[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6991MH38TD2VKSS81CV5MXR":3,"skill-same:01M6991MH38TD2VKSS81CV5MXR":84},{"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":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":6,"ownerHtmlUrl":56,"ownerBlogUrl":57,"ownerDescription":58,"updatedAt":59,"install":60,"resources":73},"01M6991MH38TD2VKSS81CV5MXR","datalineage-bigquery-asset-impact-analysis",null,"Analyzes the downstream impact (blast radius) using data lineage on Google Cloud when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don't use for: - General BigQuery querying or data analysis (use BigQuery-related tools instead). - Non-BigQuery assets (e.g., Cloud Storage files) unless they are part of the BigQuery lineage. - Creating or modifying lineage links directly.","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\u002Fdatalineage-bigquery-asset-impact-analysis\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,9],"datalineage bigquery asset impact analysis","bigquery","asset","analysis","impact","impact analysis","broken","when","downstream impact","updates","affected","Cloud Storage","files","data analysis","radius","data","lineage","storage","google cloud","2026-11-10 10:12:30",false,"---\nname: datalineage-bigquery-asset-impact-analysis\nmetadata:\n  version: \"1.0.2\"\n  category: BigDataAndAnalytics\ndescription: >-\n  Analyzes the downstream impact (blast radius) using data lineage on Google Cloud when a BigQuery table or view is broken, stale, or modified.\n  Identifies all downstream tables, dashboards, and processes that will be affected.\n  Use when:\n  - Performing a blast radius or impact analysis for a BigQuery table or view.\n  - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset.\n  - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset.\n  Don't use for:\n  - General BigQuery querying or data analysis (use BigQuery-related tools instead).\n  - Non-BigQuery assets (e.g., Cloud Storage files) unless they are part of the BigQuery lineage.\n  - Creating or modifying lineage links directly.\n---\n\n# BigQuery Asset Impact Analysis\n\nThis skill guides the agent in performing a downstream impact analysis (blast\nradius assessment) when a BigQuery table or view is reported as broken, stale,\nmissing, or when a user is planning maintenance and wants to know the\nconsequences of modifying or pausing updates to an asset.\n\nIt relies primarily on the **data lineage (Knowledge Catalog) MCP Server** on\nGoogle Cloud to discover relationships between assets.\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:datalineage-bigquery-asset-impact-analysis)\"`\nand append `agent-skills\u002F1.0 (skill:datalineage-bigquery-asset-impact-analysis)` to `User-Agent` headers.\n\n## Prerequisites\n\nThis skill requires access to the data lineage API on Google Cloud and an active\nclient connection to the Data Lineage MCP Server. For detailed connection\nconfigurations and tool schemas, refer to [MCP Usage](references\u002Fmcp-usage.md).\n\n## Analysis Workflow\n\n### 1. Resolve the Asset's Fully Qualified Name (FQN)\n\n*   Ensure you have the correct FQN format for the BigQuery asset:\n    *   *Format:* `bigquery:{project_id}.{dataset_id}.{table_or_view_id}`\n    *   *Example:* `bigquery:my-prod-project.analytics.orders`\n\n\n### 2. Determine Locations and Parent Path\n\nIdentify the locations to search and construct the Data Lineage API request:\n\n*   **Discover Asset Location**: Run the command `bq show --format=json\n    {project_id}:{dataset_id}` and extract the `location` field (e.g.,\n    `us-central1` or `us`). If location discovery fails due to permissions or\n    missing tools, prompt the user for the dataset's location.\n*   **Set Parent Path**: Set the `parent` path using the project ID and the\n    MCP server's location. Consult the `DataLineageServer` tool definition\n    to find the configured region or location (e.g., `us`). The format is:\n    `projects\u002F{project_id}\u002Flocations\u002F{mcp_server_location}`.\n*   **Configure Search Scope**: Include the discovered asset location in the\n    `locations` array of the payload (e.g., `[\"us-central1\"]` or `[\"us\",\n    \"us-central1\"]`).\n\n### 3. Retrieve the Downstream Lineage Graph\n\nCall the `DataLineageServer:search_lineage` tool to fetch downstream\nrelationships.\n\n*   **Direction**: Set to `DOWNSTREAM`.\n*   **Search Parameters**: Use `max_depth = 10` and `max_process_per_link = 5`\n    as robust defaults.\n\n### 4. Identify the Blast Radius\n\nTraverse the returned lineage links to build the impact graph:\n\n*   **Affected Assets**: The `target` of each link represents a downstream asset\n    that depends on your source asset.\n*   **Transform Processes**: Inspect the `processes` field on each link. This\n    identifies the ETL pipelines, BigQuery Views, or Scheduled Queries that\n    propagate the data.