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azure-ai-deploy

Ship a Python generative-AI app to Azure the keyless way, using DefaultAzureCredential and azd. Use when deploying a Python GenAI service, wiring an Azure OpenAI client without API keys, provisioning managed identity for Azure compute, running a pre-deploy gate, or building an azure.yaml for a container app. Triggers on "deploy to Azure", "keyless Azure OpenAI", "DefaultAzureCredential", "azd provision", "managed identity", "no API keys in code".

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timothywarner-org/claude-code
Letzte Quellaktivität
20. Juli 2026 um 13:26
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Englisch
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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
azure-ai-deploy
description
Ship a Python generative-AI app to Azure the keyless way, using DefaultAzureCredential and azd. Use when deploying a Python GenAI service, wiring an Azure OpenAI client without API keys, provisioning managed identity for Azure compute, running a pre-deploy gate, or building an azure.yaml for a container app. Triggers on "deploy to Azure", "keyless Azure OpenAI", "DefaultAzureCredential", "azd provision", "managed identity", "no API keys in code".
allowed-tools
Read, Glob, Grep, Bash, Edit, Write
# Ship a Python GenAI app to Azure (keyless) This skill deploys a Python generative-AI service to Azure with **no API keys in source**. Authentication is via **Microsoft Entra ID token** (`DefaultAzureCredential`) locally and **managed identity** on Azure compute. Deployment runs through **azd** (Azure Developer CLI), which provisions and deploys in one workflow. ## When to reach for this - You have a Python GenAI app (chat, RAG, agent) that calls **Azure OpenAI** and you want it in Azure. - You want the **keyless** pattern so no secret is stored in code, config, or environment. - You need a repeatable **pre-deploy gate** before a live class or a production push. ## Workflow ### 1. Read the auth pattern first Read `resources/references/AZURE-AUTH.md`. It covers why keys stay out of code, how `DefaultAzureCredential` resolves an identity across local and cloud, and which RBAC role the app identity needs on the Azure OpenAI resource. ### 2. Scaffold the client and config Copy the templates into the target project and edit for the real service: - `resources/templates/chat_client.py` - keyless Azure OpenAI client. Reads endpoint and deployment from env vars. No key. - `resources/templates/azure.yaml` - minimal **azd** config for a Python container app. ### 3. Set required environment The app reads two values from the process environment, never from hardcoded strings: - `AZURE_OPENAI_ENDPOINT` - the resource endpoint, for example `https://contoso-aoai.openai.azure.com/` - `AZURE_OPENAI_DEPLOYMENT` - the model deployment name, for example `gpt-4o-chat` ### 4. Run preflight ```bash uv run python ${CLAUDE_SKILL_DIR}/resources/scripts/preflight.py ``` The script verifies both env vars are set and non-empty. It exits non-zero when a value is missing, so it fails a CI step before any provisioning starts. ### 5. Work the deploy checklist Read `resources/references/DEPLOY-CHECKLIST.md` and clear every gate: evals passed, secrets stored in **Azure Key Vault**, then `azd provision` followed by `azd deploy`. ## Conventions - **uv** manages Python, not pip. Run scripts with `uv run`. - **No key strings.** The client uses `azure_ad_token_provider`, never `api_key`. - **Least privilege.** Grant the app identity the **Cognitive Services OpenAI User** role, nothing broader. - **Azure only.** This skill targets Azure services end to end.
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