[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7D7D8FNWVT3QDDFRF7T3VR0":3,"skill-same:01M7D7D8FNWVT3QDDFRF7T3VR0":79},{"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":16,"sourceHtmlUrl":17,"categories":18,"keywords":19,"publishedAt":20,"linkOnly":21,"markdownContent":22,"markdownBody":23,"frontMatter":24,"refName":28,"commitSha":29,"skillDirectory":5,"forksCount":30,"repositoryHtmlUrl":31,"repositoryHomepageUrl":32,"ownerHtmlUrl":33,"ownerBlogUrl":34,"ownerDescription":35,"updatedAt":36,"install":37,"resources":50},"01M7D7D8FNWVT3QDDFRF7T3VR0","google-cloud-solution-agentic-ai-data-science-workflow",null,"Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.","tangyixiao\u002Ftangyixiao-skills","tangyixiao","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F115929632?v=4","01M7D582YK6A4M63CAK4CSY4EW","Tang Yixiao","01M7D582Z64DMRSYR872SR34SA",2,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fgoogle-cloud-solution-agentic-ai-data-science-workflow\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fgoogle-cloud-solution-agentic-ai-data-science-workflow\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: google-cloud-solution-agentic-ai-data-science-workflow\nmetadata:\n  version: 1.0.3\n  category: MultiProductSolutions\ndescription: Designs a tailored multi-product agentic data science architecture on\n  Google Cloud that incorporates opinionated best practices. Use when architecting\n  multi-product solutions for agent-based data analytics or ML workloads. Don't use\n  for simple queries, non-agentic pipelines, general cloud reviews, or writing agent\n  code.\n---\n\n# Data science workflow with AI agents solution\n\nThis skill guides agents through the workflow to design and implement a\ntailored multi-product solution in the cloud for a given workload, use case, or\nrequirement.\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## 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 and instructions to\n  deploy the solution.\n- **Phase 4: Solution validation**: Validate that the deployment meets the\n  requirements of the workload.\n\n## Product Renaming & Terminology\n\nWhen generating solution designs, architecture diagrams, and documentation,\ncheck the latest Google Cloud documentation for the most up-to-date product\nnames. The table below provides examples of name mappings to be aware of. Note\nthat underlying APIs, Terraform resources, and IAM roles may retain their legacy\nidentifiers.\n\n| Legacy Name | Updated Name |\n| :--- | :--- |\n| Vertex AI | Gemini Enterprise Agent Platform |\n| Vertex AI Agent Engine | Agent Runtime |\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 by asking clarifying questions. You must halt and wait for the user\n  to answer these questions before proceeding to the **Identify components**\n  step. Use the following questions to guide this requirements discovery\n  process:\n   - What data sources and data types do you need to access and analyze?\n   - Who are the target end users, and what network access model do you require?\n   - What types of user queries or analytical requests do you expect end users\n     to submit to the system?\n   - What performance, security, or governance constraints apply?\n\n- [ ] **Step 2: Identify components**: Only after the user has responded to the\n  clarifying questions in the **Discover requirements** step, analyze their\n  responses to identify the components of the workload and their relationships.\n  Also identify any cross-cloud, hybrid, or on-premises components that the\n  solution needs to integrate with.\n\n- [ ] **Step 3: Generate component decomposition**: Generate a technical\n  decomposition outlining the technical components of the workload and their\n  relationships.\n\n- [ ] **Step 4: Ask for confirmation**: Present the technical decomposition and\n  ask the user to confirm if it matches their workload requirements. Do not\n  proceed to Phase 2 until this is confirmed.\n\n- [ ] **Step 5: Iterate**: If the user requests changes, generate an updated\n   technical decomposition and ask for confirmation again. Continue iterating\n   until the user explicitly confirms the 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 steps\n  of this phase before proceeding.\n   - [Data science workflow with AI agents](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fagentic-ai-data-science.md.txt)\n   - [Multi-agent AI system in Google Cloud](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fmultiagent-ai-system.md.txt)\n   - [Choose your agentic AI architecture components](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fchoose-agentic-ai-architecture-components.md.txt)\n   - [Choose a design pattern for your agentic AI system](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fchoose-design-pattern-agentic-ai-system.md.txt)\n\n- [ ] **Step 2: Define agentic AI design pattern**: Select the appropriate agent\n  design pattern and agent breakdown based on the workload requirements:\n   - **Recommended primary pattern**: Coordinator pattern.