Preset Agent Skill

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

9k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

138 d ago

last touched

this folder, not the whole repository

Install

one command, takes just this skill from the repository

npx skills add https://github.com/microsoft/skills --skill preset

What comes with it

31 217 bytes besides the instruction

EXAMPLES.md

references/preset-workflow.md

references/workflow.md

The instruction itself

11 sections, as written by the author

Deploy Model to Optimal Region

Automates intelligent Azure OpenAI model deployment by checking capacity across regions and deploying to the best available option.

What This Skill Does

Verifies Azure authentication and project scope
Checks capacity in current project's region
If no capacity: analyzes all regions and shows available alternatives
Filters projects by selected region
Supports creating new projects if needed
Deploys model with GlobalStandard SKU
Monitors deployment progress

Prerequisites

Azure CLI installed and configured
Active Azure subscription with Cognitive Services read/create permissions
Azure AI Foundry project resource ID (PROJECT_RESOURCE_ID env var or provided interactively)
Format: /subscriptions/{sub-id}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}/projects/{project}
Found in: Azure AI Foundry portal → Project → Overview → Resource ID

Quick Workflow

Fast Path (Current Region Has Capacity)

1. Check authentication → 2. Get project → 3. Check current region capacity
→ 4. Deploy immediately

Alternative Region Path (No Capacity)

1. Check authentication → 2. Get project → 3. Check current region (no capacity)
→ 4. Query all regions → 5. Show alternatives → 6. Select region + project
→ 7. Deploy

Deployment Phases

| Phase | Action | Key Commands |

|-------|--------|-------------|

| 1. Verify Auth | Check Azure CLI login and subscription | az account show, az login |

| 2. Get Project | Parse PROJECT_RESOURCE_ID ARM ID, verify exists | az cognitiveservices account show |

| 3. Get Model | List available models, user selects model + version | az cognitiveservices account list-models |

| 4. Check Current Region | Query capacity using GlobalStandard SKU | az rest --method GET .../modelCapacities |

| 5. Multi-Region Query | If no local capacity, query all regions | Same capacity API without location filter |

| 6. Select Region + Project | User picks region; find or create project | az cognitiveservices account list, az cognitiveservices account create |

| 7. Deploy | Generate unique name, calculate capacity (50% available, min 50 TPM), create deployment | az cognitiveservices account deployment create |

For detailed step-by-step instructions, see workflow reference.

Error Handling

| Error | Symptom | Resolution |

|-------|---------|------------|

| Auth failure | az account show returns error | Run az login then az account set --subscription <id> |

| No quota | All regions show 0 capacity | Defer to the quota skill for increase requests and troubleshooting; check existing deployments; try alternative models |

| Model not found | Empty capacity list | Verify model name with az cognitiveservices account list-models; check case sensitivity |

| Name conflict | "deployment already exists" | Append suffix to deployment name (handled automatically by generate_deployment_name script) |

| Region unavailable | Region doesn't support model | Select a different region from the available list |

| Permission denied | "Forbidden" or "Unauthorized" | Verify Cognitive Services Contributor role: az role assignment list --assignee <user> |

Advanced Usage

# Custom capacity
az cognitiveservices account deployment create ... --sku-capacity <value>

# Check deployment status
az cognitiveservices account deployment show --name <acct> --resource-group <rg> --deployment-name <name> --query "{Status:properties.provisioningState}"

# Delete deployment
az cognitiveservices account deployment delete --name <acct> --resource-group <rg> --deployment-name <name>

Notes

SKU: GlobalStandard only — API Version: 2024-10-01 (GA stable)

microsoft-foundry - Parent skill for Azure AI Foundry operations
quota — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill
azure-quick-review - Review Azure resources for compliance
azure-cost-estimation - Estimate costs for Azure deployments
azure-validate - Validate Azure infrastructure before deployment

Other skills for the same job

different authors, same section of the catalogue

Lamindb

by christophacham

×3

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).

22k tokens

Github Workflow Automation

by ComeOnOliver

×3

Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.