[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69962G32HG3RQHX0RMSTT3W":3,"skill-same:01M69962G32HG3RQHX0RMSTT3W":103},{"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},"01M69962G32HG3RQHX0RMSTT3W","google-cloud-solution-agentic-ai-borderless-data-lakehouse",null,"Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-driven IDE analytics workflows (use google-cloud-solution-agentic-analytics-spark-knowledge-catalog instead).","google\u002Fskills","google","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F1342004?v=4","01M698VYEFDM0VJBZDYYJ2T83S","Google","01M698WR0H76ENHE587T9EJT89",24122,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgoogle-cloud-solution-agentic-ai-borderless-data-lakehouse\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,9,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"agentic","agentic ai","google cloud","google cloud solution agentic ai borderless data lakehouse","lakehouse","cloud","data","apache","when","Cloud Storage","aws s3","focus","ingestion","knowledge","open","iceberg","workflows","storage","gemini enterprise agent platform","gemini","2026-11-10 10:12:30",false,"---\nname: google-cloud-solution-agentic-ai-borderless-data-lakehouse\nmetadata:\n  version: \"1.0.1\"\n  category: MultiProductSolutions\ndescription: >-\n  Discovers requirements and designs a borderless open data lakehouse using\n  Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting\n  multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob),\n  establishing ingestion and AI serving subsystems, configuring Cross-Cloud\n  Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data\n  agents. Don't use for single-cloud data warehouses, or when the focus is on\n  Knowledge Catalog metadata governance and Spark-driven IDE analytics workflows\n  (use google-cloud-solution-agentic-analytics-spark-knowledge-catalog instead).\n---\n\n# Borderless open data lakehouse agentic AI system\n\nFollow this workflow to help users design and implement a custom multi-product\nsolution in the cloud for a given workload, use case, or requirement.\n\n## Attribution\n\n**MUST READ**: Before executing any `gcloud`, REST API, or MCP command, you\nMUST read and follow the instructions in\n[Usage Attribution](references\u002Fattribution.md).\n\n## Product Renaming & Terminology\n\nWhen generating solution designs, architecture diagrams, and documentation, use\nthe updated Google Cloud product names. For details on legacy vs. updated\nproduct names and terminology, see\n[references\u002Fproduct_renaming.md](references\u002Fproduct_renaming.md).\n\n## Workflow\n\nThe solution design and implementation workflow consists of the following\nphases:\n\n- **Phase 1: Requirements discovery and analysis**: Analyze the workload's\n  requirements, constraints, dependencies, and current state.\n- **Phase 2: Solution design**: Build a technology stack, architecture, and\n  deployment configuration for the workload based on Google Cloud design best\n  practices and recommendations.\n- **Phase 3: Implementation plan**: Generate automation\n  and instructions to deploy the solution.\n- **Phase 4: Solution validation**: Validate that the deployment meets the\n  requirements of the workload.\n\n### Phase 1: Requirements discovery and analysis\n\n- [ ] **Step 1: Discover requirements**: Understand the functional and\n  non-functional requirements, business goals, and current state (if any) of the\n  workload, including its architecture, dependencies, and constraints. Use the\n  following questions to guide the requirements discovery process:\n   - What are your primary data sources?\n   - How do you manage and federate metadata across your data sources?\n   - What are your security and credential management requirements?\n   - What are the analytical and computational requirements to join and\n     transform this borderless data?\n   - What types of natural language prompts or user queries do you expect AI\n     agents or end-users to execute against this data?\n\n- [ ] **Step 2: Identify components**: Based on the requirements analysis,\n  identify the components of the workload and their relationships. Also identify\n  any borderless components, hybrid components, or on-prem components that the\n  solution needs to integrate with.\n\n- [ ] **Step 3: Generate component decomposition**: Generate a technical\n  decomposition of the components of the workload.\n\n- [ ] **Step 4: Ask for confirmation**: Ask the user to confirm whether the\n  generated technical decomposition matches their workload requirements.\n\n- [ ] **Step 5: Iterate**: If the user requests changes, then generate an\n  updated technical decomposition, and ask the user to confirm the changes.