azure-deploy

Mechanism

When It Triggers · How It Works · What It Produces

Breaks down how the skill works and what it produces, so you can quickly judge whether it fits your scenario and value.

Trigger

Triggered when users request "execute deployment", "push to production", "go live", "run azd up".
Only for already-prepared apps (existing deployment plan and validated status).

Mechanism

First validates prerequisites: .azure/deployment-plan.md exists with Validated status.
Auto-detects deployment tool: azd (azure.yaml present) → azd up/azd deploy; Terraform → terraform apply; Bicep → az deployment group create.
Executes deployment with streaming output and built-in error recovery.
Verifies app accessibility after successful deployment.

Outcome

Deployed app running on Azure with deployment URL, status info, and verification results.

Use Cases

Two Typical Use Cases

Scenario 01

Automated Deployment Execution and Error Recovery

After preparation and validation, dev teams need to execute deployment to production. The skill reads config from the deployment plan, auto-detects deployment tool (azd, terraform, az deployment), and executes corresponding commands. Built-in error recovery analyzes failures and suggests fixes. Supports both incremental and full deployment modes.

Scenario 02

Production Go-Live and Rollback Management

Teams are ready to publish their app to production. The skill confirms deployment target (subscription, resource group, environment), executes deployment commands with streaming output. If deployment fails, analyzes errors and suggests fixes. Verifies app accessibility after success. Strict safety: doesn't create new apps directly (handled by azure-prepare).

Skill Relationships

Which Skills I Use · Which Skills Use Me

Dependency relationships read as "the upper tier points to the lower tier." The current Skill sits in the middle tier — above are Skills that depend on it, below are Skills it depends on.

Tier 1 · These Skills Use Me

Tier 2 · Current Skill

azure-deployWorkflow

Tier 3 · I Use These Skills

Full Relationship Graph

Browse this skill's relationships within its skillset. Click a node to switch the side panel; use the search box to jump to any skill.

Skill File

SKILL.md

Open in GitHub ↗

SKILL.mdLoading from GitHub…

Skill Composition

Open in GitHub ↗

SKILL.md7.7%
references92.3%

▸skills/azure-deploy/
▸azure-deploy
- ·SKILL.md
- ▸references
  - ·auth-best-practices.md
  - ·global-rules.md
  - ·live-role-verification.md
  - ·pre-deploy-checklist.md
  - ▸recipes
    - ·README.md
    - ▸azcli
    - ▸azd
    - ▸bicep
    - ▸cicd
    - ▸terraform
  - ·region-availability.md
  - ▸sdk
    - ·azd-deployment.md
    - ·azure-identity-dotnet.md
    - ·azure-identity-java.md
    - ·azure-identity-py.md
    - ·azure-identity-ts.md
  - ·troubleshooting.md

Per the Agent Skills spec, SKILL.md is the only required file. The colored bars classify the rest into the three spec-aligned optional directories (scripts / references / assets) and an 'other' bucket. The full directory tree is below.

Related in Skillset

You May Also Need These Skills

airunway-aks-setup

Sets up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first model deployment. Helps users quickly deploy AI inference environments on AKS clusters, supporting KAITO, vLLM, and other model serving frameworks.

Workflow

appinsights-instrumentation

Guidance for instrumenting web apps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. Helps developers add Application Insights telemetry instrumentation to their apps for APM. Covers custom telemetry, distributed tracing, log collection, and diagnostic analysis configuration.

CLI Manual

azure-ai

Skill for using Azure AI services: Search, Speech, OpenAI, Document Intelligence. Covers vector/hybrid/semantic search, speech-to-text, text-to-speech, transcription, and OCR document recognition. Provides MCP tools and SDK references to help developers integrate Azure AI capabilities into their applications.

CLI Manual

azure-aigateway

Configures Azure API Management as an AI Gateway for AI models, MCP tools, and agents. Covers semantic caching, token limits, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, and more. Supports LLM policy configuration, cost control, and API-to-MCP conversion.

Workflow

/azure-deploy

When It Triggers · How It Works · What It Produces

Trigger

Mechanism

Outcome

Two Typical Use Cases

Automated Deployment Execution and Error Recovery

Production Go-Live and Rollback Management

Which Skills I Use · Which Skills Use Me

Full Relationship Graph

SKILL.md

Skill Composition

You May Also Need These Skills

airunway-aks-setup

appinsights-instrumentation

azure-ai

azure-aigateway

When It Triggers · How It Works · What It Produces

Trigger

Mechanism

Outcome

Two Typical Use Cases

Automated Deployment Execution and Error Recovery

Production Go-Live and Rollback Management

Which Skills I Use · Which Skills Use Me

Full Relationship Graph

SKILL.md

Skill Composition

You May Also Need These Skills

airunway-aks-setup

appinsights-instrumentation

azure-ai

azure-aigateway