aymenfurter/polyclaw/setup-foundry

Name: setup-foundry
Author: aymenfurter

Claude Code and agent skill by aymenfurter/polyclaw

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By aymenfurter
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aymenfurter/polyclaw
Plugin foundry-agents
plugins/foundry-agents/skills/setup-foundry/SKILL.md
Updated 25 Mar 2026
Version 1.0.0

Source check pending

Checking that the SKILL.md file is still in the repository.

Provision a Microsoft Foundry resource, create a project, deploy a model (gpt-4.1-mini), and install the required Python SDK packages. This is a one-time setup skill. Use when the user says: "setup foundry", "provision foundry", "create foundry resource", "setup azure ai foundry", "install foundry sdk"

Install

npx vskill@latest install aymenfurter/polyclaw/setup-foundry

SKILL.md

Microsoft Foundry Setup

Provision a Foundry resource, project, and model deployment via Azure CLI, then install the Python SDK.

Step 1 -- Verify Azure CLI

bash

az version --output table

If the CLI is not installed, tell the user to install it from https://learn.microsoft.com/cli/azure/install-azure-cli and come back.

Step 2 -- Authenticate

bash

az account show --output table

If not logged in, run:

bash

az login

Step 3 -- Choose Configuration

Ask the user for the following values (or offer sensible defaults):

Setting	Default
Resource group name	`foundry-agents-rg`
Foundry resource name	`foundry-<username>` (must be globally unique)
Project name	`default`
Location	`eastus`
Model to deploy	`gpt-4.1-mini`
Deployment name	`gpt-4.1-mini`

Step 4 -- Create the Resource Group

bash

az group create --name <RESOURCE_GROUP> --location <LOCATION>

Step 5 -- Create the Foundry Resource

bash

az cognitiveservices account create \
    --name <FOUNDRY_RESOURCE_NAME> \
    --resource-group <RESOURCE_GROUP> \
    --kind AIServices \
    --sku S0 \
    --location <LOCATION> \
    --allow-project-management \
    --yes

Step 6 -- Set a Custom Subdomain

The custom subdomain is required for SDK access. It must be globally unique.

bash

az cognitiveservices account update \
    --name <FOUNDRY_RESOURCE_NAME> \
    --resource-group <RESOURCE_GROUP> \
    --custom-domain <FOUNDRY_RESOURCE_NAME>

Step 7 -- Create the Project

bash

az cognitiveservices account project create \
    --name <FOUNDRY_RESOURCE_NAME> \
    --resource-group <RESOURCE_GROUP> \
    --project-name <PROJECT_NAME> \
    --location <LOCATION>

Step 8 -- Deploy a Model

bash

az cognitiveservices account deployment create \
    --name <FOUNDRY_RESOURCE_NAME> \
    --resource-group <RESOURCE_GROUP> \
    --deployment-name <DEPLOYMENT_NAME> \
    --model-name <MODEL_NAME> \
    --model-version "2025-04-14" \
    --model-format OpenAI \
    --sku-capacity 10 \
    --sku-name GlobalStandard

If the model version or SKU is not available in the selected region, try Standard SKU or a different model version. You can list available models with:

bash

az cognitiveservices account list-models \
    --name <FOUNDRY_RESOURCE_NAME> \
    --resource-group <RESOURCE_GROUP> \
    --output table

Step 9 -- Install the Python SDK

bash

pip install azure-ai-projects --pre openai azure-identity python-dotenv

Step 10 -- Verify the Setup

Build the project endpoint from the resource and project names:

https://<FOUNDRY_RESOURCE_NAME>.services.ai.azure.com/api/projects/<PROJECT_NAME>

Run a quick verification:

bash

python3 -c "
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
 
endpoint = 'https://<FOUNDRY_RESOURCE_NAME>.services.ai.azure.com/api/projects/<PROJECT_NAME>'
client = AIProjectClient(endpoint=endpoint, credential=DefaultAzureCredential())
openai_client = client.get_openai_client()
resp = openai_client.responses.create(
    model='<DEPLOYMENT_NAME>',
    input='Say hello in one sentence.',
)
print('Model response:', resp.output_text)
print('Setup verified successfully.')
"

Replace the placeholders with the actual values chosen earlier.

Step 11 -- Save Environment Variables

Tell the user to note down or export these values for future use:

bash

export FOUNDRY_PROJECT_ENDPOINT="https://<FOUNDRY_RESOURCE_NAME>.services.ai.azure.com/api/projects/<PROJECT_NAME>"
export FOUNDRY_MODEL_DEPLOYMENT_NAME="<DEPLOYMENT_NAME>"

Summary

Tell the user:

The Foundry resource, project, and model deployment are ready
The project endpoint URL (they will need it for the other skills)
The model deployment name
The Python SDK packages are installed
They can now use the Foundry Code Interpreter and Foundry Agent Chat skills

Security

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Source

SKILL.md in the repositoryCheckingplugins/foundry-agents/skills/setup-foundry/SKILL.md

Popularity

Repository stars: 50
Repository forks: 8
7-day trend score: 20

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 20 Mar 2026, 7 months ago.

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