[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6992S3G6YRFRG97SABMXHSP":3,"skill-same:01M6992S3G6YRFRG97SABMXHSP":76},{"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":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":47,"commitSha":48,"skillDirectory":49,"forksCount":50,"repositoryHtmlUrl":51,"repositoryHomepageUrl":6,"ownerHtmlUrl":52,"ownerBlogUrl":53,"ownerDescription":54,"updatedAt":55,"install":56,"resources":69},"01M6992S3G6YRFRG97SABMXHSP","gke-ai-troubleshooting-handle-disruption-gpu-tpu",null,"Diagnoses, predicts, and mitigates node disruptions during Compute Engine host maintenance and hardware or software maintenance events for GPU and TPU workloads on GKE. Use when diagnosing node disruptions, predicting host maintenance events on GPU\u002FTPU nodepools, inspecting node interruption PromQL metrics, auditing node taints, or configuring workload protection strategies (graceful termination, opportunistic maintenance, PodDisruptionBudgets). Don't use for general GKE cluster creation, network policy configuration, or non-disruption workload deployment.","google\u002Fskills","google","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F1342004?v=4","01M698VYEFDM0VJBZDYYJ2T83S","Google","01M698WR0H76ENHE587T9EJT89",24122,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-ai-troubleshooting-handle-disruption-gpu-tpu\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"gke ai troubleshooting handle disruption gpu tpu","tpu","gpu","gke","troubleshooting","when","policy","promql","for","cluster","host","creation","strategies","compute","general","maintenance","use","engine","deployment","gke cluster creation","2026-11-10 10:12:30",false,"---\nname: gke-ai-troubleshooting-handle-disruption-gpu-tpu\nmetadata:\n  version: \"1.0.1\"\n  category: CloudObservabilityAndMonitoring\ndescription: >-\n  Diagnoses, predicts, and mitigates node disruptions during Compute Engine host maintenance and hardware or software maintenance events for GPU and TPU workloads on GKE. Use when diagnosing node disruptions, predicting host maintenance events on GPU\u002FTPU nodepools, inspecting node interruption PromQL metrics, auditing node taints, or configuring workload protection strategies (graceful termination, opportunistic maintenance, PodDisruptionBudgets). Don't use for general GKE cluster creation, network policy configuration, or non-disruption workload deployment.\n---\n\n# Handle Disruption on GPUs and TPUs Troubleshooting\n\n## Attribution\n\n**MUST READ**: Before executing any `gcloud`, REST API, or MCP command, you\nMUST read [Usage Attribution](references\u002Fattribution.md). For commands executed\ndirectly in the terminal (never in generated scripts, Terraform, or user code),\nprefix `gcloud` with\n`CLOUDSDK_METRICS_REQUEST_ATTRIBUTION=\"agent-skills\u002F1.0 (skill:gke-ai-troubleshooting-handle-disruption-gpu-tpu)\"`\nand append `agent-skills\u002F1.0 (skill:gke-ai-troubleshooting-handle-disruption-gpu-tpu)` to `User-Agent` headers.\n\n## ? Diagnostic Workflow\n\n### Step 0: Context Acquisition\n\n-   **Mandatory**: When a user asks to debug or investigate an actual workload\n    disruption, node crash, or unexpected restart without providing complete\n    cluster details, you MUST immediately halt and request all missing mandatory\n    parameters (`project_id`, `location`, `cluster_name`, `timestamp`) BEFORE\n    delivering theories or general diagnostic commands. Only skip context\n    acquisition if the user explicitly requests a generic reusable runbook or\n    provides a complete static telemetry\u002Flog dump for offline analysis.\n-   **Optional**: `node_name`, `workload_name`, `workload_namespace`,\n    `nodepool_name`.\n\n### Step 1: [Low Risk] Check for Upcoming Scheduled Maintenance\n\n-   **Action**: Propose running `kubectl` to check if nodes have the scheduled\n    maintenance label indicating an upcoming disruption.\n-   **Example Command**:\n\n    ```bash\n    kubectl get nodes -l cloud.google.com\u002Fscheduled-maintenance-time -L cloud.google.com\u002Fscheduled-maintenance-time\n    ```\n\n-   **Interpretation**: The `SCHEDULED-MAINTENANCE-TIME` column shows the Unix\n    epoch time when the VM is scheduled for maintenance. If this label exists, a\n    disruption is guaranteed to occur.