[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6993NZAP78DGV2RGK35R6AX":3,"skill-same:01M6993NZAP78DGV2RGK35R6AX":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},"01M6993NZAP78DGV2RGK35R6AX","gke-batch-hpc",null,"Runs batch and HPC workloads on GKE, utilizing job queues and parallel processing. Use when running GKE batch jobs, configuring GKE HPC, or setting up GKE job queues. Don't use for standard web application deployments (use gke-app-onboarding instead).","google\u002Fskills","google","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F1342004?v=4","01M698VYEFDM0VJBZDYYJ2T83S","Google","01M698WR0H76ENHE587T9EJT89",23908,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-batch-hpc\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"gke batch hpc","batch","gke","hpc","running","containers","when","standard","queues","for","processing","app","parallel processing","application","jobs","deployments","up","use","parallel","web","2026-11-10 10:12:30",false,"---\nname: gke-batch-hpc\ndescription: >-\n  Runs batch and HPC workloads on GKE, utilizing job queues and parallel\n  processing. Use when running GKE batch jobs, configuring GKE HPC, or setting\n  up GKE job queues. Don't use for standard web application deployments (use\n  gke-app-onboarding instead).\nmetadata:\n  version: \"1.0.1\"\n  category: Containers\n---\n\n# GKE Batch & HPC Workloads\n\nThis reference covers running batch processing and high-performance computing\n(HPC) workloads on GKE.\n\n> **MCP Tools:** `apply_k8s_manifest`, `get_k8s_resource`,\n> `describe_k8s_resource`, `get_k8s_logs`, `delete_k8s_resource`,\n> `list_k8s_events`\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-batch-hpc)\"`\nand append `agent-skills\u002F1.0 (skill:gke-batch-hpc)` to `User-Agent` headers.\n\n## When to Use\n\n-   Running batch data processing pipelines\n-   HPC simulations (CFD, molecular dynamics, financial modeling)\n-   Large-scale parallel computation (MPI, MapReduce)\n-   ML training jobs\n-   CI\u002FCD build farms\n\n## Batch Processing on GKE\n\n### Kubernetes Jobs\n\n```yaml\napiVersion: batch\u002Fv1\nkind: Job\nmetadata:\n  name: batch-job\nspec:\n  parallelism: 10\n  completions: 100\n  backoffLimit: 3\n  template:\n    spec:\n      containers:\n      - name: worker\n        image: \u003CIMAGE>\n        resources:\n          requests:\n            cpu: \"1\"\n            memory: \"2Gi\"\n      restartPolicy: Never\n```\n\n### JobSet (for Complex Multi-Job Workflows)\n\nThe golden path enables JobSet monitoring (`JOBSET` in monitoringConfig).\n\n```yaml\napiVersion: jobset.x-k8s.io\u002Fv1alpha2\nkind: JobSet\nmetadata:\n  name: training-job\nspec:\n  replicatedJobs:\n  - name: workers\n    replicas: 4\n    template:\n      spec:\n        parallelism: 1\n        completions: 1\n        template:\n          spec:\n            containers:\n            - name: worker\n              image: \u003CIMAGE>\n              resources:\n                requests:\n                  cpu: \"4\"\n                  memory: \"8Gi\"\n```\n\n### Kueue (Job Queuing)\n\nKueue manages job scheduling and resource allocation for batch workloads:\n\n```bash\n# Install Kueue\nkubectl apply --server-side -f https:\u002F\u002Fgithub.com\u002Fkubernetes-sigs\u002Fkueue\u002Freleases\u002Flatest\u002Fdownload\u002Fmanifests.yaml\n```\n\n```yaml\n# Define a ClusterQueue\napiVersion: kueue.x-k8s.io\u002Fv1beta1\nkind: ClusterQueue\nmetadata:\n  name: batch-queue\nspec:\n  namespaceSelector: {}\n  resourceGroups:\n  - coveredResources: [\"cpu\", \"memory\"]\n    flavors:\n    - name: default\n      resources:\n      - name: \"cpu\"\n        nominalQuota: 100\n      - name: \"memory\"\n        nominalQuota: \"200Gi\"\n---\n# Allow a namespace to use the queue\napiVersion: kueue.x-k8s.io\u002Fv1beta1\nkind: LocalQueue\nmetadata:\n  name: batch-local\n  namespace: batch-jobs\nspec:\n  clusterQueue: batch-queue\n```\n\n## HPC on GKE\n\n### Compact Placement (Low-Latency Networking)\n\nFor tightly-coupled HPC workloads that need low-latency inter-node\ncommunication:\n\n```bash\n# Standard clusters: create node pool with compact placement\ngcloud container node-pools create hpc-pool \\\n  --cluster \u003CCLUSTER_NAME> --region \u003CREGION> \\\n  --machine-type c3-standard-44 \\\n  --placement-type COMPACT \\\n  --num-nodes 8 \\\n  --enable-autoscaling --min-nodes 0 --max-nodes 16 \\\n  --quiet\n```\n\n### MPI Workloads\n\nUse the MPI Operator for MPI-based HPC applications:\n\n```bash\n# Install MPI Operator\nkubectl apply -f https:\u002F\u002Fraw.githubusercontent.com\u002Fkubeflow\u002Fmpi-operator\u002Fmaster\u002Fdeploy\u002Fv2beta1\u002Fmpi-operator.yaml\n```\n\n```yaml\napiVersion: kubeflow.org\u002Fv2beta1\nkind: MPIJob\nmetadata:\n  name: hpc-simulation\nspec:\n  slotsPerWorker: 4\n  mpiReplicaSpecs:\n    Launcher:\n      replicas: 1\n      template:\n        spec:\n          containers:\n          - name: launcher\n            image: \u003CMPI_IMAGE>\n            command: [\"mpirun\", \"-np\", \"32\", \".\u002Fsimulation\"]\n            resources:\n              requests:\n                cpu: \"1\"\n                memory: \"2Gi\"\n              limits:\n                cpu: \"2\"\n                memory: \"4Gi\"\n    Worker:\n      replicas: 8\n      template:\n        spec:\n          containers:\n          - name: worker\n            image: \u003CMPI_IMAGE>\n            resources:\n              requests:\n                cpu: \"4\"\n                memory: \"8Gi\"\n              limits:\n                cpu: \"8\"\n                memory: \"16Gi\"\n```\n\n## Cost Optimization for Batch\u002FHPC\n\n### Spot VMs for Batch\n\nBatch workloads are ideal Spot VM candidates (interruptible, can checkpoint).\nUse a ComputeClass with Spot-first priority and `activeMigration` to return to\nSpot when available. See the `gke-compute-classes` skill for the\nSpot-with-fallback pattern.\n\n### Scale-to-Zero\n\nFor batch clusters, allow node pools to scale to zero when no jobs are running:\n\n-   Autopilot (golden path): Automatic, nodes scale to zero when no pods are\n    scheduled\n-   Standard: Set `--min-nodes 0` on batch node pools\n\n## Best Practices & Production Guidelines\n\n-   **Resource Quotas**: Always specify resource requests and limits (CPU,\n    memory, and optionally GPU\u002FTPU) for all batch\u002FHPC manifests. This is\n    critical for Kueue admission, autoscaling, and preventing resource\n    starvation in the cluster.\n-   **TPU\u002FSpot Cluster Maintenance**: For long-running AI training runs on Spot\n    VMs\u002FTPUs, advise using **GKE maintenance exclusions** to block automatic\n    cluster upgrades\u002Freboots during the active training window to minimize\n    unnecessary preemption.\n-   **MPI Workloads**: Use the **Kubeflow Training Operator** to orchestrate\n    distributed MPI applications via the `MPIJob` custom resource.\n-   **Kueue & JobSet**: Use **Kueue** for multi-tenant job queueing and fair\n    sharing; use **JobSet** for multi-component tightly coupled workloads.\n-   **Resilience**: Always set a `backoffLimit` on Jobs, and implement\n    application-level checkpointing (e.g., using Orbax or PyTorch checkpointing)\n    to survive Spot VM preemption.\n","# GKE Batch & HPC Workloads\n\nThis reference covers running batch processing and high-performance computing\n(HPC) workloads on GKE.\n\n> **MCP Tools:** `apply_k8s_manifest`, `get_k8s_resource`,\n> `describe_k8s_resource`, `get_k8s_logs`, `delete_k8s_resource`,\n> `list_k8s_events`\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-batch-hpc)\"`\nand append `agent-skills\u002F1.0 (skill:gke-batch-hpc)` to `User-Agent` headers.