[{"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",24235,"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",2014,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills","https:\u002F\u002Fgithub.com\u002Fgoogle","https:\u002F\u002Fopensource.google\u002F","Google ❤️ Open Source","2026-11-14 23:31:00",{"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",[77,85,92,99,106,113],{"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":83,"publishedAt":84},"01M7DQ9K7JF4T7NJZ2K1MQZ44Q","cloud-operator-share-context","Shares Google Cloud operating context the user or their team already have — environments, projects, services and regions, product preferences, best practices, architecture guidance, runbooks — found in agent instruction files, local memory, skills and workflows, and the repository's GCP docs, with the operator agent's memory after the user reviews the list. Use when the user runs this skill, accepts the cloud-operator or cloud-operator-setup skill's offer to share their existing Google Cloud context, or asks to share, import or save that context or a GCP fact for the operator agent. Don't use for ordinary GCP requests, which go to the cloud-operator skill.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fcloud-operator-share-context\u002FSKILL.md",[],[],"2026-11-15 14:19:17",{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":91,"publishedAt":84},"01M7DQ9ERFGGQCFAWQW0KRKYBY","cloud-operator-setup","Sets up the operator agent in a Google Cloud project via the autocloud MCP server: picks the operator project, creates or checks the user's operator agent, saves the project, and offers to share the user's existing Google Cloud operating context with the operator agent's memory. Use when the user runs this skill, asks to set up the operator or create their operator agent, or chooses setup when the cloud-operator skill asks. Don't use for ordinary GCP requests or installing gcloud.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fcloud-operator-setup\u002FSKILL.md",[],[],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":84},"01M7B4RHQCM0DK2XQ7S7ZXVN8D","developer-device-platform-device-run","Runs automated, non-interactive jobs on remote Android devices with Developer Device Platform (DDP) Device Run using `gcloud beta device-run sessions`: native Android executables or shell scripts (for example CLI tools, custom runners, test harnesses or benchmarks) and Android instrumentation tests, on one or many devices, then waits for completion and collects per-device outputs and logs. Use when running a binary or test suite on remote devices without an interactive connection. Don't use it for interactive device reservations, adb tunnels or screen streaming (use developer-device-platform-basics), or for local devices.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fdeveloper-device-platform-device-run\u002FSKILL.md",[],[],{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":84},"01M7B4R9YXMRW7RZP9Q9FMZJ2P","compute-vm-creation","Guides the planning, configuration, and provisioning of standalone Google Compute Engine virtual machines (VMs) and bare metal (-metal) instances using gcloud CLI commands or direct MCP tool execution. Use ONLY when the user wants to create, configure, or provision a new standalone Compute Engine VM or bare metal instance from conversational intents (hardware sizing, region selection, OS image, networking, security defaults). DO NOT read or use for Google Kubernetes Engine (GKE) Autopilot or Standard clusters, Kubernetes node pools or Pods, container orchestration, managing existing VMs, Managed Instance Groups (MIGs), or Instance Templates.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fcompute-vm-creation\u002FSKILL.md",[],[],{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":84},"01M7B4QD02N0YBVT0838Z5K5VC","agent-platform-autorater-customization","Customizes an autorater (evaluation metric) for an AI agent so its grades match the user's own judgment, starting from the agent's real trajectories. Use when a user wants a metric tailored to their agent, wants to turn failing trajectories into rubrics, says an existing autorater grades their agent wrong, or wants an autorater to agree with human labels (autorater hillclimbing, judge calibration). Don't use for quick evals with built-in metrics or for improving the agent itself (use agent-platform-eval-flywheel).","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fagent-platform-autorater-customization\u002FSKILL.md",[],[],{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":119,"publishedAt":84},"01M78JP22K73KK96JH0JNDT5C4","iap-agent-platform-troubleshooting","Diagnoses Identity-Aware Proxy (IAP) denials of outbound requests from Gemini Enterprise Agent Platform agents that run on Agent Runtime and send traffic through Agent Gateway. Use when an agent request fails with \"Egress request is not authorized\", when Cloud Audit Logs show an IAP egressViaIAP decision with granted false, when IAP audit entries are missing while debugging agent egress, or when Access policies, IAP allow policies, policy bindings, or Agent Registry entries might block agent egress. Don't use for Gemini model IAM permission errors (such as aiplatform.endpoints.predict), Agent Platform API errors, or Agent Runtime deployment or 500 errors (use agent-platform-troubleshooting instead). Don't use for setting up, creating, or connecting an Agent Gateway, or for IAP web apps, IAP TCP forwarding, or SSH.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fiap-agent-platform-troubleshooting\u002FSKILL.md",[],[]]