[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69944Z3RAZ7MW3XRXWS9QC7":3,"skill-same:01M69944Z3RAZ7MW3XRXWS9QC7":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},"01M69944Z3RAZ7MW3XRXWS9QC7","gke-inference",null,"Deploys and optimizes AI\u002FML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for migrating existing AI workloads to GKE (use google-cloud-solution-guided-gke-ai-migration), GKE RAG with Cloud SQL\u002FAlloyDB (use google-cloud-solution-rag-enterprise-search-gke-sqldb), or batch\u002FHPC (use gke-batch-hpc).","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-inference\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,9,32,33,34,35,36,37,38],"inference","gke inference","gke","Enterprise","when","hpc","Existing","for","with","resources","use","model","google-cloud","batch","search","gpu","rag","ml","containers","llms","2026-11-10 10:12:30",false,"---\nname: gke-inference\ndescription: >-\n  Deploys and optimizes AI\u002FML inference workloads on GKE, using GPUs, TPUs, and\n  model servers. Use when deploying GKE inference servers, configuring GKE GPU\n  resources for inference, or deploying LLMs on GKE. Don't use for migrating\n  existing AI workloads to GKE (use google-cloud-solution-guided-gke-ai-migration),\n  GKE RAG with Cloud SQL\u002FAlloyDB (use google-cloud-solution-rag-enterprise-search-gke-sqldb),\n  or batch\u002FHPC (use gke-batch-hpc).\nmetadata:\n  version: \"1.0.2\"\n  category: Containers\n---\n\n# GKE AI\u002FML Inference\n\n> **Routing Note:** For migrating existing AI workloads to GKE, open `google-cloud-solution-guided-gke-ai-migration\u002FSKILL.md`. For GKE RAG with Cloud SQL or AlloyDB (`pgvector`), open `google-cloud-solution-rag-enterprise-search-gke-sqldb\u002FSKILL.md`.\n\nThis reference covers deploying AI\u002FML inference workloads on GKE using Google's\nInference Quickstart (GIQ) and best practices for LLM serving.\n\n> **MCP Tools:** `apply_k8s_manifest`, `get_k8s_resource`, `get_k8s_logs`,\n> `get_k8s_rollout_status`, `describe_k8s_resource`, `list_k8s_events`.\n> **CLI-only:** `gcloud container ai profiles *`\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-inference)\"`\nand append `agent-skills\u002F1.0 (skill:gke-inference)` to `User-Agent` headers.\n\n## When to Use\n\n-   Deploy an AI model (Llama, Gemma, Mistral, etc.) to GKE\n-   Generate optimized Kubernetes manifests for inference\n-   Select GPU\u002FTPU accelerators for model serving\n-   Configure autoscaling for LLM inference\n\n## Prerequisites\n\n-   A golden path GKE Autopilot cluster (GPU workloads are supported via\n    ComputeClasses and NAP)\n-   `gcloud` CLI authenticated\n-   Sufficient GPU\u002FTPU quota in the target region\n\n## Workflow\n\n### 1. Discovery: Find Models and Hardware\n\n```bash\n# List all supported models\ngcloud container ai profiles models list --quiet\n\n# Find valid accelerator\u002Fserver combinations for a model\ngcloud container ai profiles list --model=\u003CMODEL_NAME> --quiet\n\n# Example: what can run Gemma 2 9B?\ngcloud container ai profiles list --model=gemma-2-9b-it --quiet\n```\n\n### 2. Generate Manifest\n\n```bash\ngcloud container ai profiles manifests create \\\n  --model=\u003CMODEL_NAME> \\\n  --model-server=\u003CSERVER> \\\n  --accelerator-type=\u003CACCELERATOR> \\\n  --target-ntpot-milliseconds=\u003CNTPOT> --quiet > inference.yaml\n```\n\n**Parameters:**\n\n-   `--model`: Model ID (e.g., `gemma-2-9b-it`, `llama-3-8b`)\n-   `--model-server`: Inference server (`vllm`, `tgi`, `triton`, `tensorrt-llm`)\n-   `--accelerator-type`: GPU\u002FTPU type (`nvidia-l4`, `nvidia-tesla-a100`,\n    `nvidia-h100-80gb`)\n-   `--target-ntpot-milliseconds`: Target Normalized Time Per Output Token\n    (optional, for latency optimization)\n\n**Example:**\n\n```bash\ngcloud container ai profiles manifests create \\\n  --model=gemma-2-9b-it \\\n  --model-server=vllm \\\n  --accelerator-type=nvidia-l4 \\\n  --target-ntpot-milliseconds=50 --quiet > inference.yaml\n```\n\n### 3. Review and Deploy\n\n```bash\n# Review for placeholders (HF tokens, PVCs)\ncat inference.yaml\n\n# Deploy\nkubectl apply -f inference.yaml\n\n# Monitor\nkubectl get pods -w\nkubectl logs -f \u003CPOD_NAME>\n```\n\n> Some models require Hugging Face tokens. Create a Kubernetes Secret and\n> reference it in the manifest.