\n  Continue iterating until the user confirms the technical decomposition.\n\n### Phase 2: Solution design\n\n- [ ] **Step 1: Retrieve relevant Google Cloud documentation**: Use available\n  search or fetch tools to read the content of the following Google Cloud\n  documentation to ground the guidance that you generate in the remaining\n  steps of this phase before proceeding.\n   - [Build hybrid and borderless architectures using Google Cloud](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fhybrid-multicloud-patterns\u002Fone-page-view.md.txt)\n   - [Build a borderless open data lakehouse](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fagentic-ai-build-multicloud-open-data-lakehouse.md.txt)\n   - [Implement agentic analytics workflows for distributed data](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fagentic-ai-cross-cloud-analytics.md.txt)\n   - [Analytics Hybrid and Multicloud Pattern](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fhybrid-multicloud-patterns-and-practices\u002Fanalytics-hybrid-multicloud-pattern.md.txt)\n   - [Google Cloud multi-regional deployment archetype](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fdeployment-archetypes\u002Fmultiregional.md.txt)\n   - [Network segmentation and connectivity for distributed applications in Cross-Cloud Network](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fccn-distributed-apps-design\u002Fconnectivity.md.txt)\n   - [Patterns for Connecting Other Cloud Service Providers with Google Cloud](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fpatterns-for-connecting-other-csps-with-gcp.md.txt)\n\n  *Important*: Use the content that you retrieve from Google Cloud\n   documentation to ground the guidance that you generate in the remaining\n   steps of this phase.\n\n- [ ] **Step 2: Map components to Google Cloud products**: For each component in\n  the confirmed technical decomposition, identify the appropriate Google Cloud\n  products and features, based on the guidelines in\n  [references\u002Fproduct_mapping.md](references\u002Fproduct_mapping.md).\n\n- [ ] **Step 3: Create architecture diagram**: Create an architecture diagram\n  that shows the components, their relationships, and data\u002Fcontrol flows.\n   - The diagram must be in the Mermaid format:\n     https:\u002F\u002Fgithub.com\u002Fmermaid-js\u002Fmermaid.\n   - The diagram must show a clear distinction between the products in the\n     data ingestion subsystem and the serving subsystem.\n   - The diagram must show Managed Service for Apache Spark as a shared\n     component for ETL\u002Fingestion processing, bridging the data ingestion and\n     serving subsystems (distinct from interactive IDE analytics workflows).\n\n- [ ] **Step 4: Generate design recommendations**: Generate design guidance\n  based on the guidelines in\n  [references\u002Fdesign_recommendations.md](references\u002Fdesign_recommendations.md).\n\n- [ ] **Step 5: Draft solution architecture**: Compile the requirements,\n  technical decomposition, product mapping, architecture diagram, and design\n  recommendations into a single Markdown file named\n  `solution-architecture-guide.md`, based on the template in\n  [assets\u002Foutput-template.md](assets\u002Foutput-template.md).\n\n- [ ] **Step 6: Request review**: Present the generated solution architecture to\n  the user and request their feedback or approval.\n\n- [ ] **Step 7: Iterate**: If the user requests changes, generate an updated\n  solution architecture and repeat steps 2-6 until the user approves the\n  solution architecture.\n\n### Phase 3: Implementation plan\n\n- [ ] **Step 1: Retrieve relevant implementation resources**:\n   - [Build a Multicloud Open Data Lakehouse with Agentic AI](https:\u002F\u002Fcodelabs.developers.google.com\u002Fnext26\u002Fmulticloud-lakehouse)\n   - [Terraform Registry documentation for biglake_iceberg_catalog](https:\u002F\u002Fregistry.terraform.io\u002Fproviders\u002Fhashicorp\u002Fgoogle\u002Flatest\u002Fdocs\u002Fresources\u002Fbiglake_iceberg_catalog)\n   - [Create an Apache Iceberg table with metadata in Lakehouse runtime catalog](https:\u002F\u002Fdocs.cloud.google.com\u002Fmanaged-spark\u002Fdocs\u002Fguides\u002Fspark-workloads-with-bigquery-metastore.md.txt)\n   - [Accelerate Spark batch workloads and sessions with Lightning Engine](https:\u002F\u002Fdocs.cloud.google.com\u002Fmanaged-spark\u002Fdocs\u002Fguides\u002Flightning-engine-serverless.md.txt)\n   - [Create data agents](https:\u002F\u002Fdocs.cloud.google.com\u002Fbigquery\u002Fdocs\u002Fcreate-data-agents.md.txt)\n\n   *Important*: Use these resources as the technical foundation for the IaC and\n   deployment instructions you generate in the remaining steps of this phase.