[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6HD54EGTR36Y7S2KCY83Y9T":3,"skill-same:01M6HD54EGTR36Y7S2KCY83Y9T":250},{"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":26,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":63,"commitSha":64,"skillDirectory":65,"forksCount":66,"repositoryHtmlUrl":67,"repositoryHomepageUrl":6,"ownerHtmlUrl":68,"ownerBlogUrl":69,"ownerDescription":70,"updatedAt":71,"install":72,"resources":85},"01M6HD54EGTR36Y7S2KCY83Y9T","cloud-monitoring-alert-configuration",null,"Configures Cloud Monitoring alerting policies using Terraform and PromQL across Cloud Run, Google Kubernetes Engine (GKE), Compute Engine, AI Hypercomputer reservations, Vertex AI Agent Engine, and generic Google Cloud resources. Use when creating, reviewing, migrating, or deduplicating Terraform alerting policies for latency, traffic, error rates, saturation, host or reservation maintenance, GPU XID insights, or AI agent telemetry. Don't use for standalone PromQL query authoring without alerting, log querying, or billing cost audits.","google\u002Fskills","google","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F1342004?v=4","01M698VYEFDM0VJBZDYYJ2T83S","Google","01M698WR0H76ENHE587T9EJT89",24044,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fcloud-monitoring-alert-configuration\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"cloud monitoring alert configuration","monitoring","cloud","alert","configuration","cloud monitoring","cloud monitoring alert","host","when","promql","standalone","traffic","insights","billing","error","resources","google cloud","query","saturation","alerting","ai agent","2026-11-10 10:12:30",false,"---\nname: cloud-monitoring-alert-configuration\nmetadata:\n  version: \"1.0.2\"\n  category: CloudObservabilityAndMonitoring\n  support_tier: team\n  tags: [cloud-monitoring, alerting, terraform, promql, observability]\n  comment: >-\n    Consolidated from Cloud Monitoring alerting policy best practices across\n    Cloud Run, Google Kubernetes Engine, Compute Engine, and Vertex AI Agent\n    Engine.\n  canonical_source: https:\u002F\u002Fdocs.cloud.google.com\u002Fmonitoring\u002Falerts.md.txt\ndescription: >-\n  Configures Cloud Monitoring alerting policies using Terraform and\n  PromQL across Cloud Run, Google Kubernetes Engine (GKE), Compute Engine, AI\n  Hypercomputer reservations, Vertex AI Agent Engine, and generic Google Cloud\n  resources. Use when creating, reviewing, migrating, or deduplicating Terraform\n  alerting policies for latency, traffic, error rates, saturation, host or\n  reservation maintenance, GPU XID insights, or AI agent telemetry. Don't use\n  for standalone PromQL query authoring without alerting, log querying, or\n  billing cost audits.\n---\n\n# Configure Cloud Monitoring Alerts\n\nConfigure high-signal alerting policies in Cloud Monitoring using\nTerraform (`google_monitoring_alert_policy`) and PromQL\n(`condition_prometheus_query_language`).\n\n## Usage Attribution\n\nFor all `gcloud` CLI, REST API, and MCP attribution requirements when executing\ncommands with this skill, follow the instructions in\n[Usage Attribution](references\u002Fattribution.md).\n\n## Enforce Core Alerting Rules\n\n1.  **Apply the Four Golden Signals**: Cover latency, traffic, errors, and\n    saturation for the target workload.\n2.  **Use Dynamic Grouping**: Aggregate metrics by resource labels and\n    parameterize thresholds with Terraform variables. Never hardcode ephemeral\n    pod or instance identifiers.\n3.  **Default to PromQL**: Use Prometheus Query Language (PromQL) for metric\n    conditions, and use log-based alerts\n    (`condition_matched_log` or `google_logging_metric`) for lifecycle and\n    audit events.\n4.  **Attach Skill Attribution Labels**: Always include `user_labels` with\n    `created-with-google-skill = \"cloud-monitoring-alert-configuration\"` on\n    every generated `google_monitoring_alert_policy` resource.\n5.  **Require User Confirmation Before Mutating Resources**: Never execute\n    commands that modify infrastructure, enable telemetry, create online\n    monitors, or apply Terraform without waiting for explicit user confirmation.\n6.  **Prompt for Notification Channels**: Alerting policies without notification\n    channels incur charges without notifying anyone during an incident. Whenever\n    notification channels are not specified in the user prompt across any\n    workflow, explicitly inform the user that no notification channels are\n    attached to the alerting policies, advise them to configure and attach\n    notification channels before deploying, and ask at the end of your response\n    if they want to configure notification channels.\n\n## Route by Target Resource Type\n\nIdentify the target resource type and read only the matching reference file\ndirectly. Never run directory listings or workspace file searches to look for\nqueries or `.tf` files unless the prompt\nexplicitly provides a file path. If a user asks to fix or validate a PromQL\nquery without providing the query text or a file path, construct a\nrepresentative broken-and-corrected query example directly.\n\n### Configure Cloud Run Alerts\n\n*   **HTTP Services**:\n    [cloud_run_services.md](references\u002Fcloud_run_services.md)\n*   **Batch Jobs**:\n    [cloud_run_jobs.md](references\u002Fcloud_run_jobs.md)\n*   **Worker Pools**:\n    [cloud_run_worker_pools.md](references\u002Fcloud_run_worker_pools.md)\n\n### Configure Google Kubernetes Engine Alerts\n\n*   **PromQL Queries and Golden Signals**:\n    [GKE PromQL Queries](references\u002Fkubernetes_promql_queries.md)\n*   **Metrics Catalog and Open-Source Alerts**:\n    [GKE Metrics](references\u002Fkubernetes_metrics_and_alerts_catalog.md)\n*   **Prerequisites and Setup**:\n    [GKE Setup](references\u002Fkubernetes_gke_configuration_prerequisites.md)\n\n### Configure AI Agent Alerts\n\n*   **Reliability**:\n    [Reliability](references\u002Fagent_platform_reliability_alert_policies.md)\n*   **Quality**:\n    [Quality](references\u002Fagent_platform_quality_alert_policies.md)\n*   **Cost**:\n    [Cost](references\u002Fagent_platform_cost_alert_policies.md)\n*   **Safety**:\n    [Safety](references\u002Fagent_platform_safety_alert_policies.md)\n*   **Security**:\n    [Security](references\u002Fagent_platform_security_alert_policies.md)\n*   **Telemetry Enablement**:\n    [Telemetry](references\u002Fagent_platform_telemetry_enablement.md)\n*   **Traffic Profiles**:\n    [Historical](references\u002Fagent_platform_has_historical_traffic_data.md)\n    and\n    [No History](references\u002Fagent_platform_no_historical_traffic_data.md)\n\n### Configure Compute Engine and AI Hypercomputer Alerts\n\n*   **Compute Engine VMs, MIGs, GPU XID Insights, Host or Reservation\n    Maintenance (`type=scheduled`, `unscheduled`, or `multiple`), and Machine\n    or Domain Health**:\n    [compute_engine_alerts.md](references\u002Fcompute_engine_alerts.md)\n\n### Configure Generic Resource Alerts\n\n*   **Generic Workloads, Custom Metrics, MQL Migration, and HCL Deduplication**:\n    [generic_alert_policies.md](references\u002Fgeneric_alert_policies.md)\n\n### Delegate Service Level Objective Alerts\n\n**Delegate to the `google-cloud-slo-alert-configuration` skill.** That skill\nowns the SLO setup wizard, the burn-rate PromQL templates\n(`references\u002Fpromql_templates.md`), the SLI metric mapping\n(`references\u002Fservice_metrics.md`), and the App Hub scoping labels\n(`references\u002Fapp_hub_labels.md`). Do **NOT** duplicate those templates here or\nclaim that this skill owns its own SLO PromQL template library.\n\nDelegate whenever the user asks for an **availability or latency SLO**, an\n**error budget or burn-rate alert**, or a **Service Level Objective** on any\nresource. When SLOs are only one part of a broader request (for example, a full\nproduction alerting suite for a Cloud Run service), delegate the SLO policies\nand continue generating the remaining golden-signal policies from the\nresource-specific reference in the same response.\n\nEvery resource reference carries a **Context to hand off** list naming the SLI\nmetric, its good or bad filter, and the grouping labels — under a `## Delegate\nService Level Objective Alerts` heading for Cloud Run and generic resources, and\ninside the SLO section itself for GKE and Agent Platform. Read it and pass that\ncontext on, so the SLO skill does not repeat discovery you have already done.\nWhen delegating, you MUST also:\n\n1.  State the SLO target and window explicitly if the user gave them, or let the\n    SLO skill apply its SRE defaults (99.9% over a rolling 28 days) if they did\n    not.