\n\n## When to Use\n\n-   Running batch data processing pipelines\n-   HPC simulations (CFD, molecular dynamics, financial modeling)\n-   Large-scale parallel computation (MPI, MapReduce)\n-   ML training jobs\n-   CI\u002FCD build farms\n\n## Batch Processing on GKE\n\n### Kubernetes Jobs\n\n```yaml\napiVersion: batch\u002Fv1\nkind: Job\nmetadata:\n  name: batch-job\nspec:\n  parallelism: 10\n  completions: 100\n  backoffLimit: 3\n  template:\n    spec:\n      containers:\n      - name: worker\n        image: \u003CIMAGE>\n        resources:\n          requests:\n            cpu: \"1\"\n            memory: \"2Gi\"\n      restartPolicy: Never\n```\n\n### JobSet (for Complex Multi-Job Workflows)\n\nThe golden path enables JobSet monitoring (`JOBSET` in monitoringConfig).\n\n```yaml\napiVersion: jobset.x-k8s.io\u002Fv1alpha2\nkind: JobSet\nmetadata:\n  name: training-job\nspec:\n  replicatedJobs:\n  - name: workers\n    replicas: 4\n    template:\n      spec:\n        parallelism: 1\n        completions: 1\n        template:\n          spec:\n            containers:\n            - name: worker\n              image: \u003CIMAGE>\n              resources:\n                requests:\n                  cpu: \"4\"\n                  memory: \"8Gi\"\n```\n\n### Kueue (Job Queuing)\n\nKueue manages job scheduling and resource allocation for batch workloads:\n\n```bash\n# Install Kueue\nkubectl apply --server-side -f https:\u002F\u002Fgithub.com\u002Fkubernetes-sigs\u002Fkueue\u002Freleases\u002Flatest\u002Fdownload\u002Fmanifests.yaml\n```\n\n```yaml\n# Define a ClusterQueue\napiVersion: kueue.x-k8s.io\u002Fv1beta1\nkind: ClusterQueue\nmetadata:\n  name: batch-queue\nspec:\n  namespaceSelector: {}\n  resourceGroups:\n  - coveredResources: [\"cpu\", \"memory\"]\n    flavors:\n    - name: default\n      resources:\n      - name: \"cpu\"\n        nominalQuota: 100\n      - name: \"memory\"\n        nominalQuota: \"200Gi\"\n---\n# Allow a namespace to use the queue\napiVersion: kueue.x-k8s.io\u002Fv1beta1\nkind: LocalQueue\nmetadata:\n  name: batch-local\n  namespace: batch-jobs\nspec:\n  clusterQueue: batch-queue\n```\n\n## HPC on GKE\n\n### Compact Placement (Low-Latency Networking)\n\nFor tightly-coupled HPC workloads that need low-latency inter-node\ncommunication:\n\n```bash\n# Standard clusters: create node pool with compact placement\ngcloud container node-pools create hpc-pool \\\n  --cluster \u003CCLUSTER_NAME> --region \u003CREGION> \\\n  --machine-type c3-standard-44 \\\n  --placement-type COMPACT \\\n  --num-nodes 8 \\\n  --enable-autoscaling --min-nodes 0 --max-nodes 16 \\\n  --quiet\n```\n\n### MPI Workloads\n\nUse the MPI Operator for MPI-based HPC applications:\n\n```bash\n# Install MPI Operator\nkubectl apply -f https:\u002F\u002Fraw.githubusercontent.com\u002Fkubeflow\u002Fmpi-operator\u002Fmaster\u002Fdeploy\u002Fv2beta1\u002Fmpi-operator.yaml\n```\n\n```yaml\napiVersion: kubeflow.org\u002Fv2beta1\nkind: MPIJob\nmetadata:\n  name: hpc-simulation\nspec:\n  slotsPerWorker: 4\n  mpiReplicaSpecs:\n    Launcher:\n      replicas: 1\n      template:\n        spec:\n          containers:\n          - name: launcher\n            image: \u003CMPI_IMAGE>\n            command: [\"mpirun\", \"-np\", \"32\", \".\u002Fsimulation\"]\n            resources:\n              requests:\n                cpu: \"1\"\n                memory: \"2Gi\"\n              limits:\n                cpu: \"2\"\n                memory: \"4Gi\"\n    Worker:\n      replicas: 8\n      template:\n        spec:\n          containers:\n          - name: worker\n            image: \u003CMPI_IMAGE>\n            resources:\n              requests:\n                cpu: \"4\"\n                memory: \"8Gi\"\n              limits:\n                cpu: \"8\"\n                memory: \"16Gi\"\n```\n\n## Cost Optimization for Batch\u002FHPC\n\n### Spot VMs for Batch\n\nBatch workloads are ideal Spot VM candidates (interruptible, can checkpoint).