate_deployment_name.sh:

DEPLOYMENT_NAME=$(bash scripts/generate_deployment_name.sh "$ACCOUNT_NAME" "$RESOURCE_GROUP" "$MODEL_NAME")

Calculate capacity — 50% of available, minimum 50 TPM:

SELECTED_CAPACITY=$(echo "$ALL_REGIONS_JSON" | jq -r ".value[] | select(.location==\"$SELECTED_REGION\" and .properties.skuName==\"GlobalStandard\") | .properties.availableCapacity")
DEPLOY_CAPACITY=$(( SELECTED_CAPACITY / 2 ))
[ "$DEPLOY_CAPACITY" -lt 50 ] && DEPLOY_CAPACITY=50

Create deployment:

az cognitiveservices account deployment create \
  --name "$ACCOUNT_NAME" \
  --resource-group "$RESOURCE_GROUP" \
  --deployment-name "$DEPLOYMENT_NAME" \
  --model-name "$MODEL_NAME" \
  --model-version "$MODEL_VERSION" \
  --model-format "OpenAI" \
  --sku-name "GlobalStandard" \
  --sku-capacity "$DEPLOY_CAPACITY"

Monitor with az cognitiveservices account deployment show ... --query "properties.provisioningState" until Succeeded or Failed.

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub1mo

    This skill provides a unified workflow for Azure OpenAI model deployments, utilizing the Azure CLI for resource management. It incorporates sensible safety measures, including mandatory user confirmation before resource creation and dynamic validation of model availability.

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    Risk: LOW · No issues

Signed by skilld at 04d245b. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 20 hours ago.

Activeupdated 2 months ago
metadata
{
  "author": "Microsoft",
  "version": "1.0.0"
}

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