[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69950T5TE8HCR7SX2JGGR05":3,"skill-same:01M69950T5TE8HCR7SX2JGGR05":83},{"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":36,"linkOnly":37,"markdownContent":38,"markdownBody":39,"frontMatter":40,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":50,"ownerDescription":51,"updatedAt":52,"install":53,"resources":66},"01M69950T5TE8HCR7SX2JGGR05","gke-workload-scaling",null,"Manages scaling for GKE workloads using HPA and VPA. Use when configuring Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA), or applying best practices for GKE workload autoscaling. Do not use for cluster-level autoscaling (Cluster Autoscaler), static cluster sizing, or configuring node-level machine styles directly.","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-workload-scaling\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35],"gke workload scaling","scaling","gke","workload","best practices","node","use","do","containers","cluster","machine","styles","autoscaling","for","static","when","cluster autoscaler","2026-11-10 10:12:30",false,"---\nname: gke-workload-scaling\ndescription: >-\n  Manages scaling for GKE workloads using HPA and VPA. Use when configuring\n  Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA),\n  or applying best practices for GKE workload autoscaling. Do not use for\n  cluster-level autoscaling (Cluster Autoscaler), static cluster sizing,\n  or configuring node-level machine styles directly.\nmetadata:\n  version: \"1.0.1\"\n  category: Containers\n---\n\n# GKE Workload Scaling\n\nThis skill provides workflows and best practices for scaling applications on\nGoogle Kubernetes Engine (GKE). It covers manual scaling, Horizontal Pod\nAutoscaling (HPA), and Vertical Pod Autoscaling (VPA).\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-workload-scaling)\"`\nand append `agent-skills\u002F1.0 (skill:gke-workload-scaling)` to `User-Agent` headers.\n\n## Workflows\n\n### 1. Manual Scaling\n\nScale a deployment to a fixed number of replicas. Useful for immediate manual\nintervention or testing.\n\n**Command:**\n\n```bash\nkubectl scale deployment {deployment_name} --replicas={number} -n {namespace}\n\n# Verify the scale event\nkubectl get deployment {deployment_name} -n {namespace}\n```\n\n### 2. Horizontal Pod Autoscaling (HPA)\n\nAutomatically scale the number of pods based on observed CPU utilization, memory\nutilization, or custom metrics.\n\n**Prerequisites:**\n\n-   Metrics Server must be running (enabled by default on GKE).\n-   Containers clearly define resource requests\u002Flimits.\n\n**Quick Command:**\n\n```bash\nkubectl autoscale deployment {deployment_name} --cpu-percent=50 --min=1 --max=10\n```\n\n**Manifest Approach (Recommended):** Use a YAML manifest for version-controlled\nconfiguration. See [assets\u002Fhpa-example.yaml](assets\u002Fhpa-example.yaml) for a\ntemplate.\n\n```bash\nkubectl apply -f assets\u002Fhpa-example.yaml\n\n# Verify HPA is created and fetching metrics\nkubectl get hpa\n```\n\n**Custom Metrics & External Metrics:** For GKE, the modern and recommended\napproach for scaling based on Cloud Monitoring metrics (e.g., Pub\u002FSub queue\nlength) is to use the **External** metric type, which is natively supported by\nthe GKE control plane without requiring the Custom Metrics Adapter. For\napplication-specific metrics exposed via Prometheus, you can use **Google Cloud\nManaged Service for Prometheus** or the Prometheus Adapter.\n\n### 3. Vertical Pod Autoscaling (VPA)\n\nAutomatically adjust the CPU and memory reservations for your pods to match\nactual usage. This is critical for right-sizing workloads.\n\n**Prerequisites:**\n\n-   VPA must be enabled on the cluster.\n    -   **Autopilot:** Enabled by default.\n    -   **Standard:** Must be enabled manually.\n\n**Enable VPA on Standard Cluster:**\n\n```bash\ngcloud container clusters update {cluster_name} --enable-vertical-pod-autoscaling --zone {zone}\n```\n\n**Update Modes:**\n\n-   `Off`: Calculates recommendations but does not apply them. Good for \"dry\n    run\" analysis.