Discover Azure OpenAI capacity, quotas, and best regions for deployment.
This skill is primarily for read-only Azure OpenAI capacity discovery and quota analysis, with no separate secret requirement or custom external endpoint declared. Given the Microsoft-hosted open-source source and clear scope, overall risk is low, but it does rely on Azure CLI authentication and queries subscription, region, and project data accessible to the user, so account scope and local script execution should be noted.
The material states authentication is via Azure CLI (`az login`). While no extra environment variables or API keys are required, it reuses the user's existing Azure session and subscription permissions; if the local Azure session is overly privileged, the query scope expands accordingly.
The README explicitly describes complex REST API queries across all accessible regions and projects for capacity and quota discovery, indicating that relevant requests are sent to Azure services. No unknown third-party endpoint is evident, and the egress appears aligned with the stated function, but it is still routine account-data transmission to the cloud.
The documentation lists executable PowerShell and shell scripts (such as `discover_and_rank.ps1/.sh` and `query_capacity.ps1/.sh`) and invokes `az` commands plus local script logic, so it has the ability to execute local scripts/commands. This is a normal capability for this type of skill, and no system privilege request beyond its purpose is evident.
Its stated scope is 'all regions and projects the user has access to,' and it reads subscription, project, model version, capacity, and quota information for ranking and analysis; the material also emphasizes it is read-only and does not deploy. No broad local file read/write behavior is described, but cloud-side visibility may be fairly broad.
The source is an open-source Microsoft GitHub repository, making the code auditable, and it has some community adoption (222 stars), both of which materially reduce supply-chain risk. No signs of closed-source exfiltration, suspicious prompt injection, or unrelated privilege requests are evident; only the undeclared license and unknown maintenance status remain minor completeness concerns.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "capacity" skill from askskill: 1. Download https://raw.githubusercontent.com/microsoft/GitHub-Copilot-for-Azure/main/plugin/skills/microsoft-foundry/models/deploy-model/capacity/SKILL.md 2. Save it as ~/.claude/skills/capacity/SKILL.md 3. Reload skills and tell me it's ready
Check which Azure regions have available capacity for GPT-4o, list quota status for each region, and recommend the top 3 regions for deployment.
A comparison of regional capacity and quotas, plus recommended deployment regions.
Search available capacity for o3-mini across my Azure projects, compare TPM quotas by project and region, and identify the best place for a new deployment.
An analysis of capacity across projects and regions, with the best deployment location identified.
I need at least 200K TPM of model capacity. Analyze which Azure regions can meet this requirement and explain the pros and cons of each candidate region.
A list of regions meeting the capacity threshold, comparative analysis, and a recommendation.
Finds available Azure OpenAI model capacity across all accessible regions and projects. Recommends the best deployment location based on capacity requirements.
| Property | Description |
|---|---|
| Purpose | Find where you can deploy a model with sufficient capacity |
| Scope | All regions and projects the user has access to |
| Output | Ranked table of regions/projects with available capacity |
| Action | Read-only analysis — does NOT deploy. Hands off to preset or customize |
| Authentication | Azure CLI (az login) |
After discovery → hand off to preset or customize for actual deployment.
Pre-built scripts handle the complex REST API calls and data processing. Use these instead of constructing commands manually.
| Script | Purpose | Usage |
|---|---|---|
scripts/discover_and_rank.ps1 | Full discovery: capacity + projects + ranking | Primary script for capacity discovery |
scripts/discover_and_rank.sh | Same as above (bash) | Primary script for capacity discovery |
scripts/query_capacity.ps1 | Raw capacity query (no project matching) | Quick capacity check or version listing |
scripts/query_capacity.sh | Same as above (bash) | Quick capacity check or version listing |
az account show --query "{Subscription:name, SubscriptionId:id}" --output table
Extract model name from user prompt. If version is unknown, query available versions:
.\scripts\query_capacity.ps1 -ModelName <model-name>
./scripts/query_capacity.sh <model-name>
This lists available versions. Use the latest version unless user specifies otherwise.
Run the full discovery script with model name, version, and minimum capacity target:
.\scripts\discover_and_rank.ps1 -ModelName <model-name> -ModelVersion <version> -MinCapacity <target>
./scripts/discover_and_rank.sh <model-name> <version> <min-capacity>
💡 The script automatically queries capacity across ALL regions, cross-references with the user's existing projects, and outputs a ranked table sorted by: meets target → project count → available capacity.
After discovery identifies candidate regions, validate that the user's subscription actually has available quota in each region. Model capacity (from Phase 3) shows what the platform can support, but subscription quota limits what this specific user can deploy.
# For each candidate region from discovery results:
$usageData = az cognitiveservices usage list --location <region> --subscription $SUBSCRIPTION_ID -o json 2>$null | ConvertFrom-Json
# Check quota for each SKU the model supports
# Quota names follow pattern: OpenAI.<SKU>.<model-name>
$usageEntry = $usageData | Where-Object { $_.name.value -eq "OpenAI.<SKU>.<model-name>" }
if ($usageEntry) {
$quotaAvailable = $usageEntry.limit - $usageEntry.currentValue
} else {
$quotaAvailable = 0 # No quota allocated
}
# For each candidate region from discovery results:
usage_json=$(az cognitiveservices usage list --location <region> --subscription "$SUBSCRIPTION_ID" -o json 2>/dev/null)
# Extract quota for specific SKU+model
quota_available=$(echo "$usage_json" | jq -r --arg name "OpenAI.<SKU>.<model-name>" \
'.[] | select(.name.value == $name) | .limit - .currentValue')
Annotate discovery results:
…
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