\n2.  Require the returned policies to carry `user_labels` with\n    `created-with-google-skill = \"cloud-monitoring-alert-configuration\"` so\n    attribution stays consistent with the rest of the generated suite. This\n    literal value does **NOT** change when you delegate: the label records the\n    entry-point skill, not whichever skill supplied the template. Do **NOT**\n    set `created-with-google-skill = \"google-cloud-slo-alert-configuration\"` on\n    policies generated through this delegation.\n3.  Preserve the burn-rate duration contract, which\n    `google-cloud-slo-alert-configuration` does not specify: Fast Burn (`1h` and\n    `5m` windows, factor `14.4`) carries `duration = \"300s\"`, and Slow Burn\n    (`[3d]` and `[6h]` lookback, factor `1.0`) **omits the `duration` field\n    entirely** — do not emit `duration = \"0s\"` in its place. Cloud Monitoring\n    rejects a non-zero retest buffer when the lookback window exceeds 25 hours.\n4.  When no notification channels are provided, inform the user that no\n    notification channels are attached to the alerting policies, advise them to\n    configure and attach notification channels, and ask if they want to\n    configure notification channels.\n\n> [!IMPORTANT]\n> **Always tell the user about the referral, even on the fallback path.** Some\n> reference files in this skill include standalone burn-rate templates to use\n> when the `google-cloud-slo-alert-configuration` skill is not installed. If you\n> emit policies from those fallback templates, your response MUST still tell the\n> user that `google-cloud-slo-alert-configuration` owns SLO configuration, state\n> that the emitted policies use the local fallback templates, and list the\n> handoff context. Never silently present SLO policies as if this\n> skill owned them.\n\n> [!NOTE]\n> **Exception**: Agent Platform (OpenTelemetry-instrumented AI agents on any\n> runtime, including Vertex AI Agent Engine and Cloud Run) is the one resource\n> whose Fast and Slow burn policies live in this skill and remain authoritative.\n> Delegate only for SLO customization — see\n> [Reliability](references\u002Fagent_platform_reliability_alert_policies.md).\n\n## Delegate Related Observability Tasks\n\n*   **Notification Channels**: Delegate to\n    `cloud-monitoring-notification-channels-configuration` to configure email,\n    PagerDuty, Pub\u002FSub, or Slack channels. When an alerting policy has no\n    notification channels attached, inform the user that no notification\n    channels are attached and that they should configure notification channels\n    and attach them to the alerting policy.\n*   **PromQL Authoring**: Delegate to `cloud-monitoring-promql-query` for\n    standalone PromQL query authoring or metric exploration.\n\n## Follow the Alert Configuration Workflow\n\n1.  **Discover**: Identify target resources, metric schemas, and scoping\n    projects using the lookup commands in the matching reference file (such as\n    [discover_metric_schema.py](scripts\u002Fdiscover_metric_schema.py) for Compute\n    Engine metrics and other Google Cloud resource metrics, or\n    [gather_agent_info.py](scripts\u002Fagent_platform\u002Fgather_agent_info.py),\n    [check_telemetry.py](scripts\u002Fagent_platform\u002Fcheck_telemetry.py),\n    [analyze_traffic.py](scripts\u002Fagent_platform\u002Fanalyze_traffic.py),\n    [create_online_monitor.py](scripts\u002Fagent_platform\u002Fcreate_online_monitor.py),\n    [list_trace_scope_table_names.py](scripts\u002Fagent_platform\u002Flist_trace_scope_table_names.py),\n    and\n    [list_log_scope_table_names.py](scripts\u002Fagent_platform\u002Flist_log_scope_table_names.py)\n    for AI agents).\n2.  **Route**: Load the reference file for the target resource type or delegate\n    SLOs to `google-cloud-slo-alert-configuration`.\n3.  **Generate**: Produce parameterized Terraform HCL configurations with\n    required `user_labels`.\n4.  **Validate**: When the loaded reference file requires a validation script\n    (such as [lint_promql.py](scripts\u002Flint_promql.py),\n    [validate_config.py](scripts\u002Fkubernetes\u002Fvalidate_config.py),\n    [lint_syntax.py](scripts\u002Fagent_platform\u002Flint_syntax.py), or\n    [scan_duplicates.py](scripts\u002Fagent_platform\u002Fscan_duplicates.py)), run or\n    print that exact script command. Do not list or search the `scripts\u002F`\n    directory.