[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6993AF0E61B2KXCBCFNC27P":3,"skill-same:01M6993AF0E61B2KXCBCFNC27P":91},{"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":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":6,"ownerHtmlUrl":57,"ownerBlogUrl":58,"ownerDescription":59,"updatedAt":60,"install":61,"resources":74},"01M6993AF0E61B2KXCBCFNC27P","gke-ai-troubleshooting-tpu-vbar-oom",null,"Diagnoses and prevents vbar_control_agent segfaults, out-of-memory (OOM) errors, and TPU device initialization failures on TPU v6e nodes in GKE caused by race conditions during TPU device resets or high-frequency metrics polling. Use when troubleshooting vbar_control_agent crashes, memory cgroup OOMs in serial console logs, tpu-device-plugin metrics checksum corruption errors, or custom TPU metrics collection conflicts on GKE TPU v6e nodes. Don't use for general non-TPU container OOM troubleshooting or standard GKE node lifecycle operations.","google\u002Fskills","google","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F1342004?v=4","01M698VYEFDM0VJBZDYYJ2T83S","Google","01M698WR0H76ENHE587T9EJT89",23830,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-ai-troubleshooting-tpu-vbar-oom\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"gke ai troubleshooting tpu vbar oom","tpu","gke","troubleshooting","oom","when","memory","custom","initialization","for","errors","conflicts","general","container","use","node","collection","lifecycle","logs","metrics","2026-11-10 10:12:30",false,"---\nname: gke-ai-troubleshooting-tpu-vbar-oom\ndescription: >-\n  Diagnoses and prevents vbar_control_agent segfaults, out-of-memory (OOM) errors,\n  and TPU device initialization failures on TPU v6e nodes in GKE caused by race\n  conditions during TPU device resets or high-frequency metrics polling. Use when\n  troubleshooting vbar_control_agent crashes, memory cgroup OOMs in serial console\n  logs, tpu-device-plugin metrics checksum corruption errors, or custom TPU metrics\n  collection conflicts on GKE TPU v6e nodes. Don't use for general non-TPU container\n  OOM troubleshooting or standard GKE node lifecycle operations.\nmetadata:\n  version: \"1.0.1\"\n  category: CloudObservabilityAndMonitoring\n---\n\n# TPU Connection Failure and VBAR OOM Troubleshooting\n\nUse this skill to systematically diagnose and prevent `vbar_control_agent`\nsegfaults and Out-Of-Memory (OOM) errors on TPU v6e nodes.\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-ai-troubleshooting-tpu-vbar-oom)\"`\nand append `agent-skills\u002F1.0 (skill:gke-ai-troubleshooting-tpu-vbar-oom)` to `User-Agent` headers.\n\n## ⚠️ Prerequisites\n\n-   Cloud Logging must be enabled for the project.\n-   Access to the project and cluster via `gcloud` or equivalent tool.\n\n## ? Diagnostic Workflow\n\n### Step 0: Context Acquisition & Time Window Definition\n\nIndependently gather required context using available GCP\u002FGKE tools or use the\nprovided `{variable}` placeholders:\n\n-   `{project_id}`: The GCP Project ID (e.g., `customer-ai-project-123`).\n-   `{cluster_name}`: The GKE Cluster Name (e.g., `tpu-cluster-prod`).\n-   `{node_name}`: The Node Name or Instance ID (e.g., `tpu-node-1`).\n-   `{workload_name}`: The Workload Name \u002F JobSet Name (e.g.,\n    `my-training-job-456`).\n-   `{namespace}`: The Workload Namespace.\n-   `{issue_time}`: The timestamp of the issue (e.g., `2026-04-14T20:00:00Z`).\n\n#### Time Handling & Execution Rules\n\n1.  **Window Calculation**: If an issue timestamp `{issue_time}` is provided,\n    calculate the query time window as `[{issue_time} - 30m]` to\n    `[{issue_time} + 30m]`.\n    -   Let `{start_time}` = `{issue_time} - 30m`\n    -   Let `{end_time}` = `{issue_time} + 30m`\n2.  **Informational vs. Live Execution**: If the user request is informational\n    or query-formulation (e.g. \"How can I check...