\n\n### Step 2: [Low Risk] Investigation via Cloud Monitoring (PromQL)\n\n-   **Action**: Call any available monitoring tool or provide PromQL for manual\n    verification.\n-   **Mandatory Monitoring Rule**: Whenever recommending follow-up monitoring or\n    interruption tracking over time, you MUST explicitly present a **PromQL**\n    query using the metric `kubernetes_io:node_interruption_count` filtered by\n    `interruption_reason=\"HW\u002FSW Maintenance\"`. Do not suggest general Cloud\n    Monitoring dashboards or Metrics Explorer without providing this specific\n    PromQL metric expression.\n-   **Example Query**:\n\n    ```promql\n    # Fetch host maintenance events for nodes\n    sum by (interruption_type,interruption_reason)( sum_over_time( kubernetes_io:node_interruption_count{monitored_resource=\"k8s_node\", interruption_reason=\"HW\u002FSW Maintenance\"}[${__interval}]))\n    ```\n\n    ```promql\n    # See the interruption count aggregated by node pool\n    sum by (node_pool_name,interruption_type,interruption_reason)( sum_over_time( kubernetes_io:node_pool_interruption_count{monitored_resource=\"k8s_node_pool\", interruption_reason=\"HW\u002FSW Maintenance\", node_pool_name=\"{nodepool_name}\" }[${__interval}]))\n    ```\n\n-   **Interpretation**: If `kubernetes_io:node_interruption_count` shows\n    values > 0 for `interruption_reason=\"HW\u002FSW Maintenance\"`, it indicates the\n    underlying Compute Engine VM was interrupted due to scheduled host\n    maintenance.\n\n### Step 3: [Low Risk] Investigation via Cloud Logging & Node Taints\n\n-   **Action**: Call `query_logs` or instruct the user to filter their GKE logs\n    for active host maintenance events, and check node taints.\n-   **Guidance**: Look for occurrences in Cloud Logging where\n    `cloud.google.com\u002Factive-node-maintenance` is set to `ONGOING`. To check if\n    GKE has cordoned the terminating node to prevent new workloads from being\n    scheduled, verify whether the\n    `cloud.google.com\u002Fimpending-node-termination:NoSchedule` taint is present\n    (either in GKE event logs or directly via `kubectl describe node`).\n-   **Interpretation**:\n    -   `cloud.google.com\u002Factive-node-maintenance` set to `ONGOING` means\n        workloads are actively being stopped by GKE due to host maintenance.\n    -   `cloud.google.com\u002Fimpending-node-termination:NoSchedule` taint means GKE\n        has cordoned the node to prevent new Pods from being scheduled on the\n        terminating node. DO NOT recommend tolerating this taint.\n\n### Step 4: Conclusion and Resolution\n\n-   **Action**: Provide a summary of findings to the user and suggest\n    appropriate mitigation strategies if host maintenance events were confirmed\n    or scheduled.\n-   **Reporting Rule**: Signal Only. Report high-signal information indicating\n    that the disruption was caused by Compute Engine host maintenance,\n    specifically affecting the underlying GPU\u002FTPU nodes. DO NOT dump raw logs.\n-   **Negative Findings Rule-Out**: If node scheduled-maintenance labels, PromQL\n    interruption counts, and active maintenance logs all return negative\u002Fempty\n    results, definitively conclude that Compute Engine host maintenance did NOT\n    cause the disruption. Direct the user to investigate application-level\n    causes (such as OOMKill events, CUDA runtime errors, or resource limits) and\n    do not propose host maintenance mitigations as the primary resolution.\n-   **Mandatory Workload Protection Triad**: Whenever host maintenance is\n    identified or anticipated on GPU\u002FTPU nodes, consistently recommend all three\n    complementary mitigations together:\n    1.  **Configure Graceful Termination**: For workloads that need time to save\n        state (e.g., ML frameworks checkpointing via Orbax), follow the guide to\n        [Enable disruption handling](https:\u002F\u002Fdocs.cloud.google.com\u002Fkubernetes-engine\u002Fdocs\u002Fconcepts\u002Fhandle-disruption-gpu-tpu#enabling-handling)\n        and set `spec.terminationGracePeriodSeconds` (up to 60 minutes) to\n        handle the `SIGTERM` signal before node shutdown.