[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69903DMT1H6R3008KGCFDGR":3,"skill-same:01M69903DMT1H6R3008KGCFDGR":108},{"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},"01M69903DMT1H6R3008KGCFDGR","bigquery-optimization",null,"Provides workflows to optimize BigQuery environments (capacity planning, editions), storage assets (partitioning, clustering, storage lifecycles, billing models), and SQL queries. Use when optimizing cost, modeling Edition migrations, rightsizing reservations, evaluating logical vs. physical storage, designing table partitioning\u002Fclustering, generating table DDL, migrating unpartitioned tables, managing partition expiration, optimizing individual SQL queries, or evaluating acceleration structures (search indexes, materialized views, BI Engine).\nDo not use for raw usage reporting (use bigquery-observability), query execution plan analysis, error troubleshooting, or diagnosing why a specific job was slow (use bigquery-troubleshooting).","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\u002Fbigquery-optimization\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"bigquery","Optimization","bigquery optimization","plan","when","billing","error","bi","modeling","query","capacity planning","optimize","partition","why","analysis","reporting","search","sql","workflows","storage","2026-11-10 10:12:30",false,"---\nname: bigquery-optimization\nmetadata:\n  version: \"1.1.1\"\n  category: BigDataAndAnalytics\ndescription: >-\n  Provides workflows to optimize BigQuery environments (capacity planning,\n  editions), storage assets (partitioning, clustering, storage lifecycles,\n  billing models), and SQL queries. Use when optimizing cost, modeling Edition\n  migrations, rightsizing reservations, evaluating logical vs. physical storage,\n  designing table partitioning\u002Fclustering, generating table DDL, migrating\n  unpartitioned tables, managing partition expiration, optimizing individual SQL\n  queries, or evaluating acceleration structures (search indexes, materialized\n  views, BI Engine).\n\n  Do not use for raw usage reporting (use bigquery-observability), query\n  execution plan analysis, error troubleshooting, or diagnosing why a specific\n  job was slow (use bigquery-troubleshooting).\n---\n\n# BigQuery Optimization Workflow\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:bigquery-optimization)\"`\nand append `agent-skills\u002F1.0 (skill:bigquery-optimization)` to `User-Agent` headers.\n\n## Prerequisites & Environment Setup\n\nBefore executing optimization analyses, evaluating editions, or applying DDL\nmodifications:\n\n1.  **Google Cloud SDK**: Ensure the\n    [Google Cloud SDK](https:\u002F\u002Fcloud.google.com\u002Fsdk\u002Fdocs\u002Finstall) is installed\n    and configured.\n2.  **Project Selection**: Set the active Google Cloud project:\n\n    ```bash\n    gcloud config set project {project_id}\n    ```\n\n3.  **API Enablement**: Ensure BigQuery and BigQuery Reservation APIs are\n    enabled:\n\n    ```bash\n    gcloud services enable \\\n        bigquery.googleapis.com bigqueryreservation.googleapis.com\n    ```\n\n4.  **Authentication**: Authenticate the environment:\n    *   CLI tools and `bq` commands: `gcloud auth login`\n    *   SDKs and automation: `gcloud auth application-default login`\n    *   Service accounts: Set\n        `GOOGLE_APPLICATION_CREDENTIALS=\"\u002Fpath\u002Fto\u002Fkey.json\"`\n\n5.  **Billing & IAM Roles**:\n    *   Verify an active Google Cloud Billing account is attached to\n        `{project_id}`.\n    *   Ensure appropriate IAM roles:\n        *   `roles\u002Fbigquery.admin` or `roles\u002Fbigquery.resourceAdmin`:\n            Reservation and capacity commitment management.\n        *   `roles\u002Fbigquery.dataEditor` or `roles\u002Fbigquery.admin`: Modifying\n            table schemas, partitioning, clustering, and storage billing\n            models.\n        *   `roles\u002Fbigquery.jobUser`: Running evaluation queries.\n\n6.  **Companion Skills Installation**:\n    This skill is part of a 3-pillar operations suite (`bigquery-observability`,\n    `bigquery-optimization`, `bigquery-troubleshooting`). If any companion skill\n    is not yet installed in your environment, install the full suite:\n\n    ```bash\n    npx skills add google\u002Fskills --skill bigquery-observability --skill bigquery-optimization --skill bigquery-troubleshooting\n    ```\n\n    *(If `bigquery-observability` is not installed, use the self-contained\n    baseline formulas and query templates provided directly in the reference\n    sections below).