\n*   **Direct vs. Indirect Impact**:\n    *   **Direct Impact (Depth 1)**: Assets directly consuming the source asset.\n        If a link has `dependency_type: EXACT_COPY`, mark the target as\n        \"Directly Stale \u002F Identical Copy\".\n    *   **Indirect Impact (Depth > 1)**: Assets further down the stream that\n        will experience cascading stale data or failures.\n\n### 5. Summarize and Format the Output\n\nPresent your findings clearly to the user using the following structure:\n\n1.  **Executive Summary**: State the total number of downstream assets affected\n    and the maximum depth of the impact.\n2.  **Critical Path**: Highlight high-priority downstream assets (e.g., assets\n    containing \"prod\", \"dashboard\", \"reporting\", or \"master\" in their names).\n3.  **Blast Radius Table**: A clean Markdown table listing the dependencies. You\n    MUST include all columns:\n\n    | Downstream Asset                 | Transform Process                     | Depth | Impact Type |\n    | :------------------------------- | :------------------------------------ | :---- | :---------- |\n    | `bigquery:project.dataset.table` | `projects\u002Fp\u002Flocations\u002Fl\u002Fprocesses\u002Fproc` | 1     | Direct      |\n    | `bigquery:project.dataset.view`  | `projects\u002Fp\u002Flocations\u002Fl\u002Fprocesses\u002Fview` | 2     | Indirect    |\n4.  **Analysis Metadata**: Provide transparency on the parameters and boundaries\n    of your search so the user can choose to expand them:\n    *   **Locations Searched**: `{list_of_locations_queried}`\n    *   **Parent Location**: `{parent_path}`\n    *   **Depth Limit**: `{max_depth}`\n    *   **Process per Link Limit**: `{max_process_per_link}`\n    *   *Tip for User*: Let the user know they can request to rerun the analysis\n        with expanded locations or larger depth limits.\n\n## Crucial Constraints & Guardrails\n\n1.  **Interpret Empty Responses Correctly**:\n    *   If the lineage response is empty, immediately assume that no\n        dependencies exist in the queried locations and report this to the\n        user.\n2.  **Strictly Banned Bypasses**:\n    *   Exclusively retrieve downstream relationships using the\n        `DataLineageServer:search_lineage` tool.\n3.  **Verify Asset Existence First**:\n    *   If `bq show` indicates the source table does not exist, stop and report\n        this directly to the user. Do not attempt to guess alternative table\n        names unless the user explicitly instructs you to do so.\n4.  **No Output Shortcutting or Hallucinated Artifacts**:\n    *   Present the complete downstream blast radius table directly in your\n        final response. Avoid telling the user you have created a separate\n        Markdown file or artifact containing the details unless you have\n        explicitly executed file-writing tools to create it.\n\n## Reference Directory\n\n-   [MCP Usage](references\u002Fmcp-usage.md): Using the data lineage remote MCP\n    server on Google Cloud and tool preferences.\n\n## External Documentation\n\n-   [Knowledge Catalog Data Lineage Documentation](https:\u002F\u002Fcloud.google.com\u002Fdataplex\u002Fdocs\u002Fabout-data-lineage)\n-   [Use the Data Lineage MCP server](https:\u002F\u002Fdocs.cloud.google.com\u002Fdataplex\u002Fdocs\u002Fuse-lineage-mcp)\n-   [Knowledge Catalog Data Lineage API Reference](https:\u002F\u002Fcloud.google.com\u002Fdataplex\u002Fdocs\u002Freference\u002Fdata-lineage\u002Frest)\n","# BigQuery Asset Impact Analysis\n\nThis skill guides the agent in performing a downstream impact analysis (blast\nradius assessment) when a BigQuery table or view is reported as broken, stale,\nmissing, or when a user is planning maintenance and wants to know the\nconsequences of modifying or pausing updates to an asset.\n\nIt relies primarily on the **data lineage (Knowledge Catalog) MCP Server** on\nGoogle Cloud to discover relationships between assets.\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:datalineage-bigquery-asset-impact-analysis)\"`\nand append `agent-skills\u002F1.0 (skill:datalineage-bigquery-asset-impact-analysis)` to `User-Agent` headers.\n\n## Prerequisites\n\nThis skill requires access to the data lineage API on Google Cloud and an active\nclient connection to the Data Lineage MCP Server. For detailed connection\nconfigurations and tool schemas, refer to [MCP Usage](references\u002Fmcp-usage.md).\n\n## Analysis Workflow\n\n### 1. Resolve the Asset's Fully Qualified Name (FQN)\n\n*   Ensure you have the correct FQN format for the BigQuery asset:\n    *   *Format:* `bigquery:{project_id}.{dataset_id}.