\", \"How do I determine...\"),\n    or if live GCP project resources are not actively targetable, directly\n    output the calculated time window, log names, and Cloud Logging filter\n    templates without attempting live log execution commands.\n\n### Step 1: Check for `vbar_control_agent` OOMs\n\nLook for specific `out of memory` messages from `vbar_control_agent` in serial\nconsole logs (`serialconsole.googleapis.com%2fserial_port_1_output`).\n\n-   **Tool to use**: `query_logs` (for live diagnostics)\n-   **Filter Templates**:\n\n**Serial Console Logs (OOMs):**\n\n```sql\nlogName=\"projects\u002F{project_id}\u002Flogs\u002Fserialconsole.googleapis.com%2fserial_port_1_output\"\nAND labels.\"compute.googleapis.com\u002Fresource_name\"=\"{node_name}\"\nAND SEARCH(text_payload, \"Memory cgroup out of memory: Killed process .* (vbar_control_ag)\")\nAND timestamp >= \"{start_time}\"\nAND timestamp \u003C= \"{end_time}\"\n```\n\n-   **Logic**: Presence of `Memory cgroup out of memory` messages related to\n    `vbar_control_agent`. Stack traces pointing to\n    `libtpu::tpunetd::VBARControlHelper::MetricsReadFromVBAR` are a strong\n    indicator.\n-   **Automation**: Proceed to next step automatically after reporting findings.\n-   **Reference**: See `references\u002Ffailure_signatures.md` for example log\n    patterns.\n\n### Step 2: Investigate `tpu-device-plugin` Metrics Fetch Failures [Low Risk]\n\nCheck if `tpu-device-plugin` is reporting metric fetch failures.\n\n-   **Tool to use**: `query_logs`\n-   **Filter Template**:\n\n```sql\nresource.type=\"k8s_container\"\nAND resource.labels.project_id=\"{project_id}\"\nAND resource.labels.cluster_name=\"{cluster_name}\"\nAND resource.labels.container_name=\"tpu-device-plugin\"\nAND severity=ERROR\nAND textPayload:\"metrics fetch failed for .* deviceID and .* device path with error: checksum didn't match with the metrics data. Corrupt data found\"\nAND timestamp >= \"{start_time}\"\nAND timestamp \u003C= \"{end_time}\"\n```\n\n-   **Logic**: Errors indicating \"metrics fetch failed\" with \"checksum didn't\n    match\" suggest vBAR memory corruption.\n-   **Automation**: Proceed to next step automatically after reporting findings.\n\n### Step 3: Check for Custom Metrics Collection Usage [Low Risk]\n\nInspect cluster configurations, workloads, or container specs to determine if\ncustom TPU metrics collection mechanisms are deployed.\n\n-   **Action**: Check if custom scripts or agents (e.g., using\n    `libtpu.sdk.tpumonitoring`) are deployed that frequently query\n    `GetHostMetrics` from `vBAR Control Agent`.\n-   **Verification Commands**:\n\n    -   **Kubectl Search (Inspect workload env\u002Fspecs)**:\n\n    ```bash\n    kubectl get pods -A -o jsonpath='{range .items[*]}{.metadata.namespace}{\"\u002F\"}{.metadata.name}{\"\\t\"}{.spec.containers[*].image}{\"\\n\"}{end}'\n    ```\n\n    -   **Log Search Filter (`query_logs`)**:\n\n    ```sql\n    resource.type=\"k8s_container\"\n    AND resource.labels.project_id=\"{project_id}\"\n    AND resource.labels.cluster_name=\"{cluster_name}\"\n    AND textPayload:\"libtpu.sdk.tpumonitoring\"\n    AND timestamp >= \"{start_time}\"\n    AND timestamp \u003C= \"{end_time}\"\n    ```\n\n-   **Logic**: Confirmation of custom metrics collection helps confirm the race\n    condition hypothesis.\n\n## ?️ Resolution Workflow\n\n### Resolution 1: Temporarily Disable Custom Metrics Collection [High Risk]\n\nIf a custom metrics collection agent is identified, recommend disabling it.\n\n-   **Action**: Recommend disabling the custom metrics collector.\n-   **Justification**: Prevents reads from vBAR during device resets, stopping\n    crashes and OOMs.\n\n### Resolution 2: Await `vbar_control_agent` Resiliency Update [Low Risk]\n\nAdvise that a permanent fix will be available in a future GKE version.