\n    2.  **Enable Opportunistic Maintenance**: To automatically trigger\n        maintenance when GKE detects that GPU\u002FTPU nodes are idle, configure\n        [Opportunistic Maintenance](https:\u002F\u002Fdocs.cloud.google.com\u002Fkubernetes-engine\u002Fdocs\u002Fconcepts\u002Fhandle-disruption-gpu-tpu#opportunistic-maintenance).\n    3.  **Configure PodDisruptionBudgets (PDBs)**: Ensure your workload uses a\n        `PodDisruptionBudget` to maintain `minAvailable` replicas during\n        evictions and disruptions.\n","# Handle Disruption on GPUs and TPUs Troubleshooting\n\n## Attribution\n\n**MUST READ**: Before executing any `gcloud`, REST API, or MCP command, you\nMUST read [Usage Attribution](references\u002Fattribution.md). For commands executed\ndirectly in the terminal (never in generated scripts, Terraform, or user code),\nprefix `gcloud` with\n`CLOUDSDK_METRICS_REQUEST_ATTRIBUTION=\"agent-skills\u002F1.0 (skill:gke-ai-troubleshooting-handle-disruption-gpu-tpu)\"`\nand append `agent-skills\u002F1.0 (skill:gke-ai-troubleshooting-handle-disruption-gpu-tpu)` to `User-Agent` headers.\n\n## ? Diagnostic Workflow\n\n### Step 0: Context Acquisition\n\n-   **Mandatory**: When a user asks to debug or investigate an actual workload\n    disruption, node crash, or unexpected restart without providing complete\n    cluster details, you MUST immediately halt and request all missing mandatory\n    parameters (`project_id`, `location`, `cluster_name`, `timestamp`) BEFORE\n    delivering theories or general diagnostic commands. Only skip context\n    acquisition if the user explicitly requests a generic reusable runbook or\n    provides a complete static telemetry\u002Flog dump for offline analysis.\n-   **Optional**: `node_name`, `workload_name`, `workload_namespace`,\n    `nodepool_name`.\n\n### Step 1: [Low Risk] Check for Upcoming Scheduled Maintenance\n\n-   **Action**: Propose running `kubectl` to check if nodes have the scheduled\n    maintenance label indicating an upcoming disruption.\n-   **Example Command**:\n\n    ```bash\n    kubectl get nodes -l cloud.google.com\u002Fscheduled-maintenance-time -L cloud.google.com\u002Fscheduled-maintenance-time\n    ```\n\n-   **Interpretation**: The `SCHEDULED-MAINTENANCE-TIME` column shows the Unix\n    epoch time when the VM is scheduled for maintenance. If this label exists, a\n    disruption is guaranteed to occur.\n\n### Step 2: [Low Risk] Investigation via Cloud Monitoring (PromQL)\n\n-   **Action**: Call any available monitoring tool or provide PromQL for manual\n    verification.\n-   **Mandatory Monitoring Rule**: Whenever recommending follow-up monitoring or\n    interruption tracking over time, you MUST explicitly present a **PromQL**\n    query using the metric `kubernetes_io:node_interruption_count` filtered by\n    `interruption_reason=\"HW\u002FSW Maintenance\"`. Do not suggest general Cloud\n    Monitoring dashboards or Metrics Explorer without providing this specific\n    PromQL metric expression.\n-   **Example Query**:\n\n    ```promql\n    # Fetch host maintenance events for nodes\n    sum by (interruption_type,interruption_reason)( sum_over_time( kubernetes_io:node_interruption_count{monitored_resource=\"k8s_node\", interruption_reason=\"HW\u002FSW Maintenance\"}[${__interval}]))\n    ```\n\n    ```promql\n    # See the interruption count aggregated by node pool\n    sum by (node_pool_name,interruption_type,interruption_reason)( sum_over_time( kubernetes_io:node_pool_interruption_count{monitored_resource=\"k8s_node_pool\", interruption_reason=\"HW\u002FSW Maintenance\", node_pool_name=\"{nodepool_name}\" }[${__interval}]))\n    ```\n\n-   **Interpretation**: If `kubernetes_io:node_interruption_count` shows\n    values > 0 for `interruption_reason=\"HW\u002FSW Maintenance\"`, it indicates the\n    underlying Compute Engine VM was interrupted due to scheduled host\n    maintenance.\n\n### Step 3: [Low Risk] Investigation via Cloud Logging & Node Taints\n\n-   **Action**: Call `query_logs` or instruct the user to filter their GKE logs\n    for active host maintenance events, and check node taints.