{table_or_view_id}`\n    *   *Example:* `bigquery:my-prod-project.analytics.orders`\n\n\n### 2. Determine Locations and Parent Path\n\nIdentify the locations to search and construct the Data Lineage API request:\n\n*   **Discover Asset Location**: Run the command `bq show --format=json\n    {project_id}:{dataset_id}` and extract the `location` field (e.g.,\n    `us-central1` or `us`). If location discovery fails due to permissions or\n    missing tools, prompt the user for the dataset's location.\n*   **Set Parent Path**: Set the `parent` path using the project ID and the\n    MCP server's location. Consult the `DataLineageServer` tool definition\n    to find the configured region or location (e.g., `us`). The format is:\n    `projects\u002F{project_id}\u002Flocations\u002F{mcp_server_location}`.\n*   **Configure Search Scope**: Include the discovered asset location in the\n    `locations` array of the payload (e.g., `[\"us-central1\"]` or `[\"us\",\n    \"us-central1\"]`).\n\n### 3. Retrieve the Downstream Lineage Graph\n\nCall the `DataLineageServer:search_lineage` tool to fetch downstream\nrelationships.\n\n*   **Direction**: Set to `DOWNSTREAM`.\n*   **Search Parameters**: Use `max_depth = 10` and `max_process_per_link = 5`\n    as robust defaults.\n\n### 4. Identify the Blast Radius\n\nTraverse the returned lineage links to build the impact graph:\n\n*   **Affected Assets**: The `target` of each link represents a downstream asset\n    that depends on your source asset.\n*   **Transform Processes**: Inspect the `processes` field on each link. This\n    identifies the ETL pipelines, BigQuery Views, or Scheduled Queries that\n    propagate the data.\n*   **Direct vs. Indirect Impact**:\n    *   **Direct Impact (Depth 1)**: Assets directly consuming the source asset.\n        If a link has `dependency_type: EXACT_COPY`, mark the target as\n        \"Directly Stale \u002F Identical Copy\".\n    *   **Indirect Impact (Depth > 1)**: Assets further down the stream that\n        will experience cascading stale data or failures.\n\n### 5. Summarize and Format the Output\n\nPresent your findings clearly to the user using the following structure:\n\n1.  **Executive Summary**: State the total number of downstream assets affected\n    and the maximum depth of the impact.\n2.  **Critical Path**: Highlight high-priority downstream assets (e.g., assets\n    containing \"prod\", \"dashboard\", \"reporting\", or \"master\" in their names).\n3.  **Blast Radius Table**: A clean Markdown table listing the dependencies. You\n    MUST include all columns:\n\n    | Downstream Asset                 | Transform Process                     | Depth | Impact Type |\n    | :------------------------------- | :------------------------------------ | :---- | :---------- |\n    | `bigquery:project.dataset.table` | `projects\u002Fp\u002Flocations\u002Fl\u002Fprocesses\u002Fproc` | 1     | Direct      |\n    | `bigquery:project.dataset.view`  | `projects\u002Fp\u002Flocations\u002Fl\u002Fprocesses\u002Fview` | 2     | Indirect    |\n4.  **Analysis Metadata**: Provide transparency on the parameters and boundaries\n    of your search so the user can choose to expand them:\n    *   **Locations Searched**: `{list_of_locations_queried}`\n    *   **Parent Location**: `{parent_path}`\n    *   **Depth Limit**: `{max_depth}`\n    *   **Process per Link Limit**: `{max_process_per_link}`\n    *   *Tip for User*: Let the user know they can request to rerun the analysis\n        with expanded locations or larger depth limits.\n\n## Crucial Constraints & Guardrails\n\n1.  **Interpret Empty Responses Correctly**:\n    *   If the lineage response is empty, immediately assume that no\n        dependencies exist in the queried locations and report this to the\n        user.\n2.  **Strictly Banned Bypasses**:\n    *   Exclusively retrieve downstream relationships using the\n        `DataLineageServer:search_lineage` tool.\n3.  **Verify Asset Existence First**:\n    *   If `bq show` indicates the source table does not exist, stop and report\n        this directly to the user. Do not attempt to guess alternative table\n        names unless the user explicitly instructs you to do so.\n4.  **No Output Shortcutting or Hallucinated Artifacts**:\n    *   Present the complete downstream blast radius table directly in your\n        final response. Avoid telling the user you have created a separate\n        Markdown file or artifact containing the details unless you have\n        explicitly executed file-writing tools to create it.\n\n## Reference Directory\n\n-   [MCP Usage](references\u002Fmcp-usage.md): Using the data lineage remote MCP\n    server on Google Cloud and tool preferences.