[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6993ZSKQFVS0AM657RW0BA3":3,"skill-same:01M6993ZSKQFVS0AM657RW0BA3":90},{"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},"01M6993ZSKQFVS0AM657RW0BA3","gke-cost-optimization",null,"Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Use when optimizing GKE cluster or workload costs, configuring GKE cost allocation or quotas, rightsizing CPU\u002Fmemory requests, or selecting Spot VMs and machine types. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).","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-cost-optimization\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"gke cost optimization","cost optimization","Optimization","gke","cost","when","memory","vms","for","requests","types","CPU","machine","cluster","spot","compute","selection","general","use","gpu","2026-11-10 10:12:30",false,"---\nname: gke-cost-optimization\ndescription: >-\n  Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost\n  allocation, and resource quotas. Use when optimizing GKE cluster or workload\n  costs, configuring GKE cost allocation or quotas, rightsizing CPU\u002Fmemory\n  requests, or selecting Spot VMs and machine types. Don't use for general\n  compute class provisioning or GPU Selection (use gke-compute-classes instead).\nmetadata:\n  version: \"1.0.1\"\n  category: CloudObservabilityAndMonitoring\n---\n\n# GKE Cost Optimization\n\nThis reference covers strategies and workflows for reducing Google Kubernetes\nEngine (GKE) costs while maintaining a secure and reliable posture.\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-cost-optimization)\"`\nand append `agent-skills\u002F1.0 (skill:gke-cost-optimization)` to `User-Agent` headers.\n\n## Workflows & Optimization Strategies\n\n### 1. Prerequisite: Cost Allocation & Monitoring\n\nTo enable GKE cost allocation (`--enable-cost-allocation`) for billing tracking\nacross namespaces and labels, inspect live cluster utilization (`kubectl top`),\nor run historical cost breakdown queries in BigQuery (`bq`), use the\n**`gke-cost-analysis`** skill. Once tracking is active and waste is diagnosed,\napply the optimization workflows below.\n\n### 2. Configure Resource Quotas\n\nResource quotas restrict total resource consumption across tenants in\nmulti-tenant clusters, preventing runaway costs. Template:\n[assets\u002Fresource-quota-example.yaml](assets\u002Fresource-quota-example.yaml)\n(set namespace + `hard` limits, then `kubectl apply -f`).\n\n### 3. Pod Rightsizing (VPA & MPA)\n\nAdjust pod resource requests to match actual utilization. Over-provisioned\nrequests are one of the largest sources of waste.\n\n-   **Use VPA in Recommendation Mode** (`updateMode: \"Off\"` — recommends\n    without evicting):\n\n```bash\n# 1. Deploy VPA in recommendation mode (template: assets\u002Fvpa-recommendation-mode.yaml)\nkubectl apply -f assets\u002Fvpa-recommendation-mode.yaml\n# 2. Wait 24+ hours for data collection, then read recommendations\nkubectl get vpa {deployment_name}-vpa -o jsonpath='{.status.recommendation}'\n```\n\n-   **Optimization Rules:**\n\nCondition                     | Action                             | Savings\n----------------------------- | ---------------------------------- | -------\nCPU request >5x P95 actual    | Reduce to `P95 * 1.2`              | High\nMemory request >3x P95 actual | Reduce to `P95 * 1.2`              | High\nCPU request >2x P95 actual    | Reduce to `P95 * 1.2`              | Medium\nNo resource requests set      | Add requests (enables bin-packing) | Medium\n\n-   **Use MPA**: Reconcile HPA and VPA recommendations when scaling both\n    horizontally and vertically to avoid conflicting scale events.\n-   **Review Cost Recommendations**: Check Google Cloud Console (`Cost\n    Management` > `GKE Cost Optimization`) for built-in rightsizing suggestions.\n\n### 4. Spot VMs via ComputeClasses & NodeSelector\n\nUse Spot VMs for fault-tolerant workloads to achieve 60-90% cost reduction.\n\n#### 4.1 ComputeClass Configuration\n\nFor a Spot-first ComputeClass with On-Demand fallback (priority ordering,\n`activeMigration`, machine family selection), use the **`gke-compute-classes`**\nskill — ComputeClass YAML generation and priority configuration are its domain,\nnot this skill's.\n\n#### 4.2 Direct Workload Spot Selection (`nodeSelector`)\n\nFor stateless or batch workloads in GKE Autopilot, target Spot capacity directly\nusing `nodeSelector`:\n\n> [!WARNING] **Preemption Warning**: Spot VMs are interruptible and can be\n> preempted at any time with a 30-second notice. Workloads must be\n> fault-tolerant and run with at least 2 replicas for high availability. Always\n> explicitly warn users about this preemption risk when recommending Spot VMs.