\n\n-   **Action**: Recommend upgrading GKE when the fix is available.\n-   **Justification**: The updated agent will be resilient to memory corruption\n    and gracefully handle reads from unbound vBARs.\n\n## ? copypaste checklist\n\n-   [ ] Acquire context and compute `[{start_time}, {end_time}]` window.\n-   [ ] Check for `vbar_control_agent` segfaults and OOMs using `query_logs`.\n-   [ ] Investigate `tpu-device-plugin` failures using `query_logs`.\n-   [ ] Inspect for custom metrics collection usage.\n-   [ ] Advise disabling custom metrics collection if applicable.\n-   [ ] Advise awaiting resiliency update.\n","# TPU Connection Failure and VBAR OOM Troubleshooting\n\nUse this skill to systematically diagnose and prevent `vbar_control_agent`\nsegfaults and Out-Of-Memory (OOM) errors on TPU v6e nodes.\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-ai-troubleshooting-tpu-vbar-oom)\"`\nand append `agent-skills\u002F1.0 (skill:gke-ai-troubleshooting-tpu-vbar-oom)` to `User-Agent` headers.\n\n## ⚠️ Prerequisites\n\n-   Cloud Logging must be enabled for the project.\n-   Access to the project and cluster via `gcloud` or equivalent tool.\n\n## ? Diagnostic Workflow\n\n### Step 0: Context Acquisition & Time Window Definition\n\nIndependently gather required context using available GCP\u002FGKE tools or use the\nprovided `{variable}` placeholders:\n\n-   `{project_id}`: The GCP Project ID (e.g., `customer-ai-project-123`).\n-   `{cluster_name}`: The GKE Cluster Name (e.g., `tpu-cluster-prod`).\n-   `{node_name}`: The Node Name or Instance ID (e.g., `tpu-node-1`).\n-   `{workload_name}`: The Workload Name \u002F JobSet Name (e.g.,\n    `my-training-job-456`).\n-   `{namespace}`: The Workload Namespace.\n-   `{issue_time}`: The timestamp of the issue (e.g., `2026-04-14T20:00:00Z`).\n\n#### Time Handling & Execution Rules\n\n1.  **Window Calculation**: If an issue timestamp `{issue_time}` is provided,\n    calculate the query time window as `[{issue_time} - 30m]` to\n    `[{issue_time} + 30m]`.\n    -   Let `{start_time}` = `{issue_time} - 30m`\n    -   Let `{end_time}` = `{issue_time} + 30m`\n2.  **Informational vs. Live Execution**: If the user request is informational\n    or query-formulation (e.g. \"How can I check...\", \"How do I determine...\"),\n    or if live GCP project resources are not actively targetable, directly\n    output the calculated time window, log names, and Cloud Logging filter\n    templates without attempting live log execution commands.\n\n### Step 1: Check for `vbar_control_agent` OOMs\n\nLook for specific `out of memory` messages from `vbar_control_agent` in serial\nconsole logs (`serialconsole.googleapis.com%2fserial_port_1_output`).\n\n-   **Tool to use**: `query_logs` (for live diagnostics)\n-   **Filter Templates**:\n\n**Serial Console Logs (OOMs):**\n\n```sql\nlogName=\"projects\u002F{project_id}\u002Flogs\u002Fserialconsole.googleapis.com%2fserial_port_1_output\"\nAND labels.\"compute.googleapis.com\u002Fresource_name\"=\"{node_name}\"\nAND SEARCH(text_payload, \"Memory cgroup out of memory: Killed process .* (vbar_control_ag)\")\nAND timestamp >= \"{start_time}\"\nAND timestamp \u003C= \"{end_time}\"\n```\n\n-   **Logic**: Presence of `Memory cgroup out of memory` messages related to\n    `vbar_control_agent`. Stack traces pointing to\n    `libtpu::tpunetd::VBARControlHelper::MetricsReadFromVBAR` are a strong\n    indicator.\n-   **Automation**: Proceed to next step automatically after reporting findings.\n-   **Reference**: See `references\u002Ffailure_signatures.md` for example log\n    patterns.\n\n### Step 2: Investigate `tpu-device-plugin` Metrics Fetch Failures [Low Risk]\n\nCheck if `tpu-device-plugin` is reporting metric fetch failures.