*\n\n## Workflows\n\nDetermine the optimization focus of the user's request and follow the relevant\nworkflow:\n\n-   **Telemetry & Observability Baseline:** For direct raw usage telemetry,\n    `INFORMATION_SCHEMA` queries, and baseline metric calculations, consult\n    [bigquery-observability](..\u002Fbigquery-observability)\n    (`bigquery_observability`). If the `bigquery-observability` companion skill\n    is not available in the active environment, all optimization guidelines, DDL\n    templates, and decision models across this skill and its reference guides\n    are fully self-contained.\n-   **Capacity & Editions Modeling:** Evaluate the cost-efficiency of migrating\n    workloads from On-Demand to Editions, as well as rightsizing active Edition\n    reservations, baseline commitments, and autoscaling caps.\n    *   *Instructions:* Read `references\u002Fcapacity_planning_editions.md` to\n        provide deep links to BigQuery's built-in recommendation UIs (e.g., Slot\n        Estimator) and guide the user through UI navigation: 1. navigate to the\n        Slot Estimator tab, 2. select 'On-Demand' as the source to analyze\n        historical query volume, and 3. review the Cost-Optimized\n        Recommendations and Slot Usage Chart.\n-   **Table & Storage Optimization:** Optimize storage costs from a billing\n    model, physical layout, and lifecycle perspective.\n    *   *Billing Architecture:* Read `references\u002Fstorage_billing_models.md` for\n        guidance on evaluating aggregate compression ratios (e.g. >2:1 threshold\n        in US) to recommend Physical vs. Logical billing, noting that the\n        break-even ratio depends on specific regional rates and custom\n        enterprise contracts. When providing `TABLE_STORAGE` queries, always\n        scope with `WHERE table_schema = '{dataset_id}'`, use the regional\n        dataset view, and warn that 0 rows indicates a region mismatch or lack\n        of native tables rather than zero billable usage.\n    *   *Partitioning & Clustering Strategy:* Read\n        `references\u002Ftable_partitioning_clustering.md` to generate production DDL\n        templates (CREATE TABLE, CTAS migrations for unpartitioned tables, and\n        modifying clustering specifications), enforce pruning with\n        `require_partition_filter = true`, and manage partition limits (up to\n        10,000 partitions\u002Ftable).\n    *   *Lifecycle Management:* Read\n        `references\u002Fstorage_lifecycle_management.md` to pinpoint inactive data\n        and define precise Time-to-Live (TTL) partition expirations, dataset\n        expirations, and Time Travel window reductions.\n    *   *Acceleration Structures:* Read `references\u002Fsearch_indexes.md` to decide\n        when to propose a search index for selective lookups over `STRING` or\n        `JSON` data, write the `CREATE SEARCH INDEX` DDL, and tell the user how\n        to verify index coverage and usage. Read\n        `references\u002Fmaterialized_views.md` to decide when to propose a\n        materialized view for repeated aggregations or joins over large base\n        tables, write the `CREATE MATERIALIZED VIEW` DDL, and tell the user how\n        to verify that smart tuning uses it. Read `references\u002Fbi_engine.md` to\n        decide when to propose BI Engine vs. Materialized Views for BI dashboard\n        acceleration, diagnose `PARTIAL` or `DISABLED` BI Engine fallback\n        reasons (`bi_engine_statistics`), and combine BI Engine with\n        materialized views that pre-join or pre-aggregate the data.\n-   **SQL Optimization:** Optimize individual SQL queries to reduce slot-time\n    and the amount of data read.\n    *   *Instructions:* Follow the instructions in\n        `references\u002Fsql_optimization.md` to provide recommendations to the user\n        on how to rewrite their SQL query to reduce slot-time and the amount of\n        data read.\n\n## Execution Guardrails\n\n-   **Terminology & Cost Framing:** Never promise or guarantee \"cost-reduction\"\n    or \"reducing expenditure.