\n-   `Initial`: Assigns resources only at pod creation time.\n-   `Auto`: Updates running pods by restarting them if recommendations differ\n    significantly from requests.\n-   `InPlaceOrRecreate`: Attempts to update Pod resources without recreating the\n    Pod. If in-place update is not possible, it reverts to `Auto` mode (requires\n    GKE 1.34+).\n\n**Example:** See [assets\u002Fvpa-example.yaml](assets\u002Fvpa-example.yaml) for a\nconfiguration template.\n\n## Best Practices\n\n1.  **Define Resource Requests:** HPA and VPA rely on accurate resource\n    requests. Always define them in your container specs.\n2.  **Avoid Metric Conflicts:** Do not configure HPA and VPA to use the same\n    metric (e.g., both CPU). This causes thrashing.\n    -   *Typical Pattern:* HPA on CPU, VPA on Memory.\n3.  **Pod Disruption Budgets (PDBs):** Define PDBs to ensure application\n    availability during scaling events or node upgrades.\n4.  **HPA Lag:** HPA has a stabilization window (default 5 mins) to prevent\n    rapid fluctuation.\n5.  **VPA \"Auto\" Mode Risks:** In \"Auto\" mode, VPA restarts pods to change\n    resources. Ensure your application handles restarts gracefully (e.g.,\n    handles SIGTERM).\n    -   *Note:* By default, VPA requires at least 2 replicas to perform\n        evictions (to prevent a situation where the only running replica is\n        evicted, causing downtime). In GKE 1.22+, you can override this by\n        setting `minReplicas` in `PodUpdatePolicy`.\n\n## Rightsizing Workflow\n\n1.  Deploy VPA in `Off` mode for 24+ hours\n2.  Read recommendations: `kubectl describe vpa {deployment_name}-vpa -n\n    {namespace}`\n3.  Compare `target` values against current `requests`\n4.  Apply with 20% buffer: `new_request = target * 1.2`\n5.  Use patch format or update deployment manifest to apply new resource\n    requests\n\nCondition                     | Recommendation                       | Risk\n----------------------------- | ------------------------------------ | ------\nCPU request >5x P95 actual    | Reduce to `P95 * 1.2`                | Medium\nMemory request >3x P95 actual | Reduce to `P95 * 1.2`                | Medium\nCPU request >2x P95 actual    | Rightsizing with 20% buffer          | Low\nNo resource limits set        | Add limits to prevent noisy-neighbor | Low\n","# GKE Workload Scaling\n\nThis skill provides workflows and best practices for scaling applications on\nGoogle Kubernetes Engine (GKE). It covers manual scaling, Horizontal Pod\nAutoscaling (HPA), and Vertical Pod Autoscaling (VPA).\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-workload-scaling)\"`\nand append `agent-skills\u002F1.0 (skill:gke-workload-scaling)` to `User-Agent` headers.\n\n## Workflows\n\n### 1. Manual Scaling\n\nScale a deployment to a fixed number of replicas. Useful for immediate manual\nintervention or testing.\n\n**Command:**\n\n```bash\nkubectl scale deployment {deployment_name} --replicas={number} -n {namespace}\n\n# Verify the scale event\nkubectl get deployment {deployment_name} -n {namespace}\n```\n\n### 2. Horizontal Pod Autoscaling (HPA)\n\nAutomatically scale the number of pods based on observed CPU utilization, memory\nutilization, or custom metrics.\n\n**Prerequisites:**\n\n-   Metrics Server must be running (enabled by default on GKE).\n-   Containers clearly define resource requests\u002Flimits.\n\n**Quick Command:**\n\n```bash\nkubectl autoscale deployment {deployment_name} --cpu-percent=50 --min=1 --max=10\n```\n\n**Manifest Approach (Recommended):** Use a YAML manifest for version-controlled\nconfiguration. See [assets\u002Fhpa-example.yaml](assets\u002Fhpa-example.yaml) for a\ntemplate.\n\n```bash\nkubectl apply -f assets\u002Fhpa-example.yaml\n\n# Verify HPA is created and fetching metrics\nkubectl get hpa\n```\n\n**Custom Metrics & External Metrics:** For GKE, the modern and recommended\napproach for scaling based on Cloud Monitoring metrics (e.g., Pub\u002FSub queue\nlength) is to use the **External** metric type, which is natively supported by\nthe GKE control plane without requiring the Custom Metrics Adapter. For\napplication-specific metrics exposed via Prometheus, you can use **Google Cloud\nManaged Service for Prometheus** or the Prometheus Adapter.