\n\n-   **Tool to use**: `query_logs`\n-   **Filter Template**:\n\n```sql\nresource.type=\"k8s_container\"\nAND resource.labels.project_id=\"{project_id}\"\nAND resource.labels.cluster_name=\"{cluster_name}\"\nAND resource.labels.container_name=\"tpu-device-plugin\"\nAND severity=ERROR\nAND textPayload:\"metrics fetch failed for .* deviceID and .* device path with error: checksum didn't match with the metrics data. Corrupt data found\"\nAND timestamp >= \"{start_time}\"\nAND timestamp \u003C= \"{end_time}\"\n```\n\n-   **Logic**: Errors indicating \"metrics fetch failed\" with \"checksum didn't\n    match\" suggest vBAR memory corruption.\n-   **Automation**: Proceed to next step automatically after reporting findings.\n\n### Step 3: Check for Custom Metrics Collection Usage [Low Risk]\n\nInspect cluster configurations, workloads, or container specs to determine if\ncustom TPU metrics collection mechanisms are deployed.\n\n-   **Action**: Check if custom scripts or agents (e.g., using\n    `libtpu.sdk.tpumonitoring`) are deployed that frequently query\n    `GetHostMetrics` from `vBAR Control Agent`.\n-   **Verification Commands**:\n\n    -   **Kubectl Search (Inspect workload env\u002Fspecs)**:\n\n    ```bash\n    kubectl get pods -A -o jsonpath='{range .items[*]}{.metadata.namespace}{\"\u002F\"}{.metadata.name}{\"\\t\"}{.spec.containers[*].image}{\"\\n\"}{end}'\n    ```\n\n    -   **Log Search Filter (`query_logs`)**:\n\n    ```sql\n    resource.type=\"k8s_container\"\n    AND resource.labels.project_id=\"{project_id}\"\n    AND resource.labels.cluster_name=\"{cluster_name}\"\n    AND textPayload:\"libtpu.sdk.tpumonitoring\"\n    AND timestamp >= \"{start_time}\"\n    AND timestamp \u003C= \"{end_time}\"\n    ```\n\n-   **Logic**: Confirmation of custom metrics collection helps confirm the race\n    condition hypothesis.\n\n## ?️ Resolution Workflow\n\n### Resolution 1: Temporarily Disable Custom Metrics Collection [High Risk]\n\nIf a custom metrics collection agent is identified, recommend disabling it.\n\n-   **Action**: Recommend disabling the custom metrics collector.\n-   **Justification**: Prevents reads from vBAR during device resets, stopping\n    crashes and OOMs.\n\n### Resolution 2: Await `vbar_control_agent` Resiliency Update [Low Risk]\n\nAdvise that a permanent fix will be available in a future GKE version.\n\n-   **Action**: Recommend upgrading GKE when the fix is available.\n-   **Justification**: The updated agent will be resilient to memory corruption\n    and gracefully handle reads from unbound vBARs.\n\n## ? copypaste checklist\n\n-   [ ] Acquire context and compute `[{start_time}, {end_time}]` window.\n-   [ ] Check for `vbar_control_agent` segfaults and OOMs using `query_logs`.\n-   [ ] Investigate `tpu-device-plugin` failures using `query_logs`.\n-   [ ] Inspect for custom metrics collection usage.\n-   [ ] Advise disabling custom metrics collection if applicable.\n-   [ ] Advise awaiting resiliency update.\n",{"name":5,"metadata":49,"description":7},{"version":50,"category":51},"1.0.1","CloudObservabilityAndMonitoring","main","bb9b3f464aae37af360d079c1d863524955b0363","skills\u002Fcloud\u002Fgke-ai-troubleshooting-tpu-vbar-oom",1991,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills","https:\u002F\u002Fgithub.com\u002Fgoogle","https:\u002F\u002Fopensource.google\u002F","Google ❤️ Open Source","2026-11-11 03:08:50",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add google\u002Fskills --skill gke-ai-troubleshooting-tpu-vbar-oom",{"tool":67,"command":68},"bunx","bunx skills add google\u002Fskills --skill gke-ai-troubleshooting-tpu-vbar-oom",{"tool":70,"command":71},"pnpm","pnpm dlx skills add google\u002Fskills --skill