\n\nThe exact Pod-level selector is:\n\n```yaml\nnodeSelector:\n  cloud.google.com\u002Fgke-spot: \"true\"\n```\n\nFull worked Deployment (replicas >= 2, `terminationGracePeriodSeconds: 25`,\n`preStop` hook): [assets\u002Fspot-deployment-example.yaml](assets\u002Fspot-deployment-example.yaml).\n\n**Spot-Suitable Workloads:**\n\nWorkload                          | Spot-Suitable?\n--------------------------------- | ---------------\nBatch \u002F data processing           | Yes\nDev \u002F test environments           | Yes\nStateless web\u002FAPI (replicas >= 2) | Yes (with PDBs)\nJobs with checkpointing           | Yes\nStateful workloads (databases)    | No\nSingle-replica critical services  | No\n\n### 5. Machine Type Selection\n\nWhen choosing node shapes or configuring ComputeClasses:\n\n| Family        | Use Case                                          | Relative Cost |\n| ------------- | ------------------------------------------------- | ------------- |\n| e2            | General purpose, burstable                        | Lowest        |\n| t2a \u002F t2d     | Scale-out (Arm\u002FAMD), price-performance optimized  | Low           |\n| n4a           | Axion Arm-based, general-purpose price-performance | Low          |\n| n4 \u002F n4d      | General purpose (Intel\u002FAMD), flexible shapes      | Low-Medium    |\n| c4a           | Axion Arm-based, general-purpose, high efficiency | Medium        |\n| c3 \u002F c4       | Compute-optimized (Intel)                         | Medium-High   |\n| c3d \u002F c4d     | Compute-optimized (AMD), high throughput          | Medium-High   |\n| ek-standard   | Autopilot enhanced                                | Medium        |\n| m3 \u002F x4       | Memory-optimized, SAP HANA, large databases       | High          |\n| g2 (L4 GPU)   | AI inference                                      | High          |\n| a3 (H100 GPU) | AI training                                       | Highest       |\n| a4 \u002F a4x      | Ultra-scale AI (Blackwell GPUs)                   | Highest       |\n\n### 6. Committed Use Discounts (CUDs)\n\nFor steady-state workloads with predictable baseline usage, purchase 1-year or\n3-year CUDs:\n\n-   **Resource-based CUDs** (committed to a machine family\u002Fregion): roughly\n    high-30s% discount for 1-year, ~55% for 3-year (varies by machine family).\n-   **Flexible CUDs** (spend-based, portable across families\u002Fregions): lower\n    discounts (~28% 1-year, ~46% 3-year) in exchange for flexibility.\n-   **Autopilot:** Autopilot-specific CUDs were retired in January 2026 — new\n    commitments covering Autopilot usage are spend-based Compute Flexible CUDs\n    (existing Autopilot CUD commitments run out their term).\n-   Applied automatically to matching usage across the region.\n-   Purchase via Google Cloud Console > Billing > Committed use discounts.\n\n**Size the commitment to the steady-state baseline only.** A commitment bills for\nthe full term whether or not you use it, so over-committing to peak usage\nconverts a discount into waste. Measure the floor of actual usage over a\nrepresentative period, commit to that, and cover everything above it with the\nelastic options already in this skill:\n\n-   **Baseline** (always running) → resource-based CUDs.\n-   **Variable \u002F bursty** → autoscaling on on-demand capacity.\n-   **Interruption-tolerant** (batch, CI, stateless workers) → Spot VMs, which\n    stack with autoscaling and need no commitment.\n\nWhen recommending CUDs, state the split explicitly rather than implying the whole\nfootprint should be committed.\n\n### 7. Cluster Management & Multi-Tenancy\n\n-   **Idle dev clusters**: GKE has no stop\u002Fstart operation, and the cluster\n    management fee accrues as long as the cluster exists. To cut idle costs,\n    scale node pools to zero (`gcloud container clusters resize {cluster_name}\n    --node-pool {pool_name} --num-nodes 0`) or delete and recreate the cluster\n    via IaC (Terraform\u002FConfig Connector).\n-   **Right-size node pools (Standard)**: Use Cluster Autoscaler with\n    appropriate min\u002Fmax limits.\n-   **Cheap warm headroom instead of overprovisioned nodes**: Standby capacity\n    buffers (Preview, GKE 1.36.0-gke.2253000+) keep pre-initialized nodes\n    suspended — you pay only disk + IP instead of full node price, with ~30s\n    resume. See the **`gke-cluster-autoscaler`** skill.\n-   **Multi-tenant consolidation**: Share a single cluster across multiple\n    engineering teams instead of maintaining per-team clusters, using Namespaces\n    and ResourceQuotas to isolate workloads.