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Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, create agent, update agent, add tool to agent, invoke agent, agent.yaml, agent insights, pull agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, prompt optimizer, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

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

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modelsdeploy-modelcustomizereferencescustomize-workflow.md

≈3.4k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Customize Workflow — Detailed Phase Instructions

Reference for: models/deploy-model/customize/SKILL.md

Phase 1: Verify Authentication

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

If not logged in: az login

Set subscription if needed:

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

Phase 2: Get Project Resource ID

Check PROJECT_RESOURCE_ID env var. If not set, prompt user.

Format: /subscriptions/{sub}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}/projects/{project}


Phase 3: Parse and Verify Project

Parse ARM resource ID to extract components:

$SUBSCRIPTION_ID = ($PROJECT_RESOURCE_ID -split '/')[2]
$RESOURCE_GROUP = ($PROJECT_RESOURCE_ID -split '/')[4]
$ACCOUNT_NAME = ($PROJECT_RESOURCE_ID -split '/')[8]
$PROJECT_NAME = ($PROJECT_RESOURCE_ID -split '/')[10]

Verify project exists and get region:

az account set --subscription $SUBSCRIPTION_ID
az cognitiveservices account show \
  --name $ACCOUNT_NAME \
  --resource-group $RESOURCE_GROUP \
  --query location -o tsv

Phase 4: Get Model Name

List available models if not provided:

az cognitiveservices account list-models \
  --name $ACCOUNT_NAME \
  --resource-group $RESOURCE_GROUP \
  --query "[].name" -o json

Present sorted unique list. Allow custom model name entry.

Detect model format:

# Get model format (e.g., OpenAI, Anthropic, Meta-Llama, Mistral, Cohere)
MODEL_FORMAT=$(az cognitiveservices account list-models \
  --name "$ACCOUNT_NAME" \
  --resource-group "$RESOURCE_GROUP" \
  --query "[?name=='$MODEL_NAME'].format" -o tsv | head -1)

MODEL_FORMAT=${MODEL_FORMAT:-"OpenAI"}
echo "Model format: $MODEL_FORMAT"

💡 Model format determines the deployment path:

  • OpenAI — Standard CLI, TPM-based capacity, RAI policies, version upgrade policies
  • Anthropic — REST API with modelProviderData, capacity=1, no RAI, no version upgrade
  • All other formats (Meta-Llama, Mistral, Cohere, etc.) — Standard CLI, capacity=1 (MaaS), no RAI, no version upgrade

Phase 5: List and Select Model Version

az cognitiveservices account list-models \
  --name $ACCOUNT_NAME \
  --resource-group $RESOURCE_GROUP \
  --query "[?name=='$MODEL_NAME'].version" -o json

Recommend latest version (first in list). Default to "latest" if no versions found.


Phase 6: List and Select SKU

⚠️ Warning: Never hardcode SKU lists — always query live data.

Step A — Query model-supported SKUs:

az cognitiveservices model list \
  --location $PROJECT_REGION \
  --subscription $SUBSCRIPTION_ID -o json

Filter: model.name == $MODEL_NAME && model.version == $MODEL_VERSION, extract model.skus[].name.

Step B — Check subscription quota per SKU:

az cognitiveservices usage list \
  --location $PROJECT_REGION \
  --subscription $SUBSCRIPTION_ID -o json

Quota key pattern: OpenAI.<SKU>.<model-name>. Calculate available = limit - currentValue.

Step C — Present only deployable SKUs (available > 0). If no SKUs have quota, direct user to the quota skill.


Phase 7: Configure Capacity

⚠️ Non-OpenAI models (MaaS): If MODEL_FORMAT != "OpenAI", capacity is always 1 (pay-per-token billing). Skip capacity configuration and set DEPLOY_CAPACITY=1. Proceed to Phase 7c (Anthropic) or Phase 8.

For OpenAI models only — query capacity via REST API:

# Current region capacity
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=$MODEL_FORMAT&modelName=$MODEL_NAME&modelVersion=$MODEL_VERSION"

Filter result for properties.skuName == $SELECTED_SKU. Read properties.availableCapacity.

Capacity defaults by SKU (OpenAI only):

SKU Unit Min Max Step Default
ProvisionedManaged PTU 50 1000 50 100
Others (TPM-based) TPM 1000 min(available, 300000) 1000 min(10000, available/2)

Validate user input: must be >= min, <= max, multiple of step. On invalid input, explain constraints.