\n\n## External Documentation\n\n-   [Knowledge Catalog Data Lineage Documentation](https:\u002F\u002Fcloud.google.com\u002Fdataplex\u002Fdocs\u002Fabout-data-lineage)\n-   [Use the Data Lineage MCP server](https:\u002F\u002Fdocs.cloud.google.com\u002Fdataplex\u002Fdocs\u002Fuse-lineage-mcp)\n-   [Knowledge Catalog Data Lineage API Reference](https:\u002F\u002Fcloud.google.com\u002Fdataplex\u002Fdocs\u002Freference\u002Fdata-lineage\u002Frest)\n",{"name":5,"metadata":48,"description":7},{"version":49,"category":50},"1.0.2","BigDataAndAnalytics","main","bb9b3f464aae37af360d079c1d863524955b0363","skills\u002Fcloud\u002Fdatalineage-bigquery-asset-impact-analysis",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":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add google\u002Fskills --skill datalineage-bigquery-asset-impact-analysis",{"tool":66,"command":67},"bunx","bunx skills add google\u002Fskills --skill datalineage-bigquery-asset-impact-analysis",{"tool":69,"command":70},"pnpm","pnpm dlx skills add google\u002Fskills --skill datalineage-bigquery-asset-impact-analysis","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fdatalineage-bigquery-asset-impact-analysis\u002FSKILL.md",true,[74,80],{"path":75,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fattribution.md","REFERENCE","参考文档",1966,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fdatalineage-bigquery-asset-impact-analysis\u002Freferences\u002Fattribution.md",{"path":81,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fmcp-usage.md",1243,"https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fdatalineage-bigquery-asset-impact-analysis\u002Freferences\u002Fmcp-usage.md",[85,120,151,180,207,231],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":97,"publishedAt":43},"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",[91,94],{"categoryNo":92,"categoryName":93,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[9,98,99,100,101,102,103,104,105,42,106,107,108,109,110,111,112,113,114,115,116,117,118,119],"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":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":135,"publishedAt":43},"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",[126,127,130],{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":128,"categoryName":129,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":131,"categoryName":132,"taxonomyType":133,"taxonomyTypeDesc":134},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[136,137,9,42,138,139,140,141,142,143,111,144,145,146,147,148,149,116,119,150,31],"dpop adoption","adoption","es6","retry","authentication","flows","challenge","code","browser","loops","at","node","token","pair","platform",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":163,"publishedAt":43},"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",[157,158,161,162],{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":159,"categoryName":160,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":128,"categoryName":129,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":131,"categoryName":132,"taxonomyType":133,"taxonomyTypeDesc":134},[164,165,166,167,42,168,169,170,171,172,173,174,175,31,176,177,178,111,179,112],"retrieving developer knowledge","knowledge","developer","MCP","mcp server","official","developer documentation","get","search","local","choice","documents","query","api","across","documentation",{"skillNo":181,"name":182,"nameZh":6,"description":183,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":184,"categories":185,"keywords":186,"publishedAt":43},"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",[],[187,188,189,190,191,192,193,194,108,195,196,197,198,199,200,173,201,202,203,31,204,205,178,206],"migrate to rust","rust","migrate","github","memory","performance","directory","safe","ports","llvm","soa","cycle","porting","fix","arrays","infographic","high-performance","URL","architecture","unrelated",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":211,"categories":212,"keywords":216,"publishedAt":43},"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",[213,214,215],{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":128,"categoryName":129,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":131,"categoryName":132,"taxonomyType":133,"taxonomyTypeDesc":134},[9,217,41,218,219,150,220,221,222,223,224,140,225,226,227,174,228,229,31,230,177],"finding google skills","as","messaging","model","trade-offs","provider","answer","fit","catalog","up","decision","use","database","candidate",{"skillNo":232,"name":233,"nameZh":6,"description":234,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":235,"categories":236,"keywords":239,"publishedAt":43},"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",[237,238],{"categoryNo":95,"categoryName":96,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":128,"categoryName":129,"taxonomyType":21,"taxonomyTypeDesc":22},[240,241,115,242,9,167,243,42,168,244,245,246,247,31,248,177,111,249,250,251,252],"workload manager basics","workload","basics","cli","review","vpc","resources","inspect","you","create","libraries","server","run"]