\n-   **Guidance**: Look for occurrences in Cloud Logging where\n    `cloud.google.com\u002Factive-node-maintenance` is set to `ONGOING`. To check if\n    GKE has cordoned the terminating node to prevent new workloads from being\n    scheduled, verify whether the\n    `cloud.google.com\u002Fimpending-node-termination:NoSchedule` taint is present\n    (either in GKE event logs or directly via `kubectl describe node`).\n-   **Interpretation**:\n    -   `cloud.google.com\u002Factive-node-maintenance` set to `ONGOING` means\n        workloads are actively being stopped by GKE due to host maintenance.\n    -   `cloud.google.com\u002Fimpending-node-termination:NoSchedule` taint means GKE\n        has cordoned the node to prevent new Pods from being scheduled on the\n        terminating node. DO NOT recommend tolerating this taint.\n\n### Step 4: Conclusion and Resolution\n\n-   **Action**: Provide a summary of findings to the user and suggest\n    appropriate mitigation strategies if host maintenance events were confirmed\n    or scheduled.\n-   **Reporting Rule**: Signal Only. Report high-signal information indicating\n    that the disruption was caused by Compute Engine host maintenance,\n    specifically affecting the underlying GPU\u002FTPU nodes. DO NOT dump raw logs.\n-   **Negative Findings Rule-Out**: If node scheduled-maintenance labels, PromQL\n    interruption counts, and active maintenance logs all return negative\u002Fempty\n    results, definitively conclude that Compute Engine host maintenance did NOT\n    cause the disruption. Direct the user to investigate application-level\n    causes (such as OOMKill events, CUDA runtime errors, or resource limits) and\n    do not propose host maintenance mitigations as the primary resolution.\n-   **Mandatory Workload Protection Triad**: Whenever host maintenance is\n    identified or anticipated on GPU\u002FTPU nodes, consistently recommend all three\n    complementary mitigations together:\n    1.  **Configure Graceful Termination**: For workloads that need time to save\n        state (e.g., ML frameworks checkpointing via Orbax), follow the guide to\n        [Enable disruption handling](https:\u002F\u002Fdocs.cloud.google.com\u002Fkubernetes-engine\u002Fdocs\u002Fconcepts\u002Fhandle-disruption-gpu-tpu#enabling-handling)\n        and set `spec.terminationGracePeriodSeconds` (up to 60 minutes) to\n        handle the `SIGTERM` signal before node shutdown.\n    2.  **Enable Opportunistic Maintenance**: To automatically trigger\n        maintenance when GKE detects that GPU\u002FTPU nodes are idle, configure\n        [Opportunistic Maintenance](https:\u002F\u002Fdocs.cloud.google.com\u002Fkubernetes-engine\u002Fdocs\u002Fconcepts\u002Fhandle-disruption-gpu-tpu#opportunistic-maintenance).\n    3.  **Configure PodDisruptionBudgets (PDBs)**: Ensure your workload uses a\n        `PodDisruptionBudget` to maintain `minAvailable` replicas during\n        evictions and disruptions.\n",{"name":5,"metadata":44,"description":7},{"version":45,"category":46},"1.0.1","CloudObservabilityAndMonitoring","main","bb9b3f464aae37af360d079c1d863524955b0363","skills\u002Fcloud\u002Fgke-ai-troubleshooting-handle-disruption-gpu-tpu",2005,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills","https:\u002F\u002Fgithub.com\u002Fgoogle","https:\u002F\u002Fopensource.google\u002F","Google ❤️ Open Source","2026-11-14 01:51:35",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add google\u002Fskills --skill gke-ai-troubleshooting-handle-disruption-gpu-tpu",{"tool":62,"command":63},"bunx","bunx skills add google\u002Fskills --skill gke-ai-troubleshooting-handle-disruption-gpu-tpu",{"tool":65,"command":66},"pnpm","pnpm dlx skills add google\u002Fskills --skill gke-ai-troubleshooting-handle-disruption-gpu-tpu","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-ai-troubleshooting-handle-disruption-gpu-tpu\u002FSKILL.md",true,[70],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"references\u002Fattribution.md","REFERENCE","参考文档",1984,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-ai-troubleshooting-handle-disruption-gpu-tpu\u002Freferences\u002Fattribution.md",[77,114,145,174,201,225],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":91,"publishedAt":39},"01M6998S2EZKD3GEJ7F943RZTE","sign-in-with-google-web","Implement, configure, and