Phase 7b: Cross-Region Fallback

If no capacity in current region, query ALL regions:

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

Filter: properties.skuName == $SELECTED_SKU && properties.availableCapacity > 0. Sort descending by capacity.

Present available regions. After user selects region, find existing projects there:

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

If projects exist, let user select one and update $ACCOUNT_NAME, $RESOURCE_GROUP. If none, direct to project/create skill.

Re-run capacity configuration with new region's available capacity.

If no region has capacity: fail with guidance to request quota increase, check existing deployments, or try different model/SKU.


Phase 7c: Anthropic Model Provider Data (Anthropic models only)

⚠️ Only execute this phase if MODEL_FORMAT == "Anthropic". For OpenAI and other models, skip to Phase 8.

Anthropic models require modelProviderData in the deployment payload. Collect this before deployment.

Step 1: Prompt user to select industry

Present the following list and ask the user to choose one:

 1. None                    (API value: none)
 2. Biotechnology           (API value: biotechnology)
 3. Consulting              (API value: consulting)
 4. Education               (API value: education)
 5. Finance                 (API value: finance)
 6. Food & Beverage         (API value: food_and_beverage)
 7. Government              (API value: government)
 8. Healthcare              (API value: healthcare)
 9. Insurance               (API value: insurance)
10. Law                     (API value: law)
11. Manufacturing           (API value: manufacturing)
12. Media                   (API value: media)
13. Nonprofit               (API value: nonprofit)
14. Technology              (API value: technology)
15. Telecommunications      (API value: telecommunications)
16. Sport & Recreation      (API value: sport_and_recreation)
17. Real Estate             (API value: real_estate)
18. Retail                  (API value: retail)
19. Other                   (API value: other)

⚠️ Do NOT pick a default industry or hardcode a value. Always ask the user. This is required by Anthropic's terms of service. The industry list is static — there is no REST API that provides it.

Store selection as SELECTED_INDUSTRY (use the API value, e.g., technology).

Step 2: Fetch tenant info (country code and organization name)

TENANT_INFO=$(az rest --method GET \
  --url "https://management.azure.com/tenants?api-version=2024-11-01" \
  --query "value[0].{countryCode:countryCode, displayName:displayName}" -o json)

COUNTRY_CODE=$(echo "$TENANT_INFO" | jq -r '.countryCode')
ORG_NAME=$(echo "$TENANT_INFO" | jq -r '.displayName')

PowerShell version:

$tenantInfo = az rest --method GET `
  --url "https://management.azure.com/tenants?api-version=2024-11-01" `
  --query "value[0].{countryCode:countryCode, displayName:displayName}" -o json | ConvertFrom-Json

$countryCode = $tenantInfo.countryCode
$orgName = $tenantInfo.displayName

Store COUNTRY_CODE and ORG_NAME for use in Phase 13.


Phase 8: Select RAI Policy (Content Filter)

⚠️ Note: RAI policies only apply to OpenAI models. Skip this phase if MODEL_FORMAT != "OpenAI" (Anthropic, Meta-Llama, Mistral, Cohere, etc. do not use RAI policies).

Present options:

  1. Microsoft.DefaultV2 — Balanced filtering (recommended). Filters hate, violence, sexual, self-harm.
  2. Microsoft.Prompt-Shield — Enhanced prompt injection/jailbreak protection.
  3. Custom policies — Organization-specific (configured in Azure Portal).

Default: Microsoft.DefaultV2.


Phase 9: Configure Advanced Options

Options are SKU-dependent:

A. Dynamic Quota (GlobalStandard only)

  • Auto-scales beyond base allocation when capacity available
  • Default: enabled

B. Priority Processing (ProvisionedManaged only)

  • Prioritizes requests during high load; additional charges apply
  • Default: disabled

C. Spillover (any SKU)

  • Redirects requests to backup deployment at capacity
  • Requires existing deployment; list with:
az cognitiveservices account deployment list \
  --name $ACCOUNT_NAME \
  --resource-group $RESOURCE_GROUP \
  --query "[].name" -o json
  • Default: disabled

Phase 10: Configure Version Upgrade Policy

⚠️ Note: Version upgrade policies only apply to OpenAI models. Skip this phase if MODEL_FORMAT != "OpenAI".

Policy Description
OnceNewDefaultVersionAvailable Auto-upgrade to new default (Recommended)
OnceCurrentVersionExpired Upgrade only when current expires
NoAutoUpgrade Manual upgrade only

Default: OnceNewDefaultVersionAvailable.