\n\n## Cost & Utilization Monitoring\n\nTo inspect live node\u002Fpod utilization (`kubectl top nodes\u002Fpods`), view cluster\ncost budgets (`gcloud billing budgets list`), or query detailed billing reports\nin BigQuery (`bq query`), refer to the **`gke-cost-analysis`** skill.\n","# GKE Cost Optimization\n\nThis reference covers strategies and workflows for reducing Google Kubernetes\nEngine (GKE) costs while maintaining a secure and reliable posture.\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-cost-optimization)\"`\nand append `agent-skills\u002F1.0 (skill:gke-cost-optimization)` to `User-Agent` headers.\n\n## Workflows & Optimization Strategies\n\n### 1. Prerequisite: Cost Allocation & Monitoring\n\nTo enable GKE cost allocation (`--enable-cost-allocation`) for billing tracking\nacross namespaces and labels, inspect live cluster utilization (`kubectl top`),\nor run historical cost breakdown queries in BigQuery (`bq`), use the\n**`gke-cost-analysis`** skill. Once tracking is active and waste is diagnosed,\napply the optimization workflows below.\n\n### 2. Configure Resource Quotas\n\nResource quotas restrict total resource consumption across tenants in\nmulti-tenant clusters, preventing runaway costs. Template:\n[assets\u002Fresource-quota-example.yaml](assets\u002Fresource-quota-example.yaml)\n(set namespace + `hard` limits, then `kubectl apply -f`).\n\n### 3. Pod Rightsizing (VPA & MPA)\n\nAdjust pod resource requests to match actual utilization. Over-provisioned\nrequests are one of the largest sources of waste.\n\n-   **Use VPA in Recommendation Mode** (`updateMode: \"Off\"` — recommends\n    without evicting):\n\n```bash\n# 1. Deploy VPA in recommendation mode (template: assets\u002Fvpa-recommendation-mode.yaml)\nkubectl apply -f assets\u002Fvpa-recommendation-mode.yaml\n# 2. Wait 24+ hours for data collection, then read recommendations\nkubectl get vpa {deployment_name}-vpa -o jsonpath='{.status.recommendation}'\n```\n\n-   **Optimization Rules:**\n\nCondition                     | Action                             | Savings\n----------------------------- | ---------------------------------- | -------\nCPU request >5x P95 actual    | Reduce to `P95 * 1.2`              | High\nMemory request >3x P95 actual | Reduce to `P95 * 1.2`              | High\nCPU request >2x P95 actual    | Reduce to `P95 * 1.2`              | Medium\nNo resource requests set      | Add requests (enables bin-packing) | Medium\n\n-   **Use MPA**: Reconcile HPA and VPA recommendations when scaling both\n    horizontally and vertically to avoid conflicting scale events.\n-   **Review Cost Recommendations**: Check Google Cloud Console (`Cost\n    Management` > `GKE Cost Optimization`) for built-in rightsizing suggestions.\n\n### 4. Spot VMs via ComputeClasses & NodeSelector\n\nUse Spot VMs for fault-tolerant workloads to achieve 60-90% cost reduction.\n\n#### 4.1 ComputeClass Configuration\n\nFor a Spot-first ComputeClass with On-Demand fallback (priority ordering,\n`activeMigration`, machine family selection), use the **`gke-compute-classes`**\nskill — ComputeClass YAML generation and priority configuration are its domain,\nnot this skill's.\n\n#### 4.2 Direct Workload Spot Selection (`nodeSelector`)\n\nFor stateless or batch workloads in GKE Autopilot, target Spot capacity directly\nusing `nodeSelector`:\n\n> [!WARNING] **Preemption Warning**: Spot VMs are interruptible and can be\n> preempted at any time with a 30-second notice. Workloads must be\n> fault-tolerant and run with at least 2 replicas for high availability. Always\n> explicitly warn users about this preemption risk when recommending Spot VMs.\n\nThe exact Pod-level selector is:\n\n```yaml\nnodeSelector:\n  cloud.google.com\u002Fgke-spot: \"true\"\n```\n\nFull worked Deployment (replicas >= 2, `terminationGracePeriodSeconds: 25`,\n`preStop` hook): [assets\u002Fspot-deployment-example.yaml](assets\u002Fspot-deployment-example.yaml).\n\n**Spot-Suitable Workloads:**\n\nWorkload                          | Spot-Suitable?