\n\n## GPU ComputeClass for Inference\n\nFor Autopilot clusters, create a ComputeClass to target GPU nodes:\n\n```yaml\napiVersion: cloud.google.com\u002Fv1\nkind: ComputeClass\nmetadata:\n  name: l4-inference\nspec:\n  priorities:\n  - machineFamily: g2\n    gpu:\n      type: nvidia-l4\n      count: 1\n    minCores: 4\n    minMemoryGb: 16\n```\n\n## Accelerator Selection Guide\n\n| Accelerator         | Best For                 | Memory      | Relative Cost |\n| ------------------- | ------------------------ | ----------- | ------------- |\n| NVIDIA T4           | Budget inference,        | 16 GB       | Lowest        |\n:                     : lightweight legacy       :             :               :\n:                     : models                   :             :               :\n| NVIDIA L4 (G2)      | Small-medium model       | 24 GB       | Low           |\n:                     : inference, video,        :             :               :\n:                     : graphics                 :             :               :\n| NVIDIA RTX PRO 6000 | Multimodal AI,           | 96 GB       | Medium        |\n: (G4)                : high-fidelity 3D,        :             :               :\n:                     : fine-tuning              :             :               :\n| Cloud TPU v5e       | Cost-effective           | Varies      | Medium        |\n:                     : transformer inference    :             :               :\n| Cloud TPU v5p       | High-performance         | Varies      | High          |\n:                     : training                 :             :               :\n| Cloud TPU v6e       | High-efficiency next-gen | 32 GB\u002Fchip  | Medium-High   |\n: (Trillium)          : training & serving       :             :               :\n| Cloud TPU v7x       | Ultra-scale inference &  | 192 GB\u002Fchip | High          |\n: (Ironwood)          : agentic workflows        :             :               :\n| NVIDIA A100         | Large model inference,   | 40\u002F80 GB    | High          |\n:                     : enterprise ML            :             :               :\n| NVIDIA H100 \u002F H200  | Frontier model training, | 80\u002F141 GB   | Highest       |\n:                     : high throughput          :             :               :\n| NVIDIA B200 (A4)    | Blackwell-scale          | 192 GB      | Highest       |\n:                     : training, FP4 precision  :             :               :\n| NVIDIA GB200 (A4X)  | Rack-scale AI (Grace     | Massive     | Highest       |\n:                     : Blackwell Superchip)     :             :               :\n\n## Autoscaling LLM Inference\n\n### GPU-based autoscaling\n\nUse custom metrics for GPU utilization:\n\n```yaml\napiVersion: autoscaling\u002Fv2\nkind: HorizontalPodAutoscaler\nmetadata:\n  name: llm-hpa\nspec:\n  scaleTargetRef:\n    apiVersion: apps\u002Fv1\n    kind: Deployment\n    name: llm-server\n  minReplicas: 1\n  maxReplicas: 10\n  metrics:\n  - type: Pods\n    pods:\n      metric:\n        name: gpu_duty_cycle\n      target:\n        type: AverageValue\n        averageValue: \"80\"\n```\n\n### Best practices for inference autoscaling\n\n1.  **Use DCGM metrics**: Golden path enables DCGM monitoring for GPU\n    utilization metrics\n2.  **Set appropriate minReplicas**: At least 1 for always-on serving; 0 for\n    batch\u002Fon-demand\n3.  **Tune scale-down delay**: LLM model loading is slow; use longer\n    stabilization windows\n4.  **Consider queue depth**: Scale on pending requests rather than pure GPU\n    utilization for latency-sensitive workloads\n\n## Optimization Tips\n\n-   **Quantization**: Use quantized models (GPTQ, AWQ) to reduce GPU memory and\n    increase throughput\n-   **Batching**: Configure model server batch size for throughput vs latency\n    trade-off\n-   **Tensor parallelism**: Split large models across multiple GPUs within a\n    node\n-   **KV cache optimization**: Tune `--gpu-memory-utilization` in vLLM for KV\n    cache allocation\n\n## Troubleshooting\n\n| Issue              | Cause                    | Fix                         |\n| ------------------ | ------------------------ | --------------------------- |\n| Invalid            | Unsupported tuple        | Re-run `gcloud container ai |\n: model\u002Faccelerator  :                          : profiles list               :\n: combination        :                          : --model=\u003CMODEL>`            :\n| GPU quota exceeded | Regional quota limit     | Request quota increase or   |\n:                    :                          : try a different region      :\n| OOM on GPU         | Model too large for      | Use larger GPU, enable      |\n:                    : accelerator              : quantization, or use tensor :\n:                    :                          : parallelism                 :\n| Slow cold start    | Large model loading from | Use local SSD for model     |\n:                    : registry                 : caching; pre-pull images    :\n","# GKE AI\u002FML Inference\n\n> **Routing Note:** For migrating existing AI workloads to GKE, open `google-cloud-solution-guided-gke-ai-migration\u002FSKILL.md`. For GKE RAG with Cloud SQL or AlloyDB (`pgvector`), open `google-cloud-solution-rag-enterprise-search-gke-sqldb\u002FSKILL.md`.\n\nThis reference covers deploying AI\u002FML inference workloads on GKE using Google's\nInference Quickstart (GIQ) and best practices for LLM serving.\n\n> **MCP Tools:** `apply_k8s_manifest`, `get_k8s_resource`, `get_k8s_logs`,\n> `get_k8s_rollout_status`, `describe_k8s_resource`, `list_k8s_events`.\n> **CLI-only:** `gcloud container ai profiles *`\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-inference)\"`\nand append `agent-skills\u002F1.0 (skill:gke-inference)` to `User-Agent` headers.\n\n## When to Use\n\n-   Deploy an AI model (Llama, Gemma, Mistral, etc.) to GKE\n-   Generate optimized Kubernetes manifests for inference\n-   Select GPU\u002FTPU accelerators for model serving\n-   Configure autoscaling for LLM inference\n\n## Prerequisites\n\n-   A golden path GKE Autopilot cluster (GPU workloads are supported via\n    ComputeClasses and NAP)\n-   `gcloud` CLI authenticated\n-   Sufficient GPU\u002FTPU quota in the target region\n\n## Workflow\n\n### 1. Discovery: Find Models and Hardware\n\n```bash\n# List all supported models\ngcloud container ai profiles models list --quiet\n\n# Find valid accelerator\u002Fserver combinations for a model\ngcloud container ai profiles list --model=\u003CMODEL_NAME> --quiet\n\n# Example: what can run Gemma 2 9B?\ngcloud container ai profiles list --model=gemma-2-9b-it --quiet\n```\n\n### 2. Generate Manifest\n\n```bash\ngcloud container ai profiles manifests create \\\n  --model=\u003CMODEL_NAME> \\\n  --model-server=\u003CSERVER> \\\n  --accelerator-type=\u003CACCELERATOR> \\\n  --target-ntpot-milliseconds=\u003CNTPOT> --quiet > inference.yaml\n```\n\n**Parameters:**\n\n-   `--model`: Model ID (e.g., `gemma-2-9b-it`, `llama-3-8b`)\n-   `--model-server`: Inference server (`vllm`, `tgi`, `triton`, `tensorrt-llm`)\n-   `--accelerator-type`: GPU\u002FTPU type (`nvidia-l4`, `nvidia-tesla-a100`,\n    `nvidia-h100-80gb`)\n-   `--target-ntpot-milliseconds`: Target Normalized Time Per Output Token\n    (optional, for latency optimization)\n\n**Example:**\n\n```bash\ngcloud container ai profiles manifests create \\\n  --model=gemma-2-9b-it \\\n  --model-server=vllm \\\n  --accelerator-type=nvidia-l4 \\\n  --target-ntpot-milliseconds=50 --quiet > inference.yaml\n```\n\n### 3. Review and Deploy\n\n```bash\n# Review for placeholders (HF tokens, PVCs)\ncat inference.yaml\n\n# Deploy\nkubectl apply -f inference.yaml\n\n# Monitor\nkubectl get pods -w\nkubectl logs -f \u003CPOD_NAME>\n```\n\n> Some models require Hugging Face tokens. Create a Kubernetes Secret and\n> reference it in the manifest.