\n5.  **Explain and Prompt**: Explain each alert policy in plain English. When no\n    notification channels are provided, tell the user that no notification\n    channels are attached to the alerting policy, advise them to configure and\n    attach notification channels, and ask if they want to configure notification\n    channels.\n","# Configure Cloud Monitoring Alerts\n\nConfigure high-signal alerting policies in Cloud Monitoring using\nTerraform (`google_monitoring_alert_policy`) and PromQL\n(`condition_prometheus_query_language`).\n\n## Usage Attribution\n\nFor all `gcloud` CLI, REST API, and MCP attribution requirements when executing\ncommands with this skill, follow the instructions in\n[Usage Attribution](references\u002Fattribution.md).\n\n## Enforce Core Alerting Rules\n\n1.  **Apply the Four Golden Signals**: Cover latency, traffic, errors, and\n    saturation for the target workload.\n2.  **Use Dynamic Grouping**: Aggregate metrics by resource labels and\n    parameterize thresholds with Terraform variables. Never hardcode ephemeral\n    pod or instance identifiers.\n3.  **Default to PromQL**: Use Prometheus Query Language (PromQL) for metric\n    conditions, and use log-based alerts\n    (`condition_matched_log` or `google_logging_metric`) for lifecycle and\n    audit events.\n4.  **Attach Skill Attribution Labels**: Always include `user_labels` with\n    `created-with-google-skill = \"cloud-monitoring-alert-configuration\"` on\n    every generated `google_monitoring_alert_policy` resource.\n5.  **Require User Confirmation Before Mutating Resources**: Never execute\n    commands that modify infrastructure, enable telemetry, create online\n    monitors, or apply Terraform without waiting for explicit user confirmation.\n6.  **Prompt for Notification Channels**: Alerting policies without notification\n    channels incur charges without notifying anyone during an incident. Whenever\n    notification channels are not specified in the user prompt across any\n    workflow, explicitly inform the user that no notification channels are\n    attached to the alerting policies, advise them to configure and attach\n    notification channels before deploying, and ask at the end of your response\n    if they want to configure notification channels.\n\n## Route by Target Resource Type\n\nIdentify the target resource type and read only the matching reference file\ndirectly. Never run directory listings or workspace file searches to look for\nqueries or `.tf` files unless the prompt\nexplicitly provides a file path. If a user asks to fix or validate a PromQL\nquery without providing the query text or a file path, construct a\nrepresentative broken-and-corrected query example directly.\n\n### Configure Cloud Run Alerts\n\n*   **HTTP Services**:\n    [cloud_run_services.md](references\u002Fcloud_run_services.md)\n*   **Batch Jobs**:\n    [cloud_run_jobs.md](references\u002Fcloud_run_jobs.md)\n*   **Worker Pools**:\n    [cloud_run_worker_pools.md](references\u002Fcloud_run_worker_pools.md)\n\n### Configure Google Kubernetes Engine Alerts\n\n*   **PromQL Queries and Golden Signals**:\n    [GKE PromQL Queries](references\u002Fkubernetes_promql_queries.md)\n*   **Metrics Catalog and Open-Source Alerts**:\n    [GKE Metrics](references\u002Fkubernetes_metrics_and_alerts_catalog.md)\n*   **Prerequisites and Setup**:\n    [GKE Setup](references\u002Fkubernetes_gke_configuration_prerequisites.md)\n\n### Configure AI Agent Alerts\n\n*   **Reliability**:\n    [Reliability](references\u002Fagent_platform_reliability_alert_policies.md)\n*   **Quality**:\n    [Quality](references\u002Fagent_platform_quality_alert_policies.md)\n*   **Cost**:\n    [Cost](references\u002Fagent_platform_cost_alert_policies.md)\n*   **Safety**:\n    [Safety](references\u002Fagent_platform_safety_alert_policies.md)\n*   **Security**:\n    [Security](references\u002Fagent_platform_security_alert_policies.md)\n*   **Telemetry Enablement**:\n    [Telemetry](references\u002Fagent_platform_telemetry_enablement.md)\n*   **Traffic Profiles**:\n    [Historical](references\u002Fagent_platform_has_historical_traffic_data.md)\n    and\n    [No History](references\u002Fagent_platform_no_historical_traffic_data.md)\n\n### Configure Compute Engine and AI Hypercomputer Alerts\n\n*   **Compute Engine VMs, MIGs, GPU XID Insights, Host or Reservation\n    Maintenance (`type=scheduled`, `unscheduled`, or `multiple`), and Machine\n    or Domain Health**:\n    [compute_engine_alerts.md](references\u002Fcompute_engine_alerts.md)\n\n### Configure Generic Resource Alerts\n\n*   **Generic Workloads, Custom Metrics, MQL Migration, and HCL Deduplication**:\n    [generic_alert_policies.md](references\u002Fgeneric_alert_policies.md)\n\n### Delegate Service Level Objective Alerts\n\n**Delegate to the `google-cloud-slo-alert-configuration` skill.