\" Always frame recommendations using the\n    terminology **\"optimizing your bill\"** or **\"improving cost-efficiency.\"**\n-   **Explicit Scope Framing & Region Resolution:** Always state the target\n    `project_id` and `region` at the very top of your response so the user\n    immediately knows the exact scope being evaluated. Follow this 3-tier\n    resolution hierarchy:\n    1.  *Explicit Region:* Use the region specified in the user's prompt (e.g.,\n        `europe-west1`).\n    2.  *Contextual Region:* Resolve the region from the specific dataset or\n        resource mentioned in the context.\n    3.  *Unspecified Fallback:* Default to `us` \u002F `region-us`, explicitly state\n        that `us` was assumed as the default, and instruct the user to\n        substitute their region if their resources reside elsewhere. *Region\n        Formatting:* In Cloud Console deep links, use the region identifier\n        directly (e.g., `region=us`, `region=europe-west1`). In SQL queries\n        against `INFORMATION_SCHEMA`, use the regional dataset qualifier (e.g.,\n        `region-us`, `region-europe-west1`).\n-   **Zero-Row Result Guard:** If querying `TABLE_STORAGE` with `WHERE\n    table_schema = '{dataset_id}'` returns 0 rows, do not proceed with an empty\n    or zero-usage evaluation. Treat this as an indicator that the dataset may\n    reside in a different region or have no native tables; stop and prompt the\n    user to confirm the dataset's regional location.\n-   **Populate Concrete Parameters:** When generating URLs and SQL queries,\n    always substitute known `project_id` and `region` values directly into the\n    code and links. Never leave literal `{project_id}` or `{location}`\n    placeholders for the user to manually edit.\n-   **No Autonomous Purchasing or Financial Mutations:** Never provide the user\n    with executable scripts (e.g., `gcloud` or `bq` shell commands like `bq\n    update --storage_billing_model=...`) designed to autonomously purchase\n    annual commitments, alter edition tier bindings, or mutate storage billing\n    models. Always guide the user to execute commitment purchases, reservation\n    changes, and storage billing model updates manually via the Cloud Console\n    UI.\n","# BigQuery Optimization Workflow\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:bigquery-optimization)\"`\nand append `agent-skills\u002F1.0 (skill:bigquery-optimization)` to `User-Agent` headers.\n\n## Prerequisites & Environment Setup\n\nBefore executing optimization analyses, evaluating editions, or applying DDL\nmodifications:\n\n1.  **Google Cloud SDK**: Ensure the\n    [Google Cloud SDK](https:\u002F\u002Fcloud.google.com\u002Fsdk\u002Fdocs\u002Finstall) is installed\n    and configured.\n2.  **Project Selection**: Set the active Google Cloud project:\n\n    ```bash\n    gcloud config set project {project_id}\n    ```\n\n3.  **API Enablement**: Ensure BigQuery and BigQuery Reservation APIs are\n    enabled:\n\n    ```bash\n    gcloud services enable \\\n        bigquery.googleapis.com bigqueryreservation.googleapis.com\n    ```\n\n4.  **Authentication**: Authenticate the environment:\n    *   CLI tools and `bq` commands: `gcloud auth login`\n    *   SDKs and automation: `gcloud auth application-default login`\n    *   Service accounts: Set\n        `GOOGLE_APPLICATION_CREDENTIALS=\"\u002Fpath\u002Fto\u002Fkey.json\"`\n\n5.  **Billing & IAM Roles**:\n    *   Verify an active Google Cloud Billing account is attached to\n        `{project_id}`.\n    *   Ensure appropriate IAM roles:\n        *   `roles\u002Fbigquery.admin` or `roles\u002Fbigquery.resourceAdmin`:\n            Reservation and capacity commitment management.\n        *   `roles\u002Fbigquery.dataEditor` or `roles\u002Fbigquery.admin`: Modifying\n            table schemas, partitioning, clustering, and storage billing\n            models.\n        *   `roles\u002Fbigquery.jobUser`: Running evaluation queries.\n\n6.  **Companion Skills Installation**:\n    This skill is part of a 3-pillar operations suite (`bigquery-observability`,\n    `bigquery-optimization`, `bigquery-troubleshooting`). If any companion skill\n    is not yet installed in your environment, install the full suite:\n\n    ```bash\n    npx skills add google\u002Fskills --skill bigquery-observability --skill bigquery-optimization --skill bigquery-troubleshooting\n    ```\n\n    *(If `bigquery-observability` is not installed, use the self-contained\n    baseline formulas and query templates provided directly in the reference\n    sections below).