\n   - **Alternative patterns**:\n     - *Single-agent pattern*: For simpler workloads scoped to a single data\n       source and direct tool use without multi-agent orchestration overhead.\n     - *Sequential or parallel pattern*: For deterministic data processing\n       pipelines with predefined, non-adaptive execution steps or concurrent\n       data gathering.\n     - *Review and critique pattern*: For complex or high-stakes data science\n       tasks that require dedicated critic loops.\n\n- [ ] **Step 3: Map components to Google Cloud products**: For each component in\n  the confirmed technical decomposition and agentic design pattern, identify the\n  appropriate Google Cloud products and features, based on the guidelines in\n  \u002Freferences\u002Fproduct-mapping.md.\n\n- [ ] **Step 4: 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 use component labels and groupings consistent with the\n     official Google Cloud architecture icons.\n\n- [ ] **Step 5: Generate design recommendations**: Generate design guidance\n  based on the guidelines in \u002Freferences\u002Fdesign-recommendations.md.\n\n- [ ] **Step 6: 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   `\u002Fassets\u002Foutput-template.md`.\n\n- [ ] **Step 7: Request review**: Present the generated solution architecture to\n  the user and request their feedback or approval. You must halt and wait for\n  the user's explicit approval before proceeding to Phase 3.\n\n- [ ] **Step 8: Iterate**: If the user requests changes, then generate an\n  updated solution architecture and repeat steps 2-7 in this phase until the\n  user explicitly approves the solution architecture.\n\n### Phase 3: Implementation plan\n\n- [ ] **Step 1: Retrieve relevant implementation resources**:\n   - [Stateful Data Science Agent on Agent Engine](https:\u002F\u002Fcodelabs.developers.google.com\u002Fnext26\u002Fadk-deploy-scale)\n   - [Build and deploy an AI agent to Cloud Run using ADK](https:\u002F\u002Fdocs.cloud.google.com\u002Frun\u002Fdocs\u002Fai\u002Fbuild-and-deploy-ai-agents\u002Fdeploy-adk-agent.md.txt)\n   - [Use AlloyDB with agents](https:\u002F\u002Fdocs.cloud.google.com\u002Falloydb\u002Fdocs\u002Fconnect-ide-using-mcp-toolbox.md.txt)\n   - [MCP Toolbox for Databases Configuration](https:\u002F\u002Fmcp-toolbox.dev\u002Fdocumentation\u002Fconfiguration\u002F)\n   - [Agents CLI reference](https:\u002F\u002Fgoogle.github.io\u002Fagents-cli\u002Fcli\u002F)\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, such as\n  Terraform, and deployment scripts to automate the provisioning of the proposed\n  Google Cloud resources.\n  -   Use Agents CLI commands (`agents-cli scaffold\n  create` or `agents-cli scaffold enhance`) to set up the agent application\n  structure, and deploy the agent to Cloud Run.\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. Update deployment instructions in\n  `solution-architecture-guide.md`, based on the template in\n  `assets\u002Foutput-template.md`.\n  -   Use the Agents CLI `agents-cli deploy` command (alongside or instead of\n      raw infrastructure\u002Fdeployment scripts) to run the agent deployment.\n\n- [ ] **Step 5: Request review**: Present the generated deployment instructions\n  to the user for feedback and confirmation. You must halt and wait for the\n  user's explicit approval before proceeding to Phase 4.\n\n- [ ] **Step 6: Iterate**: If the user requests changes, then repeat steps 2-5\n  to generate an updated implementation plan that the user requested.\n\n- [ ] **Step 7: Proceed to the next phase**: After the user approves the\n  implementation plan, proceed to Phase 4.\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\n  validation checks 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's requirements:\n  -   **Deployment dry-run**: Commands like `terraform plan` to preview\n    changes. Include instructions to run agent deployment in dry-run mode\n    (e.g., using `agents-cli deploy --dry-run` or `-n`) to preview steps and\n    Terraform executions before pushing to production.\n  -   **Local testing and quality verification**: Recommend using the Agents\n      CLI to run and test agent logic locally (`agents-cli run`) and conduct\n      systematic evaluations (`agents-cli eval run`) to verify agent quality\n      and performance before deploying.\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`, `gcloud`, or `agents-cli`)\n  that the user can run to perform these validation checks.