** That skill\nowns the SLO setup wizard, the burn-rate PromQL templates\n(`references\u002Fpromql_templates.md`), the SLI metric mapping\n(`references\u002Fservice_metrics.md`), and the App Hub scoping labels\n(`references\u002Fapp_hub_labels.md`). Do **NOT** duplicate those templates here or\nclaim that this skill owns its own SLO PromQL template library.\n\nDelegate whenever the user asks for an **availability or latency SLO**, an\n**error budget or burn-rate alert**, or a **Service Level Objective** on any\nresource. When SLOs are only one part of a broader request (for example, a full\nproduction alerting suite for a Cloud Run service), delegate the SLO policies\nand continue generating the remaining golden-signal policies from the\nresource-specific reference in the same response.\n\nEvery resource reference carries a **Context to hand off** list naming the SLI\nmetric, its good or bad filter, and the grouping labels — under a `## Delegate\nService Level Objective Alerts` heading for Cloud Run and generic resources, and\ninside the SLO section itself for GKE and Agent Platform. Read it and pass that\ncontext on, so the SLO skill does not repeat discovery you have already done.\nWhen delegating, you MUST also:\n\n1.  State the SLO target and window explicitly if the user gave them, or let the\n    SLO skill apply its SRE defaults (99.9% over a rolling 28 days) if they did\n    not.\n2.  Require the returned policies to carry `user_labels` with\n    `created-with-google-skill = \"cloud-monitoring-alert-configuration\"` so\n    attribution stays consistent with the rest of the generated suite. This\n    literal value does **NOT** change when you delegate: the label records the\n    entry-point skill, not whichever skill supplied the template. Do **NOT**\n    set `created-with-google-skill = \"google-cloud-slo-alert-configuration\"` on\n    policies generated through this delegation.\n3.  Preserve the burn-rate duration contract, which\n    `google-cloud-slo-alert-configuration` does not specify: Fast Burn (`1h` and\n    `5m` windows, factor `14.4`) carries `duration = \"300s\"`, and Slow Burn\n    (`[3d]` and `[6h]` lookback, factor `1.0`) **omits the `duration` field\n    entirely** — do not emit `duration = \"0s\"` in its place. Cloud Monitoring\n    rejects a non-zero retest buffer when the lookback window exceeds 25 hours.\n4.  When no notification channels are provided, inform the user that no\n    notification channels are attached to the alerting policies, advise them to\n    configure and attach notification channels, and ask if they want to\n    configure notification channels.\n\n> [!IMPORTANT]\n> **Always tell the user about the referral, even on the fallback path.** Some\n> reference files in this skill include standalone burn-rate templates to use\n> when the `google-cloud-slo-alert-configuration` skill is not installed. If you\n> emit policies from those fallback templates, your response MUST still tell the\n> user that `google-cloud-slo-alert-configuration` owns SLO configuration, state\n> that the emitted policies use the local fallback templates, and list the\n> handoff context. Never silently present SLO policies as if this\n> skill owned them.\n\n> [!NOTE]\n> **Exception**: Agent Platform (OpenTelemetry-instrumented AI agents on any\n> runtime, including Vertex AI Agent Engine and Cloud Run) is the one resource\n> whose Fast and Slow burn policies live in this skill and remain authoritative.\n> Delegate only for SLO customization — see\n> [Reliability](references\u002Fagent_platform_reliability_alert_policies.md).