*\n\n## Workflows\n\nDetermine the optimization focus of the user's request and follow the relevant\nworkflow:\n\n-   **Telemetry & Observability Baseline:** For direct raw usage telemetry,\n    `INFORMATION_SCHEMA` queries, and baseline metric calculations, consult\n    [bigquery-observability](..\u002Fbigquery-observability)\n    (`bigquery_observability`). If the `bigquery-observability` companion skill\n    is not available in the active environment, all optimization guidelines, DDL\n    templates, and decision models across this skill and its reference guides\n    are fully self-contained.\n-   **Capacity & Editions Modeling:** Evaluate the cost-efficiency of migrating\n    workloads from On-Demand to Editions, as well as rightsizing active Edition\n    reservations, baseline commitments, and autoscaling caps.\n    *   *Instructions:* Read `references\u002Fcapacity_planning_editions.md` to\n        provide deep links to BigQuery's built-in recommendation UIs (e.g., Slot\n        Estimator) and guide the user through UI navigation: 1. navigate to the\n        Slot Estimator tab, 2. select 'On-Demand' as the source to analyze\n        historical query volume, and 3. review the Cost-Optimized\n        Recommendations and Slot Usage Chart.\n-   **Table & Storage Optimization:** Optimize storage costs from a billing\n    model, physical layout, and lifecycle perspective.\n    *   *Billing Architecture:* Read `references\u002Fstorage_billing_models.md` for\n        guidance on evaluating aggregate compression ratios (e.g. >2:1 threshold\n        in US) to recommend Physical vs. Logical billing, noting that the\n        break-even ratio depends on specific regional rates and custom\n        enterprise contracts. When providing `TABLE_STORAGE` queries, always\n        scope with `WHERE table_schema = '{dataset_id}'`, use the regional\n        dataset view, and warn that 0 rows indicates a region mismatch or lack\n        of native tables rather than zero billable usage.\n    *   *Partitioning & Clustering Strategy:* Read\n        `references\u002Ftable_partitioning_clustering.md` to generate production DDL\n        templates (CREATE TABLE, CTAS migrations for unpartitioned tables, and\n        modifying clustering specifications), enforce pruning with\n        `require_partition_filter = true`, and manage partition limits (up to\n        10,000 partitions\u002Ftable).\n    *   *Lifecycle Management:* Read\n        `references\u002Fstorage_lifecycle_management.md` to pinpoint inactive data\n        and define precise Time-to-Live (TTL) partition expirations, dataset\n        expirations, and Time Travel window reductions.\n    *   *Acceleration Structures:* Read `references\u002Fsearch_indexes.md` to decide\n        when to propose a search index for selective lookups over `STRING` or\n        `JSON` data, write the `CREATE SEARCH INDEX` DDL, and tell the user how\n        to verify index coverage and usage. Read\n        `references\u002Fmaterialized_views.md` to decide when to propose a\n        materialized view for repeated aggregations or joins over large base\n        tables, write the `CREATE MATERIALIZED VIEW` DDL, and tell the user how\n        to verify that smart tuning uses it. Read `references\u002Fbi_engine.md` to\n        decide when to propose BI Engine vs. Materialized Views for BI dashboard\n        acceleration, diagnose `PARTIAL` or `DISABLED` BI Engine fallback\n        reasons (`bi_engine_statistics`), and combine BI Engine with\n        materialized views that pre-join or pre-aggregate the data.\n-   **SQL Optimization:** Optimize individual SQL queries to reduce slot-time\n    and the amount of data read.\n    *   *Instructions:* Follow the instructions in\n        `references\u002Fsql_optimization.md` to provide recommendations to the user\n        on how to rewrite their SQL query to reduce slot-time and the amount of\n        data read.\n\n## Execution Guardrails\n\n-   **Terminology & Cost Framing:** Never promise or guarantee \"cost-reduction\"\n    or \"reducing expenditure.