\n\n- [ ] **Step 2: Identify deployment prerequisites**: Document prerequisites for\n  the deployment, including the following:\n   - Projects and billing associations\n   - Required Google Cloud APIs\n   - Required IAM permissions\n   - Any other prerequisites\n\n- [ ] **Step 3: Generate Infrastructure as Code (IaC)**: Generate code (e.g.,\n  Terraform) and deployment scripts to automate the provisioning of the proposed\n  Google Cloud resources.\n\n- [ ] **Step 4: Write deployment instructions**: Draft sequential, step-by-step\n  deployment instructions to execute the IaC and initialize the workload\n  components.\n\n- [ ] **Step 5: Request review**: Present the generated deployment instructions\n  to the user for feedback and confirmation.\n\n- [ ] **Step 6: Iterate**: If the user requests changes, generate an updated\n  implementation plan and repeat steps 2-5 until the user approves the\n  implementation plan.\n\n### Phase 4: Solution validation\n\n- [ ] **Step 1: Retrieve relevant verification resources (optional)**: If the\n  resources from Phase 3 are not already in your context, retrieve the same\n  implementation resources as the starting point for the validation checks\n  and verification scripts that you generate in this phase.\n\n- [ ] **Step 2: Define validation checks**: Outline validation steps to verify\n  that the deployed infrastructure meets the workload requirements:\n   - **Deployment dry-run**: Commands like `terraform plan` to preview\n     changes.\n   - **Connectivity and routing**: Verification of network paths, load\n     balancer routing, and service endpoints.\n   - **Security policies**: Verification of restricted access, firewall\n     rules, and IAM enforcement.\n\n- [ ] **Step 3: Generate verification scripts**: Draft lightweight scripts or\n  command-line instructions (e.g. using `curl` or `gcloud`) that the user can\n  run to perform these validation checks.\n\n- [ ] **Step 4: Compile validation report**: Document the validation steps,\n  verification scripts, and expected outcomes in a single Markdown file.\n\n- [ ] **Step 5: Conduct validation and finalize**: Assist the user in executing\n  the validation checks and troubleshooting any deployment issues. After the\n  solution is validated successfully, request final approval from the user.\n\n- [ ] **Step 6: Iterate**: If the user requests changes, then generate an\n  updated validation plan and repeat steps 2-5 until the user approves the\n  validation plan.\n","# Borderless open data lakehouse agentic AI system\n\nFollow this workflow to help users design and implement a custom multi-product\nsolution in the cloud for a given workload, use case, or requirement.\n\n## Attribution\n\n**MUST READ**: Before executing any `gcloud`, REST API, or MCP command, you\nMUST read and follow the instructions in\n[Usage Attribution](references\u002Fattribution.md).\n\n## Product Renaming & Terminology\n\nWhen generating solution designs, architecture diagrams, and documentation, use\nthe updated Google Cloud product names. For details on legacy vs. updated\nproduct names and terminology, see\n[references\u002Fproduct_renaming.md](references\u002Fproduct_renaming.md).\n\n## Workflow\n\nThe solution design and implementation workflow consists of the following\nphases:\n\n- **Phase 1: Requirements discovery and analysis**: Analyze the workload's\n  requirements, constraints, dependencies, and current state.\n- **Phase 2: Solution design**: Build a technology stack, architecture, and\n  deployment configuration for the workload based on Google Cloud design best\n  practices and recommendations.\n- **Phase 3: Implementation plan**: Generate automation\n  and instructions to deploy the solution.\n- **Phase 4: Solution validation**: Validate that the deployment meets the\n  requirements of the workload.\n\n### Phase 1: Requirements discovery and analysis\n\n- [ ] **Step 1: Discover requirements**: Understand the functional and\n  non-functional requirements, business goals, and current state (if any) of the\n  workload, including its architecture, dependencies, and constraints. Use the\n  following questions to guide the requirements discovery process:\n   - What are your primary data sources?\n   - How do you manage and federate metadata across your data sources?\n   - What are your security and credential management requirements?\n   - What are the analytical and computational requirements to join and\n     transform this borderless data?\n   - What types of natural language prompts or user queries do you expect AI\n     agents or end-users to execute against this data?