gke-ai-troubleshooting-tpu-vbar-oom","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-ai-troubleshooting-tpu-vbar-oom\u002FSKILL.md",true,[75,81,85],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fattribution.md","REFERENCE","参考文档",1945,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-ai-troubleshooting-tpu-vbar-oom\u002Freferences\u002Fattribution.md",{"path":82,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Ffailure_signatures.md",1384,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-ai-troubleshooting-tpu-vbar-oom\u002Freferences\u002Ffailure_signatures.md",{"path":86,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"scripts\u002Fvalidate_queries.sh","SCRIPT","脚本",993,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-ai-troubleshooting-tpu-vbar-oom\u002Fscripts\u002Fvalidate_queries.sh",[92,128,156,185,211,235],{"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":104,"publishedAt":44},"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",[98,101],{"categoryNo":99,"categoryName":100,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":102,"categoryName":103,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[9,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127],"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":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":141,"publishedAt":44},"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",[134,135,136],{"categoryNo":102,"categoryName":103,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":137,"categoryName":138,"taxonomyType":139,"taxonomyTypeDesc":140},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[142,143,9,113,144,145,146,147,148,149,119,150,151,152,39,153,154,124,127,155,29],"dpop adoption","adoption","es6","retry","authentication","flows","challenge","code","browser","loops","at","token","pair","platform",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":168,"publishedAt":44},"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",[162,163,166,167],{"categoryNo":102,"categoryName":103,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":164,"categoryName":165,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":137,"categoryName":138,"taxonomyType":139,"taxonomyTypeDesc":140},[169,170,171,172,113,173,174,175,176,177,178,179,180,29,181,182,183,119,184,120],"retrieving developer knowledge","knowledge","developer","MCP","mcp server","official","developer documentation","get","search","local","choice","documents","query","api","across","documentation",{"skillNo":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":189,"categories":190,"keywords":191,"publishedAt":44},"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",[],[192,193,194,195,30,196,197,198,116,199,200,201,202,203,204,178,205,206,207,29,208,209,183,210],"migrate to rust","rust","migrate","github","performance","directory","safe","ports","llvm","soa","cycle","porting","fix","arrays","infographic","high-performance","URL","architecture","unrelated",{"skillNo":212,"name":213,"nameZh":6,"description":214,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":215,"categories":216,"keywords":220,"publishedAt":44},"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",[217,218,219],{"categoryNo":102,"categoryName":103,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":137,"categoryName":138,"taxonomyType":139,"taxonomyTypeDesc":140},[9,221,222,223,224,155,225,226,227,228,229,146,230,231,232,179,38,233,29,234,182],"finding google skills","storage","as","messaging","model","trade-offs","provider","answer","fit","catalog","up","decision","database","candidate",{"skillNo":236,"name":237,"nameZh":6,"description":238,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":239,"categories":240,"keywords":243,"publishedAt":44},"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",[241,242],{"categoryNo":102,"categoryName":103,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[244,245,123,246,9,172,247,113,173,248,249,250,251,29,252,182,119,253,254,255,256],"workload manager basics","workload","basics","cli","review","vpc","resources","inspect","you","create","libraries","server","run"]