\n\n### 3. Vertical Pod Autoscaling (VPA)\n\nAutomatically adjust the CPU and memory reservations for your pods to match\nactual usage. This is critical for right-sizing workloads.\n\n**Prerequisites:**\n\n-   VPA must be enabled on the cluster.\n    -   **Autopilot:** Enabled by default.\n    -   **Standard:** Must be enabled manually.\n\n**Enable VPA on Standard Cluster:**\n\n```bash\ngcloud container clusters update {cluster_name} --enable-vertical-pod-autoscaling --zone {zone}\n```\n\n**Update Modes:**\n\n-   `Off`: Calculates recommendations but does not apply them. Good for \"dry\n    run\" analysis.\n-   `Initial`: Assigns resources only at pod creation time.\n-   `Auto`: Updates running pods by restarting them if recommendations differ\n    significantly from requests.\n-   `InPlaceOrRecreate`: Attempts to update Pod resources without recreating the\n    Pod. If in-place update is not possible, it reverts to `Auto` mode (requires\n    GKE 1.34+).\n\n**Example:** See [assets\u002Fvpa-example.yaml](assets\u002Fvpa-example.yaml) for a\nconfiguration template.\n\n## Best Practices\n\n1.  **Define Resource Requests:** HPA and VPA rely on accurate resource\n    requests. Always define them in your container specs.\n2.  **Avoid Metric Conflicts:** Do not configure HPA and VPA to use the same\n    metric (e.g., both CPU). This causes thrashing.\n    -   *Typical Pattern:* HPA on CPU, VPA on Memory.\n3.  **Pod Disruption Budgets (PDBs):** Define PDBs to ensure application\n    availability during scaling events or node upgrades.\n4.  **HPA Lag:** HPA has a stabilization window (default 5 mins) to prevent\n    rapid fluctuation.\n5.  **VPA \"Auto\" Mode Risks:** In \"Auto\" mode, VPA restarts pods to change\n    resources. Ensure your application handles restarts gracefully (e.g.,\n    handles SIGTERM).\n    -   *Note:* By default, VPA requires at least 2 replicas to perform\n        evictions (to prevent a situation where the only running replica is\n        evicted, causing downtime). In GKE 1.22+, you can override this by\n        setting `minReplicas` in `PodUpdatePolicy`.\n\n## Rightsizing Workflow\n\n1.  Deploy VPA in `Off` mode for 24+ hours\n2.  Read recommendations: `kubectl describe vpa {deployment_name}-vpa -n\n    {namespace}`\n3.  Compare `target` values against current `requests`\n4.  Apply with 20% buffer: `new_request = target * 1.2`\n5.  Use patch format or update deployment manifest to apply new resource\n    requests\n\nCondition                     | Recommendation                       | Risk\n----------------------------- | ------------------------------------ | ------\nCPU request >5x P95 actual    | Reduce to `P95 * 1.2`                | Medium\nMemory request >3x P95 actual | Reduce to `P95 * 1.2`                | Medium\nCPU request >2x P95 actual    | Rightsizing with 20% buffer          | Low\nNo resource limits set        | Add limits to prevent noisy-neighbor | Low\n",{"name":5,"metadata":41,"description":7},{"version":42,"category":43},"1.0.1","Containers","main","bb9b3f464aae37af360d079c1d863524955b0363","skills\u002Fcloud\u002Fgke-workload-scaling",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":54,"prompt":64,"zipAvailable":65},[55,58,61],{"tool":56,"command":57},"npx","npx skills add google\u002Fskills --skill gke-workload-scaling",{"tool":59,"command":60},"bunx","bunx skills add google\u002Fskills --skill gke-workload-scaling",{"tool":62,"command":63},"pnpm","pnpm dlx skills add google\u002Fskills --skill