[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69AX0AGXSA8C3GKHA78QWWH":3,"skill-same:01M69AX0AGXSA8C3GKHA78QWWH":124},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":59,"ownerHtmlUrl":60,"ownerBlogUrl":61,"ownerDescription":62,"updatedAt":63,"install":64,"resources":77},"01M69AX0AGXSA8C3GKHA78QWWH","airunway-aks-setup",null,"Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: \"setup AI Runway\", \"onboard AKS cluster\", \"install AI Runway\", \"airunway setup\", \"deploy model to AKS\", \"GPU inference on AKS\", \"KAITO setup on AKS\", \"run LLM on AKS\", \"vLLM on AKS\", \"set up model serving on AKS\", \"AI Runway controller\".","microsoft\u002Fskills","microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","01M698VYDXZ6JW5WH3CRV650R8","Microsoft","01M69AJYYW4E1RMSYCQDJ4FJSH",3199,"MIT","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fplugins\u002Fazure-skills\u002Fskills\u002Fairunway-aks-setup\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,9],"airunway aks setup","setup","aks","vLLM","running","when","bare","cluster","model serving","provider","up","model","onboard","SET","controller","inference","gpu","verification","deployment","assessment","deploy model","deploy","2026-11-10 10:12:30",false,"---\nname: airunway-aks-setup\ndescription: \"Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: \\\"setup AI Runway\\\", \\\"onboard AKS cluster\\\", \\\"install AI Runway\\\", \\\"airunway setup\\\", \\\"deploy model to AKS\\\", \\\"GPU inference on AKS\\\", \\\"KAITO setup on AKS\\\", \\\"run LLM on AKS\\\", \\\"vLLM on AKS\\\", \\\"set up model serving on AKS\\\", \\\"AI Runway controller\\\".\"\nlicense: MIT\nmetadata:\n  author: Microsoft\n  version: \"1.1.2\"\nargument-hint: \"[skip-to-step N]\"\n---\n\n# AI Runway AKS Setup\n\nThis skill walks users from a bare Kubernetes cluster to a running AI model deployment. Follow each step in sequence unless the user provides `skip-to-step N` to resume from a specific phase.\n\n> **Cost awareness:** GPU node pools incur significant compute charges (A100-80GB can cost $3–5+\u002Fhr). Confirm the user understands cost implications before provisioning GPU resources.\n\n## Prerequisites\n\nThis skill assumes an AKS cluster already exists. If the user does not have a cluster, hand off to the `azure-kubernetes` skill first to provision one (with a GPU node pool unless CPU-only inference is acceptable), then return here.\n\n## Quick Reference\n\n| Property | Value |\n|----------|-------|\n| Best for | End-to-end AI Runway onboarding on AKS |\n| CLI tools | `kubectl`, `make`, `curl` |\n| MCP tools | None |\n| Related skills | `azure-kubernetes` (cluster setup), `azure-diagnostics` (baseline troubleshooting), optional `aks-gpu-inference` (existing GPU\u002Finference Day-2 incidents only) |\n\n## When to Use This Skill\n\nUse this skill when the user wants to:\n- Set up AI Runway on an existing AKS cluster from scratch\n- Install the AI Runway controller and CRDs\n- Assess GPU hardware compatibility for model deployment\n- Choose and install an inference provider (KAITO, Dynamo, KubeRay)\n- Deploy their first AI model to AKS via AI Runway\n- Resume a partially-complete AI Runway setup from a specific step\n\n## GPU setup versus Day-2 incidents\n\nKeep initial GPU node-pool, provider, controller, and model setup in this skill.\nFor an incident affecting an existing GPU or inference deployment, load\n[optional-gpu-day2.md](references\u002Foptional-gpu-day2.md) before deciding whether\nto offer the focused optional add-on.\n\n## MCP Tools\n\nThis skill uses no MCP tools. All cluster operations are performed directly via `kubectl` and `make`.