\n\n  -   The validation plan MUST include instructions using the Agents CLI for\n      local runs, evaluations, and post-deployment validation checks (e.g.,\n      `agents-cli run --url \u003Cservice-url>` to test the deployed service\n      endpoint).\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 you\n  validate the solution 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 the validation drafting and script\n  generation steps in this phase until the user approves the validation plan.\n","# Data science workflow with AI agents solution\n\nThis skill guides agents through the workflow to design and implement a\ntailored multi-product solution in the cloud for a given workload, use case, or\nrequirement.\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## 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 and instructions to\n  deploy the solution.\n- **Phase 4: Solution validation**: Validate that the deployment meets the\n  requirements of the workload.\n\n## Product Renaming & Terminology\n\nWhen generating solution designs, architecture diagrams, and documentation,\ncheck the latest Google Cloud documentation for the most up-to-date product\nnames. The table below provides examples of name mappings to be aware of. Note\nthat underlying APIs, Terraform resources, and IAM roles may retain their legacy\nidentifiers.\n\n| Legacy Name | Updated Name |\n| :--- | :--- |\n| Vertex AI | Gemini Enterprise Agent Platform |\n| Vertex AI Agent Engine | Agent Runtime |\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 by asking clarifying questions. You must halt and wait for the user\n  to answer these questions before proceeding to the **Identify components**\n  step. Use the following questions to guide this requirements discovery\n  process:\n   - What data sources and data types do you need to access and analyze?\n   - Who are the target end users, and what network access model do you require?\n   - What types of user queries or analytical requests do you expect end users\n     to submit to the system?\n   - What performance, security, or governance constraints apply?\n\n- [ ] **Step 2: Identify components**: Only after the user has responded to the\n  clarifying questions in the **Discover requirements** step, analyze their\n  responses to identify the components of the workload and their relationships.\n  Also identify any cross-cloud, hybrid, or on-premises components that the\n  solution needs to integrate with.\n\n- [ ] **Step 3: Generate component decomposition**: Generate a technical\n  decomposition outlining the technical components of the workload and their\n  relationships.\n\n- [ ] **Step 4: Ask for confirmation**: Present the technical decomposition and\n  ask the user to confirm if it matches their workload requirements. Do not\n  proceed to Phase 2 until this is confirmed.\n\n- [ ] **Step 5: Iterate**: If the user requests changes, generate an updated\n   technical decomposition and ask for confirmation again. Continue iterating\n   until the user explicitly confirms the 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 steps\n  of this phase before proceeding.\n   - [Data science workflow with AI agents](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fagentic-ai-data-science.md.txt)\n   - [Multi-agent AI system in Google Cloud](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fmultiagent-ai-system.md.txt)\n   - [Choose your agentic AI architecture components](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fchoose-agentic-ai-architecture-components.md.txt)\n   - [Choose a design pattern for your agentic AI system](https:\u002F\u002Fdocs.cloud.google.com\u002Farchitecture\u002Fchoose-design-pattern-agentic-ai-system.md.txt)\n\n- [ ] **Step 2: Define agentic AI design pattern**: Select the appropriate agent\n  design pattern and agent breakdown based on the workload requirements:\n   - **Recommended primary pattern**: Coordinator pattern.\n   - **Alternative patterns**:\n     - *Single-agent pattern*: For simpler workloads scoped to a single data\n       source and direct tool use without multi-agent orchestration overhead.\n     - *Sequential or parallel pattern*: For deterministic data processing\n       pipelines with predefined, non-adaptive execution steps or concurrent\n       data gathering.\n     - *Review and critique pattern*: For complex or high-stakes data science\n       tasks that require dedicated critic loops.\n\n- [ ] **Step 3: Map components to Google Cloud products**: For each component in\n  the confirmed technical decomposition and agentic design pattern, identify the\n  appropriate Google Cloud products and features, based on the guidelines in\n  \u002Freferences\u002Fproduct-mapping.md.