\n--------------------------------- | ---------------\nBatch \u002F data processing           | Yes\nDev \u002F test environments           | Yes\nStateless web\u002FAPI (replicas >= 2) | Yes (with PDBs)\nJobs with checkpointing           | Yes\nStateful workloads (databases)    | No\nSingle-replica critical services  | No\n\n### 5. Machine Type Selection\n\nWhen choosing node shapes or configuring ComputeClasses:\n\n| Family        | Use Case                                          | Relative Cost |\n| ------------- | ------------------------------------------------- | ------------- |\n| e2            | General purpose, burstable                        | Lowest        |\n| t2a \u002F t2d     | Scale-out (Arm\u002FAMD), price-performance optimized  | Low           |\n| n4a           | Axion Arm-based, general-purpose price-performance | Low          |\n| n4 \u002F n4d      | General purpose (Intel\u002FAMD), flexible shapes      | Low-Medium    |\n| c4a           | Axion Arm-based, general-purpose, high efficiency | Medium        |\n| c3 \u002F c4       | Compute-optimized (Intel)                         | Medium-High   |\n| c3d \u002F c4d     | Compute-optimized (AMD), high throughput          | Medium-High   |\n| ek-standard   | Autopilot enhanced                                | Medium        |\n| m3 \u002F x4       | Memory-optimized, SAP HANA, large databases       | High          |\n| g2 (L4 GPU)   | AI inference                                      | High          |\n| a3 (H100 GPU) | AI training                                       | Highest       |\n| a4 \u002F a4x      | Ultra-scale AI (Blackwell GPUs)                   | Highest       |\n\n### 6. Committed Use Discounts (CUDs)\n\nFor steady-state workloads with predictable baseline usage, purchase 1-year or\n3-year CUDs:\n\n-   **Resource-based CUDs** (committed to a machine family\u002Fregion): roughly\n    high-30s% discount for 1-year, ~55% for 3-year (varies by machine family).\n-   **Flexible CUDs** (spend-based, portable across families\u002Fregions): lower\n    discounts (~28% 1-year, ~46% 3-year) in exchange for flexibility.\n-   **Autopilot:** Autopilot-specific CUDs were retired in January 2026 — new\n    commitments covering Autopilot usage are spend-based Compute Flexible CUDs\n    (existing Autopilot CUD commitments run out their term).\n-   Applied automatically to matching usage across the region.\n-   Purchase via Google Cloud Console > Billing > Committed use discounts.\n\n**Size the commitment to the steady-state baseline only.** A commitment bills for\nthe full term whether or not you use it, so over-committing to peak usage\nconverts a discount into waste. Measure the floor of actual usage over a\nrepresentative period, commit to that, and cover everything above it with the\nelastic options already in this skill:\n\n-   **Baseline** (always running) → resource-based CUDs.\n-   **Variable \u002F bursty** → autoscaling on on-demand capacity.\n-   **Interruption-tolerant** (batch, CI, stateless workers) → Spot VMs, which\n    stack with autoscaling and need no commitment.\n\nWhen recommending CUDs, state the split explicitly rather than implying the whole\nfootprint should be committed.\n\n### 7. Cluster Management & Multi-Tenancy\n\n-   **Idle dev clusters**: GKE has no stop\u002Fstart operation, and the cluster\n    management fee accrues as long as the cluster exists. To cut idle costs,\n    scale node pools to zero (`gcloud container clusters resize {cluster_name}\n    --node-pool {pool_name} --num-nodes 0`) or delete and recreate the cluster\n    via IaC (Terraform\u002FConfig Connector).\n-   **Right-size node pools (Standard)**: Use Cluster Autoscaler with\n    appropriate min\u002Fmax limits.\n-   **Cheap warm headroom instead of overprovisioned nodes**: Standby capacity\n    buffers (Preview, GKE 1.36.0-gke.2253000+) keep pre-initialized nodes\n    suspended — you pay only disk + IP instead of full node price, with ~30s\n    resume. See the **`gke-cluster-autoscaler`** skill.\n-   **Multi-tenant consolidation**: Share a single cluster across multiple\n    engineering teams instead of maintaining per-team clusters, using Namespaces\n    and ResourceQuotas to isolate workloads.\n\n## Cost & Utilization Monitoring\n\nTo inspect live node\u002Fpod utilization (`kubectl top nodes\u002Fpods`), view cluster\ncost budgets (`gcloud billing budgets list`), or query detailed billing reports\nin BigQuery (`bq query`), refer to the **`gke-cost-analysis`** skill.