\" Always frame recommendations using the\n    terminology **\"optimizing your bill\"** or **\"improving cost-efficiency.\"**\n-   **Explicit Scope Framing & Region Resolution:** Always state the target\n    `project_id` and `region` at the very top of your response so the user\n    immediately knows the exact scope being evaluated. Follow this 3-tier\n    resolution hierarchy:\n    1.  *Explicit Region:* Use the region specified in the user's prompt (e.g.,\n        `europe-west1`).\n    2.  *Contextual Region:* Resolve the region from the specific dataset or\n        resource mentioned in the context.\n    3.  *Unspecified Fallback:* Default to `us` \u002F `region-us`, explicitly state\n        that `us` was assumed as the default, and instruct the user to\n        substitute their region if their resources reside elsewhere. *Region\n        Formatting:* In Cloud Console deep links, use the region identifier\n        directly (e.g., `region=us`, `region=europe-west1`). In SQL queries\n        against `INFORMATION_SCHEMA`, use the regional dataset qualifier (e.g.,\n        `region-us`, `region-europe-west1`).\n-   **Zero-Row Result Guard:** If querying `TABLE_STORAGE` with `WHERE\n    table_schema = '{dataset_id}'` returns 0 rows, do not proceed with an empty\n    or zero-usage evaluation. Treat this as an indicator that the dataset may\n    reside in a different region or have no native tables; stop and prompt the\n    user to confirm the dataset's regional location.\n-   **Populate Concrete Parameters:** When generating URLs and SQL queries,\n    always substitute known `project_id` and `region` values directly into the\n    code and links. Never leave literal `{project_id}` or `{location}`\n    placeholders for the user to manually edit.\n-   **No Autonomous Purchasing or Financial Mutations:** Never provide the user\n    with executable scripts (e.g., `gcloud` or `bq` shell commands like `bq\n    update --storage_billing_model=...`) designed to autonomously purchase\n    annual commitments, alter edition tier bindings, or mutate storage billing\n    models. Always guide the user to execute commitment purchases, reservation\n    changes, and storage billing model updates manually via the Cloud Console\n    UI.\n",{"name":5,"metadata":44,"description":7},{"version":45,"category":46},"1.1.1","BigDataAndAnalytics","main","bb9b3f464aae37af360d079c1d863524955b0363","skills\u002Fcloud\u002Fbigquery-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 bigquery-optimization",{"tool":62,"command":63},"bunx","bunx skills add google\u002Fskills --skill bigquery-optimization",{"tool":65,"command":66},"pnpm","pnpm dlx skills add google\u002Fskills --skill bigquery-optimization","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fbigquery-optimization\u002FSKILL.md",true,[70,76,80,84,88,92,96,100,104],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"references\u002Fattribution.md","REFERENCE","参考文档",1903,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fbigquery-optimization\u002Freferences\u002Fattribution.md",{"path":77,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fbi_engine.md",7439,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fbigquery-optimization\u002Freferences\u002Fbi_engine.md",{"path":81,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fcapacity_planning_editions.md",7148,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fbigquery-optimization\u002Freferences\u002Fcapacity_planning_editions.md",{"path":85,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fmaterialized_views.md",5022,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fbigquery-optimization\u002Freferences\u002Fmaterialized_views.md",{"path":89,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Fsearch_indexes.md",4151,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fbigquery-optimization\u002Freferences\u002Fsearch_indexes.md",{"path":93,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":94,"sourceHtmlUrl":95},"references\u002Fsql_optimization.md",24066,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fbigquery-optimization\u002Freferences\u002Fsql_optimization.md",{"path":97,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":98,"sourceHtmlUrl":99},"references