\n\n- [ ] **Step 4: 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 use component labels and groupings consistent with the\n     official Google Cloud architecture icons.\n\n- [ ] **Step 5: Generate design recommendations**: Generate design guidance\n  based on the guidelines in \u002Freferences\u002Fdesign-recommendations.md.\n\n- [ ] **Step 6: 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   `\u002Fassets\u002Foutput-template.md`.\n\n- [ ] **Step 7: Request review**: Present the generated solution architecture to\n  the user and request their feedback or approval. You must halt and wait for\n  the user's explicit approval before proceeding to Phase 3.\n\n- [ ] **Step 8: Iterate**: If the user requests changes, then generate an\n  updated solution architecture and repeat steps 2-7 in this phase until the\n  user explicitly approves the solution architecture.\n\n### Phase 3: Implementation plan\n\n- [ ] **Step 1: Retrieve relevant implementation resources**:\n   - [Stateful Data Science Agent on Agent Engine](https:\u002F\u002Fcodelabs.developers.google.com\u002Fnext26\u002Fadk-deploy-scale)\n   - [Build and deploy an AI agent to Cloud Run using ADK](https:\u002F\u002Fdocs.cloud.google.com\u002Frun\u002Fdocs\u002Fai\u002Fbuild-and-deploy-ai-agents\u002Fdeploy-adk-agent.md.txt)\n   - [Use AlloyDB with agents](https:\u002F\u002Fdocs.cloud.google.com\u002Falloydb\u002Fdocs\u002Fconnect-ide-using-mcp-toolbox.md.txt)\n   - [MCP Toolbox for Databases Configuration](https:\u002F\u002Fmcp-toolbox.dev\u002Fdocumentation\u002Fconfiguration\u002F)\n   - [Agents CLI reference](https:\u002F\u002Fgoogle.github.io\u002Fagents-cli\u002Fcli\u002F)\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, such as\n  Terraform, and deployment scripts to automate the provisioning of the proposed\n  Google Cloud resources.\n  -   Use Agents CLI commands (`agents-cli scaffold\n  create` or `agents-cli scaffold enhance`) to set up the agent application\n  structure, and deploy the agent to Cloud Run.\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. Update deployment instructions in\n  `solution-architecture-guide.md`, based on the template in\n  `assets\u002Foutput-template.md`.\n  -   Use the Agents CLI `agents-cli deploy` command (alongside or instead of\n      raw infrastructure\u002Fdeployment scripts) to run the agent deployment.\n\n- [ ] **Step 5: Request review**: Present the generated deployment instructions\n  to the user for feedback and confirmation. You must halt and wait for the\n  user's explicit approval before proceeding to Phase 4.\n\n- [ ] **Step 6: Iterate**: If the user requests changes, then repeat steps 2-5\n  to generate an updated implementation plan that the user requested.\n\n- [ ] **Step 7: Proceed to the next phase**: After the user approves the\n  implementation plan, proceed to Phase 4.\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\n  validation checks 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's requirements:\n  -   **Deployment dry-run**: Commands like `terraform plan` to preview\n    changes. Include instructions to run agent deployment in dry-run mode\n    (e.g., using `agents-cli deploy --dry-run` or `-n`) to preview steps and\n    Terraform executions before pushing to production.\n  -   **Local testing and quality verification**: Recommend using the Agents\n      CLI to run and test agent logic locally (`agents-cli run`) and conduct\n      systematic evaluations (`agents-cli eval run`) to verify agent quality\n      and performance before deploying.\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`, `gcloud`, or `agents-cli`)\n  that the user can run to perform these validation checks.\n\n  -   The validation plan MUST include instructions using the Agents CLI for\n      local runs, evaluations, and post-deployment validation checks (e.g.,\n      `agents-cli run --url \u003Cservice-url>` to test the deployed service\n      endpoint).\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 you\n  validate the solution 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 the validation drafting and script\n  generation steps in this phase until the user approves the validation plan.