secure Sign In With Google (SiwG) using Google Identity Services (GIS \u002F `https:\u002F\u002Faccounts.google.com\u002Fgsi\u002Fclient`) across web architectures. Use when creating Google sign-in buttons, implementing Google One Tap with FedCM, integrating GIS in React\u002FNext.js\u002FAngular\u002FHTML, verifying ID tokens on backend runtimes (Python\u002FNode.js\u002FGo\u002FJava), enforcing Google Workspace domain restrictions (`hd`), securing client-side ID tokens via WebCrypto nonces or encrypted IndexedDB, embedding in cross-origin iframes via the Intermediate Iframe API (`https:\u002F\u002Faccounts.google.com\u002Fgsi\u002Fintermediate`), configuring Content Security Policy (CSP), COOP, and Permissions Policy (`identity-credentials-get`) headers, or handling sign-out and revocation. Don't use for legacy Google Sign-In (`gapi.auth2`), Google Cloud IAM service accounts, or Android\u002FiOS native Credential Manager.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fidentity\u002Fsign-in-with-google-web\u002FSKILL.md",[83,88],{"categoryNo":84,"categoryName":85,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",{"categoryNo":89,"categoryName":90,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000010","后端与接口",[9,92,93,94,95,96,97,98,99,100,101,25,102,103,104,105,106,107,108,109,110,111,112,113],"sign in with google web","sign","with","web","android","go","security","google workspace","google cloud","implement","python","implementing","react","iam","permissions","buttons","secure","manager","identity","architectures","domain","runtimes",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":129,"publishedAt":39},"01M6998PHPDCAD684VW5WRG83D","dpop-adoption","Implement and debug OAuth 2.0 DPoP (RFC 9449) refresh token sender-constraining for WebCrypto, Node.js ES6, and browser runtimes integrating with Google's OAuth platform. Use when configuring non-extractable asymmetric key pairs (P-256), generating DPoP Proof JWTs for authorization code exchange and token refresh, or handling 400 use_dpop_nonce challenge retry loops at oauth2.googleapis.com\u002Ftoken. Don't use for unconstrained OAuth 2.0 flows (where refresh tokens are not bound to a client key pair), or for Google Cloud IAM \u002F service account authentication.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fidentity\u002Fdpop-adoption\u002FSKILL.md",[120,121,124],{"categoryNo":89,"categoryName":90,"taxonomyType":86,"taxonomyTypeDesc":87},{"categoryNo":122,"categoryName":123,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[130,131,9,100,132,133,134,135,136,137,105,138,139,140,141,142,143,110,113,144,24],"dpop adoption","adoption","es6","retry","authentication","flows","challenge","code","browser","loops","at","node","token","pair","platform",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":157,"publishedAt":39},"01M6998M72TC73EEAQH4XC7EXW","retrieving-developer-knowledge","Searches, retrieves, and synthesizes official Google developer documentation across Google Cloud, AI\u002FGemini, Android, Chrome, Web, Flutter, Go, Firebase, and other Google developer platforms. Integrates with the Developer Knowledge MCP server (search_documents, get_documents, answer_query) or the Developer Knowledge REST API fallback. Use when searching for gcloud CLI commands, API syntax, IAM permissions, official documentation, architectural comparisons, or product choice overviews. Don't use for local filesystem lookups or non-Google documentation.