\nUse a ComputeClass with Spot-first priority and `activeMigration` to return to\nSpot when available. See the `gke-compute-classes` skill for the\nSpot-with-fallback pattern.\n\n### Scale-to-Zero\n\nFor batch clusters, allow node pools to scale to zero when no jobs are running:\n\n-   Autopilot (golden path): Automatic, nodes scale to zero when no pods are\n    scheduled\n-   Standard: Set `--min-nodes 0` on batch node pools\n\n## Best Practices & Production Guidelines\n\n-   **Resource Quotas**: Always specify resource requests and limits (CPU,\n    memory, and optionally GPU\u002FTPU) for all batch\u002FHPC manifests. This is\n    critical for Kueue admission, autoscaling, and preventing resource\n    starvation in the cluster.\n-   **TPU\u002FSpot Cluster Maintenance**: For long-running AI training runs on Spot\n    VMs\u002FTPUs, advise using **GKE maintenance exclusions** to block automatic\n    cluster upgrades\u002Freboots during the active training window to minimize\n    unnecessary preemption.\n-   **MPI Workloads**: Use the **Kubeflow Training Operator** to orchestrate\n    distributed MPI applications via the `MPIJob` custom resource.\n-   **Kueue & JobSet**: Use **Kueue** for multi-tenant job queueing and fair\n    sharing; use **JobSet** for multi-component tightly coupled workloads.\n-   **Resilience**: Always set a `backoffLimit` on Jobs, and implement\n    application-level checkpointing (e.g., using Orbax or PyTorch checkpointing)\n    to survive Spot VM preemption.\n",{"name":5,"metadata":44,"description":7},{"version":45,"category":46},"1.0.1","Containers","main","bb9b3f464aae37af360d079c1d863524955b0363","skills\u002Fcloud\u002Fgke-batch-hpc",1994,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills","https:\u002F\u002Fgithub.com\u002Fgoogle","https:\u002F\u002Fopensource.google\u002F","Google ❤️ Open Source","2026-11-12 00:37:41",{"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-batch-hpc",{"tool":62,"command":63},"bunx","bunx skills add google\u002Fskills --skill gke-batch-hpc",{"tool":65,"command":66},"pnpm","pnpm dlx skills add google\u002Fskills --skill gke-batch-hpc","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-batch-hpc\u002FSKILL.md",true,[70],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"references\u002Fattribution.md","REFERENCE","参考文档",1879,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-batch-hpc\u002Freferences\u002Fattribution.md",[77,114,145,174,201,224],{"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",[9,92,93,94,38,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113],"sign in with google web","sign","with","android","go","security","google workspace","google cloud","implement","policy","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",[130,131,9,99,132,133,134,135,136,137,105,138,139,140,141,142,143,110,113,144,25],"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},[158,159,160,161,99,162,163,164,165,166,167,168,169,25,170,171,172,105,173,106],"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",[],[181,182,183,184,185,186,187,188,102,189,190,191,192,193,194,167,195,196,197,25,198,199,172,200],"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},[9,211,212,213,214,144,215,216,217,218,219,134,220,35,221,168,36,222,25,223,171],"finding google skills","storage","as","messaging","model","trade-offs","provider","answer","fit","catalog","decision","database","candidate",{"skillNo":225,"name":226,"nameZh":6,"description":227,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":228,"categories":229,"keywords":232,"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",[230,231],{"categoryNo":89,"categoryName":90,"taxonomyType":86,"taxonomyTypeDesc":87},{"categoryNo":122,"categoryName":123,"taxonomyType":86,"taxonomyTypeDesc":87},[233,234,109,235,9,161,236,99,162,237,238,239,240,25,241,171,105,242,243,244,245],"workload manager basics","workload","basics","cli","review","vpc","resources","inspect","you","create","libraries","server","run"]