\n\n## Rules\n\n1. Execute steps in sequence — load the reference for each step as you reach it\n2. Report cluster state at each step: ✓ healthy, ✗ missing\u002Ffailed\n3. Ask for user confirmation before any install or deployment action\n4. If a step is already complete, report status and skip to the next step\n5. If the user provides `skip-to-step N`, start at step N; assume prior steps are complete\n\n## Steps\n\n| # | Step | Reference |\n|---|------|-----------|\n| 1 | **Cluster Verification** — context check, node inventory, GPU detection | [step-1-verify.md](references\u002Fsteps\u002Fstep-1-verify.md) |\n| 2 | **Controller Installation** — CRD + controller deployment | [step-2-controller.md](references\u002Fsteps\u002Fstep-2-controller.md) |\n| 3 | **GPU Assessment** — detect GPU models, flag dtype\u002Fattention constraints | [step-3-gpu.md](references\u002Fsteps\u002Fstep-3-gpu.md) |\n| 4 | **Provider Setup** — recommend and install inference provider | [step-4-provider.md](references\u002Fsteps\u002Fstep-4-provider.md) |\n| 5 | **First Deployment** — pick a model, deploy, verify Ready | [step-5-deploy.md](references\u002Fsteps\u002Fstep-5-deploy.md) |\n| 6 | **Summary** — recap, smoke test, next steps | [step-6-summary.md](references\u002Fsteps\u002Fstep-6-summary.md) |\n\n## Error Handling\n\n| Error \u002F Symptom | Likely Cause | Remediation |\n|-----------------|--------------|-------------|\n| No kubeconfig context | Not connected to a cluster | Run `az aks get-credentials` or equivalent |\n| Controller in CrashLoopBackOff | Config or RBAC issue | `kubectl logs -n airunway-system -l control-plane=controller-manager --previous` |\n| Provider not ready | Image pull or RBAC issue | `kubectl logs \u003Cpod-name> -n \u003Cnamespace>` for the provider pod |\n| ModelDeployment stuck in Pending | GPU scheduling failure or provider not ready | `kubectl describe modeldeployment \u003Cname> -n \u003Cnamespace>` events |\n| `bfloat16` errors at inference | T4 or V100 lacks bfloat16 support | Add `--dtype float16` to serving args |\n\nFor full error handling and rollback procedures, see [troubleshooting.md](references\u002Ftroubleshooting.md).\n","# AI Runway AKS Setup\n\nThis skill walks users from a bare Kubernetes cluster to a running AI model deployment. Follow each step in sequence unless the user provides `skip-to-step N` to resume from a specific phase.\n\n> **Cost awareness:** GPU node pools incur significant compute charges (A100-80GB can cost $3–5+\u002Fhr). Confirm the user understands cost implications before provisioning GPU resources.\n\n## Prerequisites\n\nThis skill assumes an AKS cluster already exists. If the user does not have a cluster, hand off to the `azure-kubernetes` skill first to provision one (with a GPU node pool unless CPU-only inference is acceptable), then return here.\n\n## Quick Reference\n\n| Property | Value |\n|----------|-------|\n| Best for | End-to-end AI Runway onboarding on AKS |\n| CLI tools | `kubectl`, `make`, `curl` |\n| MCP tools | None |\n| Related skills | `azure-kubernetes` (cluster setup), `azure-diagnostics` (baseline troubleshooting), optional `aks-gpu-inference` (existing GPU\u002Finference Day-2 incidents only) |\n\n## When to Use This Skill\n\nUse this skill when the user wants to:\n- Set up AI Runway on an existing AKS cluster from scratch\n- Install the AI Runway controller and CRDs\n- Assess GPU hardware compatibility for model deployment\n- Choose and install an inference provider (KAITO, Dynamo, KubeRay)\n- Deploy their first AI model to AKS via AI Runway\n- Resume a partially-complete AI Runway setup from a specific step\n\n## GPU setup versus Day-2 incidents\n\nKeep initial GPU node-pool, provider, controller, and model setup in this skill.\nFor an incident affecting an existing GPU or inference deployment, load\n[optional-gpu-day2.md](references\u002Foptional-gpu-day2.md) before deciding whether\nto offer the focused optional add-on.\n\n## MCP Tools\n\nThis skill uses no MCP tools. All cluster operations are performed directly via `kubectl` and `make`.