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fdevelopers\u002Fretrieving-developer-knowledge\u002FSKILL.md",[151,152,155,156],{"categoryNo":89,"categoryName":90,"taxonomyType":86,"taxonomyTypeDesc":87},{"categoryNo":153,"categoryName":154,"taxonomyType":86,"taxonomyTypeDesc":87},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":122,"categoryName":123,"taxonomyType":86,"taxonomyTypeDesc":87},{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},[158,159,160,161,100,162,163,164,165,166,167,168,169,24,170,171,172,105,173,106],"retrieving developer knowledge","knowledge","developer","MCP","mcp server","official","developer documentation","get","search","local","choice","documents","query","api","across","documentation",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":180,"publishedAt":39},"01M6998J2CZEXS9BES9S3WWFKC","migrate-to-rust","Clones, analyzes, plans, and migrates legacy codebases (C++, Fortran, Java, Python, C, Shell) from a GitHub URL or local directory to a modern, high-performance, deterministic, 100% Safe Rust architecture, tracking live telemetry across every task bucket, test failure\u002Ffix cycle, and Profile-Guided Optimization (PGO) stage, and producing a first-person AI storytelling report and interactive HTML infographic. Use when migrating a GitHub repository or local codebase to Rust, porting legacy C++, Fortran, Python, or Java workloads to memory-safe Rust, designing Struct-of-Arrays (SoA) or SIMD Rust architectures, or optimizing Rust ports with LLVM PGO. Don't use for unrelated non-Rust refactoring or single-line syntax questions.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fdevelopers\u002Fmigrate-to-rust\u002FSKILL.md",[],[181,182,183,184,185,186,187,188,102,189,190,191,192,193,194,167,195,196,197,24,198,199,172,200],"migrate to rust","rust","migrate","github","memory","performance","directory","safe","ports","llvm","soa","cycle","porting","fix","arrays","infographic","high-performance","URL","architecture","unrelated",{"skillNo":202,"name":203,"nameZh":6,"description":204,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":205,"categories":206,"keywords":210,"publishedAt":39},"01M6998GCC3K29WZ82KXMB9E02","finding-google-skills","Google platform decision and setup guidance, loaded on demand from Google's skill catalog. Use when a developer is choosing or setting up part of their stack, such as where to run a service, a database, storage, messaging, authentication, analytics, ads, or AI model serving, and a Google product is a reasonable candidate - whether or not a vendor is named - or when a request names a Google product or API. Brings in the matching Google skill so the answer can weigh Google options, their trade-offs, and when they are not the right fit. Skip when the stack is already settled on another provider and no Google product is named, or the task involves no platform choice.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fdevelopers\u002Ffinding-google-skills\u002FSKILL.md",[207,208,209],{"categoryNo":89,"categoryName":90,"taxonomyType":86,"taxonomyTypeDesc":87},{"categoryNo":122,"categoryName":123,"taxonomyType":86,"taxonomyTypeDesc":87},{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},[9,211,212,213,214,144,215,216,217,218,219,134,220,221,222,168,35,223,24,224,171],"finding google skills","storage","as","messaging","model","trade-offs","provider","answer","fit","catalog","up","decision","database","candidate",{"skillNo":226,"name":227,"nameZh":6,"description":228,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":229,"categories":230,"keywords":233,"publishedAt":39},"01M6998DWNHTH0BS6W1CY8ZXMP","workload-manager-basics","Use this skill to manage Google Cloud Workload Manager evaluations, rules, scanned resources, and validation results by using public client libraries and the REST API. Use when you need to inspect workload best-practice rules, create and run evaluations for Google Cloud general best practices, SAP, SQL Server, or custom organizational rules, review violations, export results to BigQuery, or automate Workload Manager through client libraries because no service-specific public CLI or MCP server is available. Don't use for general Google Compute Engine instance management, VPC configuration, or standard IAM auditing.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fworkload-manager-basics\u002FSKILL.md",[231,232],{"categoryNo":89,"categoryName":90,"taxonomyType":86,"taxonomyTypeDesc":87},{"categoryNo":122,"categoryName":123,"taxonomyType":86,"taxonomyTypeDesc":87},[234,235,109,236,9,161,237,100,162,238,239,240,241,24,242,171,105,243,244,245,246],"workload manager basics","workload","basics","cli","review","vpc","resources","inspect","you","create","libraries","server","run"]