\n",{"name":5,"metadata":44,"description":7},{"version":45,"category":46},"1.0.1","CloudObservabilityAndMonitoring","main","bb9b3f464aae37af360d079c1d863524955b0363","skills\u002Fcloud\u002Fgke-cost-optimization",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-cost-optimization",{"tool":62,"command":63},"bunx","bunx skills add google\u002Fskills --skill gke-cost-optimization",{"tool":65,"command":66},"pnpm","pnpm dlx skills add google\u002Fskills --skill gke-cost-optimization","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-cost-optimization\u002FSKILL.md",true,[70,76,80,84],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"assets\u002Fresource-quota-example.yaml","ASSET","资产文件",400,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-cost-optimization\u002Fassets\u002Fresource-quota-example.yaml",{"path":77,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":78,"sourceHtmlUrl":79},"assets\u002Fspot-deployment-example.yaml",733,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-cost-optimization\u002Fassets\u002Fspot-deployment-example.yaml",{"path":81,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":82,"sourceHtmlUrl":83},"assets\u002Fvpa-recommendation-mode.yaml",525,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-cost-optimization\u002Fassets\u002Fvpa-recommendation-mode.yaml",{"path":85,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Fattribution.md","REFERENCE","参考文档",1903,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-cost-optimization\u002Freferences\u002Fattribution.md",[91,129,160,189,215,239],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":105,"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",[97,102],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",{"categoryNo":103,"categoryName":104,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000010","后端与接口",[9,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128],"sign in with google web","sign","with","web","android","go","security","google workspace","google cloud","implement","policy","python","implementing","react","iam","permissions","buttons","secure","manager","identity","architectures","domain","runtimes",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":144,"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",[135,136,139],{"categoryNo":103,"categoryName":104,"taxonomyType":100,"taxonomyTypeDesc":101},{"categoryNo":137,"categoryName":138,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":140,"categoryName":141,"taxonomyType":142,"taxonomyTypeDesc":143},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[145,146,9,114,147,148,149,150,151,152,120,153,154,155,156,157,158,125,128,159,24],"dpop adoption","adoption","es6","retry","authentication","flows","challenge","code","browser","loops","at","node","token","pair","platform",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":172,"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",[166,167,170,171],{"categoryNo":103,"categoryName":104,"taxonomyType":100,"taxonomyTypeDesc":101},{"categoryNo":168,"categoryName":169,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":137,"categoryName":138,"taxonomyType":100,"taxonomyTypeDesc":101},{"categoryNo":140,"categoryName":141,"taxonomyType":142,"taxonomyTypeDesc":143},[173,174,175,176,114,177,178,179,180,181,182,183,184,24,185,186,187,120,188,121],"retrieving developer knowledge","knowledge","developer","MCP","mcp server","official","developer documentation","get","search","local","choice","documents","query","api","across","documentation",{"skillNo":190,"name":191,"nameZh":6,"description":192,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":193,"categories":194,"keywords":195,"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",[],[196,197,198,199,25,200,201,202,117,203,204,205,206,207,208,182,209,210,211,24,212,213,187,214],"migrate to rust","rust","migrate","github","performance","directory","safe","ports","llvm","soa","cycle","porting","fix","arrays","infographic","high-performance","URL","architecture","unrelated",{"skillNo":216,"name":217,"nameZh":6,"description":218,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":219,"categories":220,"keywords":224,"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",[221,222,223],{"categoryNo":103,"categoryName":104,"taxonomyType":100,"taxonomyTypeDesc":101},{"categoryNo":137,"categoryName":138,"taxonomyType":100,"taxonomyTypeDesc":101},{"categoryNo":140,"categoryName":141,"taxonomyType":142,"taxonomyTypeDesc":143},[9,225,226,227,228,159,229,230,231,232,233,149,234,235,236,183,37,237,24,238,186],"finding google skills","storage","as","messaging","model","trade-offs","provider","answer","fit","catalog","up","decision","database","candidate",{"skillNo":240,"name":241,"nameZh":6,"description":242,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":243,"categories":244,"keywords":247,"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",[245,246],{"categoryNo":103,"categoryName":104,"taxonomyType":100,"taxonomyTypeDesc":101},{"categoryNo":137,"categoryName":138,"taxonomyType":100,"taxonomyTypeDesc":101},[248,249,124,250,9,176,251,114,177,252,253,254,255,24,256,186,120,257,258,259,260],"workload manager basics","workload","basics","cli","review","vpc","resources","inspect","you","create","libraries","server","run"]