\n\n## Delegate Related Observability Tasks\n\n*   **Notification Channels**: Delegate to\n    `cloud-monitoring-notification-channels-configuration` to configure email,\n    PagerDuty, Pub\u002FSub, or Slack channels. When an alerting policy has no\n    notification channels attached, inform the user that no notification\n    channels are attached and that they should configure notification channels\n    and attach them to the alerting policy.\n*   **PromQL Authoring**: Delegate to `cloud-monitoring-promql-query` for\n    standalone PromQL query authoring or metric exploration.\n\n## Follow the Alert Configuration Workflow\n\n1.  **Discover**: Identify target resources, metric schemas, and scoping\n    projects using the lookup commands in the matching reference file (such as\n    [discover_metric_schema.py](scripts\u002Fdiscover_metric_schema.py) for Compute\n    Engine metrics and other Google Cloud resource metrics, or\n    [gather_agent_info.py](scripts\u002Fagent_platform\u002Fgather_agent_info.py),\n    [check_telemetry.py](scripts\u002Fagent_platform\u002Fcheck_telemetry.py),\n    [analyze_traffic.py](scripts\u002Fagent_platform\u002Fanalyze_traffic.py),\n    [create_online_monitor.py](scripts\u002Fagent_platform\u002Fcreate_online_monitor.py),\n    [list_trace_scope_table_names.py](scripts\u002Fagent_platform\u002Flist_trace_scope_table_names.py),\n    and\n    [list_log_scope_table_names.py](scripts\u002Fagent_platform\u002Flist_log_scope_table_names.py)\n    for AI agents).\n2.  **Route**: Load the reference file for the target resource type or delegate\n    SLOs to `google-cloud-slo-alert-configuration`.\n3.  **Generate**: Produce parameterized Terraform HCL configurations with\n    required `user_labels`.\n4.  **Validate**: When the loaded reference file requires a validation script\n    (such as [lint_promql.py](scripts\u002Flint_promql.py),\n    [validate_config.py](scripts\u002Fkubernetes\u002Fvalidate_config.py),\n    [lint_syntax.py](scripts\u002Fagent_platform\u002Flint_syntax.py), or\n    [scan_duplicates.py](scripts\u002Fagent_platform\u002Fscan_duplicates.py)), run or\n    print that exact script command. Do not list or search the `scripts\u002F`\n    directory.\n5.  **Explain and Prompt**: Explain each alert policy in plain English. When no\n    notification channels are provided, tell the user that no notification\n    channels are attached to the alerting policy, advise them to configure and\n    attach notification channels, and ask if they want to configure notification\n    channels.\n",{"name":5,"metadata":53,"description":7},{"tags":54,"comment":58,"version":59,"category":60,"support_tier":61,"canonical_source":62},[55,46,56,36,57],"cloud-monitoring","terraform","observability","Consolidated from Cloud Monitoring alerting policy best practices across Cloud Run, Google Kubernetes Engine, Compute Engine, and Vertex AI Agent 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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",[],[350,351,352,353,354,355,356,357,274,358,359,360,361,362,363,337,364,365,366,35,367,368,341,369],"migrate to rust","rust","migrate","github","memory","performance","directory","safe","ports","llvm","soa","cycle","porting","fix","arrays","infographic","high-performance","URL","architecture","unrelated",{"skillNo":371,"name":372,"nameZh":6,"description":373,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":374,"categories":375,"keywords":379,"publishedAt":48},"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",[376,377,378],{"categoryNo":261,"categoryName":262,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":295,"categoryName":296,"taxonomyType":297,"taxonomyTypeDesc":298},[9,380,381,382,383,314,384,385,386,387,388,304,389,390,391,338,392,393,35,394,340],"finding google skills","storage","as","messaging","model","trade-offs","provider","answer","fit","catalog","up","decision","use","database","candidate",{"skillNo":396,"name":397,"nameZh":6,"description":398,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":399,"categories":400,"keywords":403,"publishedAt":48},"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",[401,402],{"categoryNo":261,"categoryName":262,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[404,405,281,406,9,331,407,43,332,408,409,42,410,35,411,340,277,412,413,414,415],"workload manager basics","workload","basics","cli","review","vpc","inspect","you","create","libraries","server","run"]