[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skills:sce6":3,"skills:pro6":50,"skills:creators9":95,"skills:stats":193,"skills:all:stars:217:":195},[4,15,22,29,36,43],{"categoryNo":5,"taxonomyType":6,"taxonomyTypeDesc":7,"categoryName":8,"categoryNameEn":9,"description":10,"level":11,"skillCount":12,"parentNo":13,"parentName":13,"children":14},"01JBSEEDCAT000000000000022","SCENARIO","使用场景","智能体与自动化","Agents & Automation","Skill、MCP、智能体与工作流自动化",1,39450,null,[],{"categoryNo":16,"taxonomyType":6,"taxonomyTypeDesc":7,"categoryName":17,"categoryNameEn":18,"description":19,"level":11,"skillCount":20,"parentNo":13,"parentName":13,"children":21},"01JBSEEDCAT000000000000006","软件开发","Software Development","编码、测试、部署等软件研发任务",26531,[],{"categoryNo":23,"taxonomyType":6,"taxonomyTypeDesc":7,"categoryName":24,"categoryNameEn":25,"description":26,"level":11,"skillCount":27,"parentNo":13,"parentName":13,"children":28},"01JBSEEDCAT000000000000014","设计创意","Design & Creative","视觉设计、品牌与多媒体创作",13568,[],{"categoryNo":30,"taxonomyType":6,"taxonomyTypeDesc":7,"categoryName":31,"categoryNameEn":32,"description":33,"level":11,"skillCount":34,"parentNo":13,"parentName":13,"children":35},"01JBSEEDCAT000000000000001","办公效率","Office Productivity","日常办公文档、表格、演示与沟通类任务",6285,[],{"categoryNo":37,"taxonomyType":6,"taxonomyTypeDesc":7,"categoryName":38,"categoryNameEn":39,"description":40,"level":11,"skillCount":41,"parentNo":13,"parentName":13,"children":42},"01JBSEEDCAT000000000000017","内容创作","Content Creation","写作、文案与营销内容生产",6029,[],{"categoryNo":44,"taxonomyType":6,"taxonomyTypeDesc":7,"categoryName":45,"categoryNameEn":46,"description":47,"level":11,"skillCount":48,"parentNo":13,"parentName":13,"children":49},"01JBSEEDCAT000000000000020","研究与学习","Research & Learning","资料检索、知识整理与学习辅导",5531,[],[51,60,67,74,81,88],{"categoryNo":52,"taxonomyType":53,"taxonomyTypeDesc":54,"categoryName":55,"categoryNameEn":56,"description":57,"level":11,"skillCount":58,"parentNo":13,"parentName":13,"children":59},"01JBSEEDCAT000000000000030","PROFESSION","适用职业","开发工程师","Software Engineer","从事软件编码、测试与运维的工程师",13273,[],{"categoryNo":61,"taxonomyType":53,"taxonomyTypeDesc":54,"categoryName":62,"categoryNameEn":63,"description":64,"level":11,"skillCount":65,"parentNo":13,"parentName":13,"children":66},"01JBSEEDCAT000000000000032","设计师","Designer","视觉、交互与品牌设计人员",7774,[],{"categoryNo":68,"taxonomyType":53,"taxonomyTypeDesc":54,"categoryName":69,"categoryNameEn":70,"description":71,"level":11,"skillCount":72,"parentNo":13,"parentName":13,"children":73},"01JBSEEDCAT000000000000033","数据分析师","Data Analyst","数据分析、报表与商业智能人员",4987,[],{"categoryNo":75,"taxonomyType":53,"taxonomyTypeDesc":54,"categoryName":76,"categoryNameEn":77,"description":78,"level":11,"skillCount":79,"parentNo":13,"parentName":13,"children":80},"01JBSEEDCAT000000000000036","内容创作者","Content Creator","作者、编辑与自媒体创作者",3940,[],{"categoryNo":82,"taxonomyType":53,"taxonomyTypeDesc":54,"categoryName":83,"categoryNameEn":84,"description":85,"level":11,"skillCount":86,"parentNo":13,"parentName":13,"children":87},"01JBSEEDCAT000000000000035","市场营销","Marketer","市场推广、增长与品牌运营人员",3446,[],{"categoryNo":89,"taxonomyType":53,"taxonomyTypeDesc":54,"categoryName":90,"categoryNameEn":91,"description":92,"level":11,"skillCount":93,"parentNo":13,"parentName":13,"children":94},"01JBSEEDCAT000000000000037","律师与法务","Legal","合同、合规与法律事务人员",3316,[],{"records":96,"total":191,"current":11,"size":192},[97,111,120,130,140,150,160,169,179],{"accountNo":98,"login":99,"displayName":100,"avatarUrl":101,"accountType":102,"accountTypeDesc":103,"profileDescription":104,"skillCount":105,"repoCount":106,"professionCount":107,"scenarioCount":108,"totalStars":109,"updatedAt":110},"01M698VYDXZ6JW5WH3CRV650R8","microsoft","Microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","ORGANIZATION","组织","Open