\n\n## Rules\n\n1. Execute steps in sequence — load the reference for each step as you reach it\n2. Report cluster state at each step: ✓ healthy, ✗ missing\u002Ffailed\n3. Ask for user confirmation before any install or deployment action\n4. If a step is already complete, report status and skip to the next step\n5. If the user provides `skip-to-step N`, start at step N; assume prior steps are complete\n\n## Steps\n\n| # | Step | Reference |\n|---|------|-----------|\n| 1 | **Cluster Verification** — context check, node inventory, GPU detection | [step-1-verify.md](references\u002Fsteps\u002Fstep-1-verify.md) |\n| 2 | **Controller Installation** — CRD + controller deployment | [step-2-controller.md](references\u002Fsteps\u002Fstep-2-controller.md) |\n| 3 | **GPU Assessment** — detect GPU models, flag dtype\u002Fattention constraints | [step-3-gpu.md](references\u002Fsteps\u002Fstep-3-gpu.md) |\n| 4 | **Provider Setup** — recommend and install inference provider | [step-4-provider.md](references\u002Fsteps\u002Fstep-4-provider.md) |\n| 5 | **First Deployment** — pick a model, deploy, verify Ready | [step-5-deploy.md](references\u002Fsteps\u002Fstep-5-deploy.md) |\n| 6 | **Summary** — recap, smoke test, next steps | [step-6-summary.md](references\u002Fsteps\u002Fstep-6-summary.md) |\n\n## Error Handling\n\n| Error \u002F Symptom | Likely Cause | Remediation |\n|-----------------|--------------|-------------|\n| No kubeconfig context | Not connected to a cluster | Run `az aks get-credentials` or equivalent |\n| Controller in CrashLoopBackOff | Config or RBAC issue | `kubectl logs -n airunway-system -l control-plane=controller-manager --previous` |\n| Provider not ready | Image pull or RBAC issue | `kubectl logs \u003Cpod-name> -n \u003Cnamespace>` for the provider pod |\n| ModelDeployment stuck in Pending | GPU scheduling failure or provider not ready | `kubectl describe modeldeployment \u003Cname> -n \u003Cnamespace>` events |\n| `bfloat16` errors at inference | T4 or V100 lacks bfloat16 support | Add `--dtype float16` to serving args |\n\nFor full error handling and rollback procedures, see [troubleshooting.md](references\u002Ftroubleshooting.md).\n",{"name":5,"license":15,"metadata":51,"description":7,"argument-hint":53},{"author":12,"version":52},"1.1.2","[skip-to-step 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Use when creating new skills or updating existing skills.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fskill-creator\u002FSKILL.md",[131],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[133,134,135,9,136,137,138,139,140,141,142,143,144,145,146,147,148,29,149],"skill creator","creator","coding agents","foundry","microsoft foundry","ai coding agents","azure","services","coding","guide","use","ai coding","with","sdks","for","Existing","new",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":166,"publishedAt":46},"01M69B4FD57V1MYKRWAJJECTZ7","podcast-generation","Generate AI-powered podcast-style audio narratives using Azure OpenAI's GPT Realtime Mini model via WebSocket. Use when building text-to-speech features, audio narrative generation, podcast creation from content, or integrating with Azure OpenAI Realtime API for real audio output. Covers full-stack implementation from React frontend to Python FastAPI backend with WebSocket streaming.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fpodcast-generation\u002FSKILL.md",[156,159,162,165],{"categoryNo":157,"categoryName":158,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":160,"categoryName":161,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":163,"categoryName":164,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[167,168,169,170,171,172,173,139,174,175,176,177,178,179,180,35,181,182,183,184,185,29,186],"podcast generation","podcast","generation","frontend","python","react","realtime","azure openai","generate","streaming","api","Audio","stack","text","fastapi","creation","python fastapi","narrative","features","fastapi backend",{"skillNo":188,"name":189,"nameZh":6,"description":190,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":191,"categories":192,"keywords":194,"publishedAt":46},"01M69B4CYPM5SESY5ZWEFDXEQZ","microsoft-docs","Understand Microsoft technologies by querying official documentation. Use whenever the user asks how something works, wants tutorials, needs configuration options, limits, quotas, or best practices for any Microsoft technology (Azure, .NET, M365, Windows, Power Platform, etc.)