\n\n- [ ] **Step 2: Identify components**: Based on the requirements analysis,\n  identify the components of the workload and their relationships. Also identify\n  any borderless components, hybrid components, or on-prem components that the\n  solution needs to integrate with.\n\n- [ ] **Step 3: Generate component decomposition**: Generate a technical\n  decomposition of the components of the workload.\n\n- [ ] **Step 4: Ask for confirmation**: Ask the user to confirm whether the\n  generated technical decomposition matches their workload requirements.\n\n- [ ] **Step 5: Iterate**: If the user requests changes, then generate an\n  updated technical decomposition, and ask the user to confirm the changes.\n  Continue iterating until the user confirms the technical decomposition.\n\n### Phase 2: Solution design\n\n- [ ] **Step 1: Retrieve relevant Google Cloud documentation**: Use available\n  search or fetch tools to read the content of the following Google Cloud\n  documentation to ground the guidance that you generate in the remaining\n  steps of this phase before proceeding.\n   - [Build hybrid and borderless architectures using Google Cloud](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fhybrid-multicloud-patterns\u002Fone-page-view.md.txt)\n   - [Build a borderless open data lakehouse](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fagentic-ai-build-multicloud-open-data-lakehouse.md.txt)\n   - [Implement agentic analytics workflows for distributed data](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fagentic-ai-cross-cloud-analytics.md.txt)\n   - [Analytics Hybrid and Multicloud Pattern](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fhybrid-multicloud-patterns-and-practices\u002Fanalytics-hybrid-multicloud-pattern.md.txt)\n   - [Google Cloud multi-regional deployment archetype](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fdeployment-archetypes\u002Fmultiregional.md.txt)\n   - [Network segmentation and connectivity for distributed applications in Cross-Cloud Network](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fccn-distributed-apps-design\u002Fconnectivity.md.txt)\n   - [Patterns for Connecting Other Cloud Service Providers with Google Cloud](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fpatterns-for-connecting-other-csps-with-gcp.md.txt)\n\n  *Important*: Use the content that you retrieve from Google Cloud\n   documentation to ground the guidance that you generate in the remaining\n   steps of this phase.\n\n- [ ] **Step 2: Map components to Google Cloud products**: For each component in\n  the confirmed technical decomposition, identify the appropriate Google Cloud\n  products and features, based on the guidelines in\n  [references\u002Fproduct_mapping.md](references\u002Fproduct_mapping.md).\n\n- [ ] **Step 3: Create architecture diagram**: Create an architecture diagram\n  that shows the components, their relationships, and data\u002Fcontrol flows.\n   - The diagram must be in the Mermaid format:\n     https:\u002F\u002Fgithub.com\u002Fmermaid-js\u002Fmermaid.\n   - The diagram must show a clear distinction between the products in the\n     data ingestion subsystem and the serving subsystem.\n   - The diagram must show Managed Service for Apache Spark as a shared\n     component for ETL\u002Fingestion processing, bridging the data ingestion and\n     serving subsystems (distinct from interactive IDE analytics workflows).\n\n- [ ] **Step 4: Generate design recommendations**: Generate design guidance\n  based on the guidelines in\n  [references\u002Fdesign_recommendations.md](references\u002Fdesign_recommendations.md).\n\n- [ ] **Step 5: Draft solution architecture**: Compile the requirements,\n  technical decomposition, product mapping, architecture diagram, and design\n  recommendations into a single Markdown file named\n  `solution-architecture-guide.md`, based on the template in\n  [assets\u002Foutput-template.md](assets\u002Foutput-template.md).\n\n- [ ] **Step 6: Request review**: Present the generated solution architecture to\n  the user and request their feedback or approval.\n\n- [ ] **Step 7: Iterate**: If the user requests changes, generate an updated\n  solution architecture and repeat steps 2-6 until the user approves the\n  solution architecture.