source projects and samples from Microsoft",4160,229,11,7,829295,"2026-11-14 03:52:46",{"accountNo":112,"login":113,"displayName":113,"avatarUrl":114,"accountType":102,"accountTypeDesc":103,"profileDescription":115,"skillCount":116,"repoCount":117,"professionCount":108,"scenarioCount":108,"totalStars":118,"updatedAt":119},"01M698P3FSAZ6B4R928ZF19KPT","openclaw","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F252820863?v=4","Your personal, open source AI assistant.",362,32,437710,"2026-11-14 03:02:00",{"accountNo":121,"login":122,"displayName":123,"avatarUrl":124,"accountType":102,"accountTypeDesc":103,"profileDescription":13,"skillCount":125,"repoCount":126,"professionCount":108,"scenarioCount":127,"totalStars":128,"updatedAt":129},"01M6AC29YGCR4S2NTJFV0G8B81","openai","OpenAI","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F14957082?v=4",171,23,6,343687,"2026-11-14 03:12:46",{"accountNo":131,"login":132,"displayName":133,"avatarUrl":134,"accountType":102,"accountTypeDesc":103,"profileDescription":13,"skillCount":135,"repoCount":136,"professionCount":137,"scenarioCount":108,"totalStars":138,"updatedAt":139},"01M6AC83E6K4FDKK7HZ8WNMQKE","apache","The Apache Software Foundation","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F47359?v=4",419,75,8,298187,"2026-11-14 03:13:01",{"accountNo":141,"login":142,"displayName":143,"avatarUrl":144,"accountType":102,"accountTypeDesc":103,"profileDescription":145,"skillCount":146,"repoCount":147,"professionCount":107,"scenarioCount":108,"totalStars":148,"updatedAt":149},"01M698VYEFDM0VJBZDYYJ2T83S","google","Google","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F1342004?v=4","Google ❤️ Open Source",528,48,243019,"2026-11-14 03:03:50",{"accountNo":151,"login":152,"displayName":153,"avatarUrl":154,"accountType":102,"accountTypeDesc":103,"profileDescription":155,"skillCount":156,"repoCount":157,"professionCount":107,"scenarioCount":108,"totalStars":158,"updatedAt":159},"01M6AC83DS133TPXFSP1DHNCC6","github","GitHub","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","How people build software.",940,28,238680,"2026-11-14 03:13:39",{"accountNo":161,"login":162,"displayName":163,"avatarUrl":164,"accountType":102,"accountTypeDesc":103,"profileDescription":13,"skillCount":165,"repoCount":166,"professionCount":107,"scenarioCount":108,"totalStars":167,"updatedAt":168},"01M697XGDPXJAVQ8391V2BCYTE","anthropics","Anthropic","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F76263028?v=4",674,17,231826,"2026-11-13 04:18:26",{"accountNo":170,"login":171,"displayName":172,"avatarUrl":173,"accountType":102,"accountTypeDesc":103,"profileDescription":13,"skillCount":174,"repoCount":175,"professionCount":176,"scenarioCount":108,"totalStars":177,"updatedAt":178},"01M6AC83EPDGBFN19MWYRREJD0","Significant-Gravitas","AutoGPT","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F130738209?v=4",86,2,10,188019,"2026-11-14 