\n",{"name":5,"metadata":25,"description":7},{"version":26,"category":27},"1.0.3","MultiProductSolutions","main","2a870f189175a48c39f5f7e26a6d93b0be9cd13b",0,"https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills","https:\u002F\u002Ftangyixiao.github.io\u002Ftangyixiao-skills\u002F","https:\u002F\u002Fgithub.com\u002Ftangyixiao","http:\u002F\u002Fwww.sxyz.net\u002F","I am Tang Yixiao, a student in Shaoxing No.1 High School. I am eager to learn on GitHub and contribute my part to this community.","2026-11-11 15:45:59",{"source":8,"skillName":5,"commands":38,"prompt":48,"zipAvailable":49},[39,42,45],{"tool":40,"command":41},"npx","npx skills add tangyixiao\u002Ftangyixiao-skills --skill google-cloud-solution-agentic-ai-data-science-workflow",{"tool":43,"command":44},"bunx","bunx skills add tangyixiao\u002Ftangyixiao-skills --skill google-cloud-solution-agentic-ai-data-science-workflow",{"tool":46,"command":47},"pnpm","pnpm dlx skills add tangyixiao\u002Ftangyixiao-skills --skill google-cloud-solution-agentic-ai-data-science-workflow","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fgoogle-cloud-solution-agentic-ai-data-science-workflow\u002FSKILL.md",true,[51,57,63,67,71,75],{"path":52,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":55,"sourceHtmlUrl":56},"assets\u002Foutput-template.md","ASSET","资产文件",7924,"https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fgoogle-cloud-solution-agentic-ai-data-science-workflow\u002Fassets\u002Foutput-template.md",{"path":58,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":61,"sourceHtmlUrl":62},"INSTALL-SOURCE.json","REFERENCE","参考文档",622,"https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fgoogle-cloud-solution-agentic-ai-data-science-workflow\u002FINSTALL-SOURCE.json",{"path":64,"resourceType":64,"resourceTypeDesc":65,"sizeBytes":66,"sourceHtmlUrl":16},"LICENSE","许可证",11358,{"path":68,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":69,"sourceHtmlUrl":70},"references\u002Fattribution.md",2010,"https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fgoogle-cloud-solution-agentic-ai-data-science-workflow\u002Freferences\u002Fattribution.md",{"path":72,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":73,"sourceHtmlUrl":74},"references\u002Fdesign-recommendations.md",4902,"https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fgoogle-cloud-solution-agentic-ai-data-science-workflow\u002Freferences\u002Fdesign-recommendations.md",{"path":76,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Fproduct-mapping.md",3164,"https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fgoogle-cloud-solution-agentic-ai-data-science-workflow\u002Freferences\u002Fproduct-mapping.md",[80,88,97,105,113,121],{"skillNo":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":84,"sourceHtmlUrl":85,"categories":86,"keywords":87,"publishedAt":20},"01M7D8JK8FYBFNNAJXWD89CFH7","xlsx","Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fxlsx\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fxlsx\u002FSKILL.md",[],[],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":93,"sourceHtmlUrl":94,"categories":95,"keywords":96,"publishedAt":20},"01M7D8JG8ERGGQQAMX0DWCG9GR","writing-shape","Writing, exploit: shape raw material into an article, paragraph by paragraph.","MIT","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-shape\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-shape\u002FSKILL.md",[],[],{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":101,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":20},"01M7D8JE672GW3EHSE8EF185M4","writing-fragments","Writing, explore: mine raw fragments, no structure yet.","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-fragments\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-fragments\u002FSKILL.md",[],[],{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":109,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":20},"01M7D8JBK572YDFMND8Q6CEYJW","writing-for-agents","Writing documents for agents. Use when creating or editing skills, or modifying AGENTS.md or CLAUDE.md.","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-for-agents\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-for-agents\u002FSKILL.md",[],[],{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":117,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":20},"01M7D8J5QPWEXX61EGBKYH73R8","writing-clearly-and-concisely","Use when writing prose humans will read—documentation, commit messages, error messages, explanations, reports, or UI text. Applies Strunk's timeless rules for clearer, stronger, more professional writing.","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-clearly-and-concisely\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-clearly-and-concisely\u002FSKILL.md",[],[],{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":125,"sourceHtmlUrl":126,"categories":127,"keywords":128,"publishedAt":20},"01M7D8J36YWC8WZS5DK5AH03CD","writing-beats","Writing, exploit; assemble raw material into a journey of beats, grounding each term before a beat leans on it.","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-beats\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Ftangyixiao\u002Ftangyixiao-skills\u002Fblob\u002Fmain\u002Fwriting-beats\u002FSKILL.md",[],[]]