\n\n### Phase 3: Implementation plan\n\n- [ ] **Step 1: Retrieve relevant implementation resources**:\n   - [Build a Multicloud Open Data Lakehouse with Agentic AI](https:\u002F\u002Fcodelabs.developers.google.com\u002Fnext26\u002Fmulticloud-lakehouse)\n   - [Terraform Registry documentation for biglake_iceberg_catalog](https:\u002F\u002Fregistry.terraform.io\u002Fproviders\u002Fhashicorp\u002Fgoogle\u002Flatest\u002Fdocs\u002Fresources\u002Fbiglake_iceberg_catalog)\n   - [Create an Apache Iceberg table with metadata in Lakehouse runtime catalog](https:\u002F\u002Fdocs.cloud.google.com\u002Fmanaged-spark\u002Fdocs\u002Fguides\u002Fspark-workloads-with-bigquery-metastore.md.txt)\n   - [Accelerate Spark batch workloads and sessions with Lightning Engine](https:\u002F\u002Fdocs.cloud.google.com\u002Fmanaged-spark\u002Fdocs\u002Fguides\u002Flightning-engine-serverless.md.txt)\n   - [Create data agents](https:\u002F\u002Fdocs.cloud.google.com\u002Fbigquery\u002Fdocs\u002Fcreate-data-agents.md.txt)\n\n   *Important*: Use these resources as the technical foundation for the IaC and\n   deployment instructions you generate in the remaining steps of this phase.\n\n- [ ] **Step 2: Identify deployment prerequisites**: Document prerequisites for\n  the deployment, including the following:\n   - Projects and billing associations\n   - Required Google Cloud APIs\n   - Required IAM permissions\n   - Any other prerequisites\n\n- [ ] **Step 3: Generate Infrastructure as Code (IaC)**: Generate code (e.g.,\n  Terraform) and deployment scripts to automate the provisioning of the proposed\n  Google Cloud resources.\n\n- [ ] **Step 4: Write deployment instructions**: Draft sequential, step-by-step\n  deployment instructions to execute the IaC and initialize the workload\n  components.\n\n- [ ] **Step 5: Request review**: Present the generated deployment instructions\n  to the user for feedback and confirmation.\n\n- [ ] **Step 6: Iterate**: If the user requests changes, generate an updated\n  implementation plan and repeat steps 2-5 until the user approves the\n  implementation plan.\n\n### Phase 4: Solution validation\n\n- [ ] **Step 1: Retrieve relevant verification resources (optional)**: If the\n  resources from Phase 3 are not already in your context, retrieve the same\n  implementation resources as the starting point for the validation checks\n  and verification scripts that you generate in this phase.\n\n- [ ] **Step 2: Define validation checks**: Outline validation steps to verify\n  that the deployed infrastructure meets the workload requirements:\n   - **Deployment dry-run**: Commands like `terraform plan` to preview\n     changes.\n   - **Connectivity and routing**: Verification of network paths, load\n     balancer routing, and service endpoints.\n   - **Security policies**: Verification of restricted access, firewall\n     rules, and IAM enforcement.\n\n- [ ] **Step 3: Generate verification scripts**: Draft lightweight scripts or\n  command-line instructions (e.g. using `curl` or `gcloud`) that the user can\n  run to perform these validation checks.\n\n- [ ] **Step 4: Compile validation report**: Document the validation steps,\n  verification scripts, and expected outcomes in a single Markdown file.\n\n- [ ] **Step 5: Conduct validation and finalize**: Assist the user in executing\n  the validation checks and troubleshooting any deployment issues. After the\n  solution is validated successfully, request final approval from the user.\n\n- [ ] **Step 6: Iterate**: If the user requests changes, then generate an\n  updated validation plan and repeat steps 2-5 until the user approves the\n  validation plan.\n",{"name":5,"metadata":49,"description":7},{"version":50,"category":51},"1.0.1","MultiProductSolutions","main","bb9b3f464aae37af360d079c1d863524955b0363","skills\u002Fcloud\u002Fgoogle-cloud-solution-agentic-ai-borderless-data-lakehouse",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":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add google\u002Fskills --skill google-cloud-solution-agentic-ai-borderless-data-lakehouse",{"tool":67,"command":68},"bunx","bunx skills add google\u002Fskills --skill google-cloud-solution-agentic-ai-borderless-data-lakehouse",{"tool":70,"command":71},"pnpm","pnpm dlx skills add google\u002Fskills --skill google-cloud-solution-agentic-ai-borderless-data-lakehouse","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgoogle-cloud-solution-agentic-ai-borderless-data-lakehouse\u002FSKILL.md",true,[75,81,87,91,95,99],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"agents\u002Fopenai.yaml","REFERENCE","参考文档",43,"https:\u002F\u002Fgithub.com\u002FEthanYoQ\u002FSkill-hub\u002Fblob\u002Fmine\u002Farchives\u002Fuser-installed-skills\u002Ftavily-research\u002F32d5ebb055722a74\u002Ftavily-research\u002Fagents\u002Fopenai.yaml",{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"assets\u002Foutput-template.md","ASSET","资产文件",6606,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgoogle-cloud-solution-agentic-ai-borderless-data-lakehouse\u002Fassets\u002Foutput-template.md",{"path":88,