Identify Azure OpenAI regions and projects with sufficient GlobalStandard capacity.
npx skills add https://github.com/rrbanda/skills --skill capacity-rrbanda
Please help me install this Agent Skill. Skill: capacity Source: https://github.com/rrbanda/skills/tree/main/skills/cloud-infra/capacity Command: npx skills add https://github.com/rrbanda/skills --skill capacity-rrbanda
Finding suitable Azure OpenAI deployment locations by analyzing capacity, available regions, and project presence to minimize trial-and-error and deployment delays.
Run the capacity discovery script with a model name, version, and a minimum TPM target to generate a ranked deployment shortlist.
High-intent search queries and answers about installing and using this skill.
To find Azure OpenAI capacity, you can run a discovery script that cross-references AI projects and computes region quotas using the GlobalStandard SKU to return a ranked deployment shortlist.
Quota-aware ranking analyzes remaining OpenAI GlobalStandard capacity across subscriptions and regions, surfacing the locations with sufficient available quota to minimize trial-and-error deployment delays.
You need az, jq, and python3 installed to query Azure REST APIs, cross-reference AI projects, and compute region quotas for identifying available deployment locations.
Yes, you can specify a model name, version, and a minimum TPM target when running the capacity discovery script to generate a ranked deployment shortlist.
The best way to avoid quota limits is to perform capacity discovery across all subscriptions and regions first, using a script to return a quota-aware deployment shortlist before applying presets or customizations.
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