—even if they don't mention \"docs.\" If the question is about understanding a concept rather than writing code, this is the right skill.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fmicrosoft-docs\u002FSKILL.md",[193],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[195,9,196,197,198,199,200,201,202,203,204,139,205,206,207,143,208,67,209,210,211,212,213,147],"docs","microsoft docs","MCP","writing","cli","mcp server","windows","power platform","official","documentation","learn","best practices","question","power","back","technology","platform","learn mcp","concept",{"skillNo":215,"name":216,"nameZh":6,"description":217,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":218,"categories":219,"keywords":221,"publishedAt":46},"01M69B49C3YPD1ZJ3NC0HPCY58","mcp-builder","Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP), Node\u002FTypeScript (MCP SDK), or C#\u002F.NET (Microsoft MCP SDK).","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fmcp-builder\u002FSKILL.md",[220],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[197,222,223,224,171,225,226,227,9,228,229,35,230,231,142,143,232,233,234,145,147,29],"mcp builder","builder","typescript","llms","sdk","model context protocol","fastmcp","Tools","node","context","integrate","mcp sdk","building mcp servers",{"skillNo":236,"name":237,"nameZh":6,"description":238,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":239,"categories":240,"keywords":253,"publishedAt":46},"01M69B467A7CDMFQ0T6Q0ZA12Q","kql","KQL language expertise for writing correct, efficient Kusto Query Language queries. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph). USE THIS SKILL whenever writing, debugging, or reviewing KQL queries — even simple ones — because the gotchas section prevents the most common errors that waste tool calls and cause expensive retry cascades. Trigger on: KQL, Kusto, ADX, Azure Data Explorer, Fabric Real-Time Intelligence, EventHouse, Log Analytics, log analysis, data exploration, time series, anomaly detection, summarize, where clause, join, extend, project, let statement, parse operator, extract function, any mention of pipe-forward query syntax.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fkql\u002FSKILL.md",[241,244,245,250],{"categoryNo":242,"categoryName":243,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":246,"categoryName":247,"taxonomyType":248,"taxonomyTypeDesc":249},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",{"categoryNo":251,"categoryName":252,"taxonomyType":248,"taxonomyTypeDesc":249},"01JBSEEDCAT000000000000036","内容创作者",[237,198,254,255,256,139,257,258,259,260,261,262,263,264,265,266,267,268,269,147,270,271,272],"summarize","memory","analytics","vector","time series","tool","discipline","log","geo","exploration","pipe","time","project","regex","join","datetime","section","anomaly","parse",{"skillNo":274,"name":275,"nameZh":6,"description":276,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":277,"categories":278,"keywords":279,"publishedAt":46},"01M69B44HJQ7897EZ7NE7DWQX3","github-issue-creator","Convert raw notes, error logs, voice dictation, or screenshots into crisp GitHub-flavored markdown issue reports. Use when the user pastes bug info, error messages, or informal descriptions and wants a structured GitHub issue. Supports images\u002FGIFs for visual evidence.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fgithub-issue-creator\u002FSKILL.md",[],[280,281,282,283,134,284,285,286,287,288,289,290,143,291,292,293,294,295,296,147,29],"github","github issue creator","github issue","issue","issue creator","screenshots","notes","logs","error messages","messages","convert","evidence","reports","images","info","visual","error"]