\u002Fstorage_billing_models.md",6381,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fbigquery-optimization\u002Freferences\u002Fstorage_billing_models.md",{"path":101,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":102,"sourceHtmlUrl":103},"references\u002Fstorage_lifecycle_management.md",2274,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fbigquery-optimization\u002Freferences\u002Fstorage_lifecycle_management.md",{"path":105,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":106,"sourceHtmlUrl":107},"references\u002Ftable_partitioning_clustering.md",15714,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fbigquery-optimization\u002Freferences\u002Ftable_partitioning_clustering.md",[109,147,178,205,232,256],{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":123,"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",[115,120],{"categoryNo":116,"categoryName":117,"taxonomyType":118,"taxonomyTypeDesc":119},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",{"categoryNo":121,"categoryName":122,"taxonomyType":118,"taxonomyTypeDesc":119},"01JBSEEDCAT000000000000010","后端与接口",[9,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146],"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":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":162,"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",[153,154,157],{"categoryNo":121,"categoryName":122,"taxonomyType":118,"taxonomyTypeDesc":119},{"categoryNo":155,"categoryName":156,"taxonomyType":118,"taxonomyTypeDesc":119},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":158,"categoryName":159,"taxonomyType":160,"taxonomyTypeDesc":161},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[163,164,9,132,165,166,167,168,169,170,138,171,172,173,174,175,176,143,146,177,23],"dpop adoption","adoption","es6","retry","authentication","flows","challenge","code","browser","loops","at","node","token","pair","platform",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":190,"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",[184,185,188,189],{"categoryNo":121,"categoryName":122,"taxonomyType":118,"taxonomyTypeDesc":119},{"categoryNo":186,"categoryName":187,"taxonomyType":118,"taxonomyTypeDesc":119},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":155,"categoryName":156,"taxonomyType":118,"taxonomyTypeDesc":119},{"categoryNo":158,"categoryName":159,"taxonomyType":160,"taxonomyTypeDesc":161},[191,192,193,194,132,195,196,197,198,35,199,200,201,23,28,202,203,138,204,139],"retrieving developer knowledge","knowledge","developer","MCP","mcp server","official","developer documentation","get","local","choice","documents","api","across","documentation",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":209,"categories":210,"keywords":211,"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",[],[212,213,214,215,216,217,218,219,135,220,221,222,223,224,225,199,226,227,228,23,229,230,203,231],"migrate to rust","rust","migrate","github","memory","performance","directory","safe","ports","llvm","soa","cycle","porting","fix","arrays","infographic","high-performance","URL","architecture","unrelated",{"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":241,"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",[238,239,240],{"categoryNo":121,"categoryName":122,"taxonomyType":118,"taxonomyTypeDesc":119},{"categoryNo":155,"categoryName":156,"taxonomyType":118,"taxonomyTypeDesc":119},{"categoryNo":158,"categoryName":159,"taxonomyType":160,"taxonomyTypeDesc":161},[9,242,38,243,244,177,245,246,247,248,249,167,250,251,252,200,253,254,23,255,202],"finding google skills","as","messaging","model","trade-offs","provider","answer","fit","catalog","up","decision","use","database","candidate",{"skillNo":257,"name":258,"nameZh":6,"description":259,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":260,"categories":261,"keywords":264,"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",[262,263],{"categoryNo":121,"categoryName":122,"taxonomyType":118,"taxonomyTypeDesc":119},{"categoryNo":155,"categoryName":156,"taxonomyType":118,"taxonomyTypeDesc":119},[265,266,142,267,9,194,268,132,195,269,270,271,272,23,273,202,138,274,275,276,277],"workload manager basics","workload","basics","cli","review","vpc","resources","inspect","you","create","libraries","server","run"]