Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work. Evaluates build health, app completeness, dependencies and local services, stack compatibility, and deployment feasibility. Answers questions about what your app needs before it can be deployed — frameworks, dependencies, and configuration. Checks whether dependencies are compatible and identifies deployment blockers and unsupported frameworks. WHEN: \"evaluate my repo\", \"is my app ready to deploy\", \"what does my app need to deploy\", \"what do I need before deploying\", \"does my app need\", \"can I ship this to Azure\", \"scan my repo for issues\", \"is this app deployable\", \"check if my app is ready for Azure\", \"do I need a Dockerfile\", \"what's blocking my deployment\", \"are there any blockers\", \"are my dependencies compatible\", \"does Azure support my framework\", \"what needs to change before deploying\", \"check my app configuration\".
Performs comprehensive preflight validation of Bicep deployments to Azure, including template syntax validation, what-if analysis, and permission checks. Use this skill before any deployment to Azure to preview changes, identify potential issues, and ensure the deployment will succeed. Activate when users mention deploying to Azure, validating Bicep files, checking deployment permissions, previewing infrastructure changes, running what-if, or preparing for azd provision.
Use this skill to deploy and operate a CollectX (clx) based DOCA telemetry collector on a host or BlueField — wiring providers / counters into the collector, running the collection daemon, and shaping its exporters (Prometheus pull, Fluent Bit push, NetFlow, file / IPC) so the metrics actually leave the box. Trigger even when the user never says CollectX or clx — implicit phrasings: {collector emits nothing downstream}, {add a provider to the clx collector}, {turn on the Prometheus endpoint}, {ship counters to Fluent Bit from the DPU}, {daemon starts but no schema rows appear}. This skill owns the CollectX collection mechanism plus the operator's own doca-telemetry / doca-telemetry-exporter usage; it ROUTES the productized DOCA Telemetry Service (DTS) to public docs (AGENTS.md Non-goal #7), the reader API to doca-telemetry, and the publisher API to doca-telemetry-exporter. Refuse to invent clx symbols, provider names, schema fields, flags, or config paths — describe the class and route to the live source.
Use when deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.
[Skill] deployment context, aks, azure, koji - Resolve Koji AKS deployment context -- resource group, cluster name, subscription, Log Analytics workspace, and monitoring resource names from deployment_summary.yaml when available, or discover them dynamically from Azure.
This skill should be used when the user wants to "deploy an agent", "deploy my ADK agent", "set up CI/CD", "configure secrets", "troubleshoot a deployment", or needs guidance on Agent Runtime, Cloud Run, or GKE deployment targets, or binding an agent to an Agent Gateway. Covers deployment workflows, service accounts, rollback, and production infrastructure. Applies to any framework agents-cli deploys (ADK, LangChain, ...). Part of the agents-cli skills suite. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), evaluation (use google-agents-cli-eval), or project scaffolding (use google-agents-cli-scaffold).
Use this skill when the user is hands-on deploying an in-bundle DOCA service container (Argus, DMS, Firefly, or UROM service) on a BlueField — kubelet standalone watching a static-pod manifests directory, YAML pod-spec drop, kubelet status / ENTRYPOINT logs / per-service liveness, smoke-before-bulk, and the layered error taxonomy (pod-spec, scheduling, image pull, runtime, mount, network, version, host). Trigger even when the user does not say "container deployment" — typical implicit phrasings include "how do I run my built service on the BlueField?", "where do I drop the pod-spec YAML?", "pod stuck in Pending / ImagePullBackOff / CrashLoopBackOff", "container Running but service isn't ready", "pod restart-loops after edit", or "DMS and Firefly together". Refuse and route elsewhere for per-service config schemas, DOCA install, library-API questions, external NVIDIA services (BlueMan, HBN, SNAP, Virtio-net), or full Kubernetes-cluster ops — those belong to other skills.
Deploy a Microsoft Agent Framework (MAF) workflow as a managed online endpoint to an Azure ML workspace or an Azure AI Foundry hub-based project. Wraps any workflow into an init()/run() scoring script, creates conda environment, endpoint and deployment YAMLs, deploy script, and assigns RBAC. Supports managed identity auth and Application Insights tracing. WHEN: deploy MAF workflow, deploy agent-framework workflow, create online endpoint for MAF, deploy workflow to AML, deploy workflow to Foundry project, managed online endpoint for agent workflow, wrap workflow in scoring script, deploy agent as endpoint, realtime endpoint in Foundry project.
Deploy web projects to Netlify using the Netlify CLI (`npx netlify`). Use when the user asks to deploy, host, publish, or link a site/repo on Netlify, including preview and production deploys.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Validate a commit-specific Next.js preview package and manually trigger the entire Next.js deployment test suite through the test_e2e_deploy_release.yml GitHub Actions workflow. Use only when asked to run the full deploy test suite or this workflow specifically from an internal vercel/next.js PR branch. Do not use for focused deployment-test sanity checks; run the relevant tests locally with pnpm test-deploy instead. Covers resolving the latest branch SHA, waiting for vercel-packages, preserving default workflow inputs, dispatching the workflow, and verifying the run.
Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.
Take a working agent prototype to a scalable, observable production deployment on Microsoft Foundry. Covers deployment patterns (client-hosted, hosted agents, agent workflows), the agent lifecycle, model routing, response caching, evaluation gates, human-in-the-loop approval, observability with OpenTelemetry, cost optimisation, and smoke-testing deployed agents with the AI Smoke Test action. Based on Lesson 16 of AI Agents for Beginners. USE FOR: deploy an agent to production, scale an agent, Microsoft Foundry hosted agent, Foundry Agent Service, model routing, response caching, evaluation gate, release gate, human approval workflow, agent observability, agent tracing, agent cost optimisation, smoke test a hosted agent, production customer support agent. DO NOT USE FOR: building your first agent (start with Lesson 01), running agents locally on-device (use local-ai-agents / Lesson 17), Azure infrastructure provisioning unrelated to agents, non-Foundry deployment targets.
name: deploying-scalable-agents
license: MIT
---
# نشر عوامل قابلة للتوسع باستخدام Microsoft Foundry
> مهارة مرافق لدروس [الدرس 16 – نشر عوامل قابلة للتوسع](../../../16-deploying-scalable-agents/README.md).
> استخدمها لمساعدة المتعلم على نقل وكيل من
name: deploying-scalable-agents
license: MIT
---
# Разгръщане на мащабируеми агенти с Microsoft Foundry
> Придружаващо умение за [Урок 16 – Разгръщане на мащабируеми агенти](../../../16-deploying-scalable-agents/README.md).
> Използвайте го, за да помогнете на учащия
name: deploying-scalable-agents
license: MIT
---
# মাইক্রোসফট ফাউন্ড্রির সাথে স্কেলযোগ্য এজেন্ট মোতায়েন
> [Lesson 16 – Deploying Scalable Agents](../../../16-deploying-scalable-agents/README.md) এর সঙ্গী দক্ষতা।
> এটি ব্যবহার করে একজন শেখার্থীকে সাহায্য করুন একটি এজেন্টকে
name: deploying-scalable-agents
license: MIT
---
# Nasazení škálovatelných agentů s Microsoft Foundry
> Doplňková dovednost k [Lekci 16 – Nasazení škálovatelných agentů](../../../16-deploying-scalable-agents/README.md).
> Použijte ji k pomoci studentovi přesunout agenta z prototypu
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