Phase 11: Generate Deployment Name

List existing deployments to avoid conflicts:

az cognitiveservices account deployment list \
  --name $ACCOUNT_NAME \
  --resource-group $RESOURCE_GROUP \
  --query "[].name" -o json

Auto-generate: use model name as base, append -2, -3 etc. if taken. Allow custom override. Validate: ^[\w.-]{2,64}$.


Phase 12: Review Configuration

Display summary of all selections for user confirmation before proceeding:

  • Model, version, deployment name
  • SKU, capacity (with unit), region
  • RAI policy, version upgrade policy
  • Advanced options (dynamic quota, priority, spillover)
  • Account, resource group, project

User confirms or cancels.


Phase 13: Execute Deployment

💡 MODEL_FORMAT was already detected in Phase 4. Use the stored value here.

Standard CLI deployment (non-Anthropic models):

Create deployment:

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

💡 Note: For non-OpenAI MaaS models, $DEPLOY_CAPACITY is 1 (set in Phase 7).

Anthropic model deployment (requires modelProviderData):

The Azure CLI does not support --model-provider-data. Use the ARM REST API directly.

⚠️ Industry, country code, and organization name should have been collected in Phase 7c.

echo "Creating Anthropic model deployment via REST API..."

az rest --method PUT \
  --url "https://management.azure.com/subscriptions/$SUBSCRIPTION_ID/resourceGroups/$RESOURCE_GROUP/providers/Microsoft.CognitiveServices/accounts/$ACCOUNT_NAME/deployments/$DEPLOYMENT_NAME?api-version=2024-10-01" \
  --body "{
    \"sku\": {
      \"name\": \"$SELECTED_SKU\",
      \"capacity\": 1
    },
    \"properties\": {
      \"model\": {
        \"format\": \"Anthropic\",
        \"name\": \"$MODEL_NAME\",
        \"version\": \"$MODEL_VERSION\"
      },
      \"modelProviderData\": {
        \"industry\": \"$SELECTED_INDUSTRY\",
        \"countryCode\": \"$COUNTRY_CODE\",
        \"organizationName\": \"$ORG_NAME\"
      }
    }
  }"

PowerShell version:

Write-Host "Creating Anthropic model deployment via REST API..."

$body = @{
    sku = @{
        name = $SELECTED_SKU
        capacity = 1
    }
    properties = @{
        model = @{
            format = "Anthropic"
            name = $MODEL_NAME
            version = $MODEL_VERSION
        }
        modelProviderData = @{
            industry = $SELECTED_INDUSTRY
            countryCode = $countryCode
            organizationName = $orgName
        }
    }
} | ConvertTo-Json -Depth 5

az rest --method PUT `
  --url "https://management.azure.com/subscriptions/$SUBSCRIPTION_ID/resourceGroups/$RESOURCE_GROUP/providers/Microsoft.CognitiveServices/accounts/$ACCOUNT_NAME/deployments/${DEPLOYMENT_NAME}?api-version=2024-10-01" `
  --body $body

💡 Note: Anthropic models use capacity: 1 (MaaS billing model), not TPM-based capacity. RAI policy is not applicable for Anthropic models.

Monitor deployment status:

az cognitiveservices account deployment show \
  --name $ACCOUNT_NAME \
  --resource-group $RESOURCE_GROUP \
  --deployment-name $DEPLOYMENT_NAME \
  --query "properties.provisioningState" -o tsv

Poll until Succeeded or Failed. Timeout after 5 minutes.

Get endpoint:

az cognitiveservices account show \
  --name $ACCOUNT_NAME \
  --resource-group $RESOURCE_GROUP \
  --query "properties.endpoint" -o tsv

On success, display deployment name, model, version, SKU, capacity, region, RAI policy, rate limits, endpoint, and Microsoft Foundry portal link.

Source: SKILL.md on GitHub

2 warnings3d4 checks · Risk SAFE
  • Gen Agent Trust Hub3d

    This skill provides a comprehensive environment for managing the end-to-end lifecycle of AI agents, models, and infrastructure on Microsoft Foundry. It includes sub-skills for deployment, evaluation, fine-tuning, and troubleshooting. The skill utilizes dynamic code execution and shell command wrappers, which are used within the context of local development and cloud orchestration. All external resources and dependencies originate from trusted organizations and well-known services.

  • Socket3d

    2 alerts: gptSecurity, gptAnomaly

  • Snyk3d

    Risk: LOW · No issues

  • Runlayer7mo

    36/36 files flagged

Signed by skilld at 04110d9. 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 last week
metadata
{
  "author": "Microsoft",
  "version": "1.2.26"
}

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