\n\n## GPU ComputeClass for Inference\n\nFor Autopilot clusters, create a ComputeClass to target GPU nodes:\n\n```yaml\napiVersion: cloud.google.com\u002Fv1\nkind: ComputeClass\nmetadata:\n  name: l4-inference\nspec:\n  priorities:\n  - machineFamily: g2\n    gpu:\n      type: nvidia-l4\n      count: 1\n    minCores: 4\n    minMemoryGb: 16\n```\n\n## Accelerator Selection Guide\n\n| Accelerator         | Best For                 | Memory      | Relative Cost |\n| ------------------- | ------------------------ | ----------- | ------------- |\n| NVIDIA T4           | Budget inference,        | 16 GB       | Lowest        |\n:                     : lightweight legacy       :             :               :\n:                     : models                   :             :               :\n| NVIDIA L4 (G2)      | Small-medium model       | 24 GB       | Low           |\n:                     : inference, video,        :             :               :\n:                     : graphics                 :             :               :\n| NVIDIA RTX PRO 6000 | Multimodal AI,           | 96 GB       | Medium        |\n: (G4)                : high-fidelity 3D,        :             :               :\n:                     : fine-tuning              :             :               :\n| Cloud TPU v5e       | Cost-effective           | Varies      | Medium        |\n:                     : transformer inference    :             :               :\n| Cloud TPU v5p       | High-performance         | Varies      | High          |\n:                     : training                 :             :               :\n| Cloud TPU v6e       | High-efficiency next-gen | 32 GB\u002Fchip  | Medium-High   |\n: (Trillium)          : training & serving       :             :               :\n| Cloud TPU v7x       | Ultra-scale inference &  | 192 GB\u002Fchip | High          |\n: (Ironwood)          : agentic workflows        :             :               :\n| NVIDIA A100         | Large model inference,   | 40\u002F80 GB    | High          |\n:                     : enterprise ML            :             :               :\n| NVIDIA H100 \u002F H200  | Frontier model training, | 80\u002F141 GB   | Highest       |\n:                     : high throughput          :             :               :\n| NVIDIA B200 (A4)    | Blackwell-scale          | 192 GB      | Highest       |\n:                     : training, FP4 precision  :             :               :\n| NVIDIA GB200 (A4X)  | Rack-scale AI (Grace     | Massive     | Highest       |\n:                     : Blackwell Superchip)     :             :               :\n\n## Autoscaling LLM Inference\n\n### GPU-based autoscaling\n\nUse custom metrics for GPU utilization:\n\n```yaml\napiVersion: autoscaling\u002Fv2\nkind: HorizontalPodAutoscaler\nmetadata:\n  name: llm-hpa\nspec:\n  scaleTargetRef:\n    apiVersion: apps\u002Fv1\n    kind: Deployment\n    name: llm-server\n  minReplicas: 1\n  maxReplicas: 10\n  metrics:\n  - type: Pods\n    pods:\n      metric:\n        name: gpu_duty_cycle\n      target:\n        type: AverageValue\n        averageValue: \"80\"\n```\n\n### Best practices for inference autoscaling\n\n1.  **Use DCGM metrics**: Golden path enables DCGM monitoring for GPU\n    utilization metrics\n2.  **Set appropriate minReplicas**: At least 1 for always-on serving; 0 for\n    batch\u002Fon-demand\n3.  **Tune scale-down delay**: LLM model loading is slow; use longer\n    stabilization windows\n4.  **Consider queue depth**: Scale on pending requests rather than pure GPU\n    utilization for latency-sensitive workloads\n\n## Optimization Tips\n\n-   **Quantization**: Use quantized models (GPTQ, AWQ) to reduce GPU memory and\n    increase throughput\n-   **Batching**: Configure model server batch size for throughput vs latency\n    trade-off\n-   **Tensor parallelism**: Split large models across multiple GPUs within a\n    node\n-   **KV cache optimization**: Tune `--gpu-memory-utilization` in vLLM for KV\n    cache allocation\n\n## Troubleshooting\n\n| Issue              | Cause                    | Fix                         |\n| ------------------ | ------------------------ | --------------------------- |\n| Invalid            | Unsupported tuple        | Re-run `gcloud container ai |\n: model\u002Faccelerator  :                          : profiles list               :\n: combination        :                          : --model=\u003CMODEL>`            :\n| GPU quota exceeded | Regional quota limit     | Request quota increase or   |\n:                    :                          : try a different region      :\n| OOM on GPU         | Model too large for      | Use larger GPU, enable      |\n:                    : accelerator              : quantization, or use tensor :\n:                    :                          : parallelism                 :\n| Slow cold start    | Large model loading from | Use local SSD for model     |\n:                    : registry                 : caching; pre-pull images    :\n",{"name":5,"metadata":44,"description":7},{"version":45,"category":46},"1.0.2","Containers","main","bb9b3f464aae37af360d079c1d863524955b0363","skills\u002Fcloud\u002Fgke-inference",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-inference",{"tool":62,"command":63},"bunx","bunx skills add google\u002Fskills --skill gke-inference",{"tool":65,"command":66},"pnpm","pnpm dlx skills add google\u002Fskills --skill gke-inference","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-inference\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-inference\u002Freferences\u002Fattribution.md",[77,114,145,173,200,223],{"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",[9,92,93,27,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113],"sign in with google web","sign","web","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",[130,131,9,99,132,133,134,135,136,137,105,138,139,140,141,142,143,110,113,144,23],"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},[158,159,160,161,99,162,163,164,165,33,166,167,168,23,169,170,171,105,172,106],"retrieving developer knowledge","knowledge","developer","MCP","mcp server","official","developer documentation","get","local","choice","documents","query","api","across","documentation",{"skillNo":174,"name":175,"nameZh":6,"description":176,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":177,"categories":178,"keywords":179,"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",[],[180,181,182,183,184,185,186,187,102,188,189,190,191,192,193,166,194,195,196,23,197,198,171,199],"migrate to rust","rust","migrate","github","memory","performance","directory","safe","ports","llvm","soa","cycle","porting","fix","arrays","infographic","high-performance","URL","architecture","unrelated",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":204,"categories":205,"keywords":209,"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",[206,207,208],{"categoryNo":89,"categoryName":90,"taxonomyType":86,"taxonomyTypeDesc":87},{"categoryNo":122,"categoryName":123,"taxonomyType":86,"taxonomyTypeDesc":87},{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},[9,210,211,212,213,144,30,214,215,216,217,134,218,219,220,167,29,221,23,222,170],"finding google skills","storage","as","messaging","trade-offs","provider","answer","fit","catalog","up","decision","database","candidate",{"skillNo":224,"name":225,"nameZh":6,"description":226,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":227,"categories":228,"keywords":231,"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",[229,230],{"categoryNo":89,"categoryName":90,"taxonomyType":86,"taxonomyTypeDesc":87},{"categoryNo":122,"categoryName":123,"taxonomyType":86,"taxonomyTypeDesc":87},[232,233,109,234,9,161,235,99,162,236,237,28,238,23,239,170,105,240,241,242,243],"workload manager basics","workload","basics","cli","review","vpc","inspect","you","create","libraries","server","run"]