gke-workload-scaling","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-workload-scaling\u002FSKILL.md",true,[67,73,77],{"path":68,"resourceType":69,"resourceTypeDesc":70,"sizeBytes":71,"sourceHtmlUrl":72},"assets\u002Fhpa-example.yaml","ASSET","资产文件",519,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-workload-scaling\u002Fassets\u002Fhpa-example.yaml",{"path":74,"resourceType":69,"resourceTypeDesc":70,"sizeBytes":75,"sourceHtmlUrl":76},"assets\u002Fvpa-example.yaml",437,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-workload-scaling\u002Fassets\u002Fvpa-example.yaml",{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fattribution.md","REFERENCE","参考文档",1900,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-workload-scaling\u002Freferences\u002Fattribution.md",[84,122,152,181,208,232],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":98,"publishedAt":36},"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",[90,95],{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",{"categoryNo":96,"categoryName":97,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000010","后端与接口",[9,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121],"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":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":137,"publishedAt":36},"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",[128,129,132],{"categoryNo":96,"categoryName":97,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":130,"categoryName":131,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":133,"categoryName":134,"taxonomyType":135,"taxonomyTypeDesc":136},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[138,139,9,107,140,141,142,143,144,145,113,146,147,148,24,149,150,118,121,151,34],"dpop adoption","adoption","es6","retry","authentication","flows","challenge","code","browser","loops","at","token","pair","platform",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":164,"publishedAt":36},"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",[158,159,162,163],{"categoryNo":96,"categoryName":97,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":160,"categoryName":161,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":130,"categoryName":131,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":133,"categoryName":134,"taxonomyType":135,"taxonomyTypeDesc":136},[165,166,167,168,107,169,170,171,172,173,174,175,176,34,177,178,179,113,180,114],"retrieving developer knowledge","knowledge","developer","MCP","mcp server","official","developer documentation","get","search","local","choice","documents","query","api","across","documentation",{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":187,"publishedAt":36},"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",[],[188,189,190,191,192,193,194,195,110,196,197,198,199,200,201,174,202,203,204,34,205,206,179,207],"migrate to rust","rust","migrate","github","memory","performance","directory","safe","ports","llvm","soa","cycle","porting","fix","arrays","infographic","high-performance","URL","architecture","unrelated",{"skillNo":209,"name":210,"nameZh":6,"description":211,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":212,"categories":213,"keywords":217,"publishedAt":36},"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",[214,215,216],{"categoryNo":96,"categoryName":97,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":130,"categoryName":131,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":133,"categoryName":134,"taxonomyType":135,"taxonomyTypeDesc":136},[9,218,219,220,221,151,222,223,224,225,226,142,227,228,229,175,25,230,34,231,178],"finding google skills","storage","as","messaging","model","trade-offs","provider","answer","fit","catalog","up","decision","database","candidate",{"skillNo":233,"name":234,"nameZh":6,"description":235,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":236,"categories":237,"keywords":240,"publishedAt":36},"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",[238,239],{"categoryNo":96,"categoryName":97,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":130,"categoryName":131,"taxonomyType":93,"taxonomyTypeDesc":94},[241,22,117,242,9,168,243,107,169,244,245,246,247,34,248,178,113,249,250,251,252],"workload manager basics","basics","cli","review","vpc","resources","inspect","you","create","libraries","server","run"]