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fattribution.md",2022,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgoogle-cloud-solution-agentic-ai-borderless-data-lakehouse\u002Freferences\u002Fattribution.md",{"path":92,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fdesign_recommendations.md",5045,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgoogle-cloud-solution-agentic-ai-borderless-data-lakehouse\u002Freferences\u002Fdesign_recommendations.md",{"path":96,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Fproduct_mapping.md",3833,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgoogle-cloud-solution-agentic-ai-borderless-data-lakehouse\u002Freferences\u002Fproduct_mapping.md",{"path":100,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Fproduct_renaming.md",1523,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgoogle-cloud-solution-agentic-ai-borderless-data-lakehouse\u002Freferences\u002Fproduct_renaming.md",[104,141,170,198,225,249],{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":108,"categories":109,"keywords":118,"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",[110,115],{"categoryNo":111,"categoryName":112,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",{"categoryNo":116,"categoryName":117,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000010","后端与接口",[9,119,120,121,122,123,124,125,126,26,127,128,129,130,131,132,133,134,135,136,137,138,139,140],"sign in with google web","sign","with","web","android","go","security","google workspace","implement","policy","python","implementing","react","iam","permissions","buttons","secure","manager","identity","architectures","domain","runtimes",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":154,"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",[147,148,151],{"categoryNo":116,"categoryName":117,"taxonomyType":113,"taxonomyTypeDesc":114},{"categoryNo":149,"categoryName":150,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":152,"categoryName":153,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000030","开发工程师",[155,156,9,26,157,158,159,160,161,162,132,163,164,165,166,167,168,137,140,169,32],"dpop adoption","adoption","es6","retry","authentication","flows","challenge","code","browser","loops","at","node","token","pair","platform",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":182,"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",[176,177,180,181],{"categoryNo":116,"categoryName":117,"taxonomyType":113,"taxonomyTypeDesc":114},{"categoryNo":178,"categoryName":179,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":149,"categoryName":150,"taxonomyType":113,"taxonomyTypeDesc":114},{"categoryNo":152,"categoryName":153,"taxonomyType":21,"taxonomyTypeDesc":22},[183,37,184,185,26,186,187,188,189,190,191,192,193,32,194,195,196,132,197,133],"retrieving developer knowledge","developer","MCP","mcp server","official","developer documentation","get","search","local","choice","documents","query","api","across","documentation",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":202,"categories":203,"keywords":204,"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",[],[205,206,207,208,209,210,211,212,129,213,214,215,216,217,218,191,219,220,221,32,222,223,196,224],"migrate to rust","rust","migrate","github","memory","performance","directory","safe","ports","llvm","soa","cycle","porting","fix","arrays","infographic","high-performance","URL","architecture","unrelated",{"skillNo":226,"name":227,"nameZh":6,"description":228,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":229,"categories":230,"keywords":234,"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",[231,232,233],{"categoryNo":116,"categoryName":117,"taxonomyType":113,"taxonomyTypeDesc":114},{"categoryNo":149,"categoryName":150,"taxonomyType":113,"taxonomyTypeDesc":114},{"categoryNo":152,"categoryName":153,"taxonomyType":21,"taxonomyTypeDesc":22},[9,235,41,236,237,169,238,239,240,241,242,159,243,244,245,192,246,247,32,248,195],"finding google skills","as","messaging","model","trade-offs","provider","answer","fit","catalog","up","decision","use","database","candidate",{"skillNo":250,"name":251,"nameZh":6,"description":252,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":253,"categories":254,"keywords":257,"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",[255,256],{"categoryNo":116,"categoryName":117,"taxonomyType":113,"taxonomyTypeDesc":114},{"categoryNo":149,"categoryName":150,"taxonomyType":113,"taxonomyTypeDesc":114},[258,259,136,260,9,185,261,26,186,262,263,264,265,32,266,195,132,267,268,269,270],"workload manager basics","workload","basics","cli","review","vpc","resources","inspect","you","create","libraries","server","run"]