03:13:09",{"accountNo":180,"login":181,"displayName":182,"avatarUrl":183,"accountType":184,"accountTypeDesc":185,"profileDescription":186,"skillCount":187,"repoCount":188,"professionCount":127,"scenarioCount":108,"totalStars":189,"updatedAt":190},"01M6DJCCDDG53MTYYRTXAXFG17","garrytan","Garry Tan","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F19957?v=4","USER","个人作者","Writes software, dreams. ",324,3,174700,"2026-11-14 02:22:54",1925,9,{"skillCount":194,"professionCount":107,"scenarioCount":108,"creatorCount":191},196837,{"records":196,"total":194,"current":608,"size":609},[197,229,250,269,285,300,333,358,381,399,421,441,469,498,524,544,561,583],{"skillNo":198,"name":199,"nameZh":13,"description":200,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":205,"categories":206,"keywords":207,"publishedAt":228},"01M6992ZX02SEBJQVRJH4KZE87","gke-ai-troubleshooting-tpu-metrics-monitoring","Monitors and troubleshoots GKE TPU workloads, nodes, and node pools using GKE system metrics and PromQL. Use when monitoring TensorCore duty cycle, TPU memory, node readiness, multi-host TPU node pool availability, host maintenance or preemption interruptions, and calculating MTTR or MTBI metrics for GKE TPUs. Don't use for general non-TPU GKE workload monitoring or non-metric TPU debugging.","google\u002Fskills","01M698WR0H76ENHE587T9EJT89",24122,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-ai-troubleshooting-tpu-metrics-monitoring\u002FSKILL.md",[],[208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227],"gke ai troubleshooting tpu metrics monitoring","monitoring","tpu","gke","metrics monitoring","troubleshooting","metrics","memory","availability","for","cycle","system","mttr","general","host","use","debugging","when","promql","multi","2026-11-10 10:12:30",{"skillNo":230,"name":231,"nameZh":13,"description":232,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":233,"categories":234,"keywords":235,"publishedAt":228},"01M6992Y037TMWAB3Z885V2J83","gke-ai-troubleshooting-tpu-dynamic-slices-monitoring","Monitors, troubleshoots, and manages GKE TPU Dynamic Slices custom resources. Use when checking TPU slice lifecycle states, troubleshooting slice provisioning failures, validating single-slice or multi-slice (JobSet) workload manifests, or safely patching stuck finalizers and disabling the slice controller. Don't use for generic GKE cluster node pool creation or standard non-TPU workload management (use gke-basics or gke-cluster-creation instead).","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-ai-troubleshooting-tpu-dynamic-slices-monitoring\u002FSKILL.md",[],[236,209,210,211,213,237,238,239,240,241,225,242,243,244,245,246,247,217,248,249],"gke ai troubleshooting tpu dynamic slices monitoring","containers","resources","creation","states","manifests","controller","node","workload","patching","cluster","generic","slice","basics",{"skillNo":251,"name":252,"nameZh":13,"description":253,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":254,"categories":255,"keywords":256,"publishedAt":228},"01M6992WJD7EMBMR6AAFZS8FTM","gke-ai-troubleshooting-node-unresponsive-timeout","Diagnose and mitigate GKE TPU or GPU nodes stuck in NotReady \u002F NodeStatusUnknown (\"Kubelet stopped posting node status\") due to host kernel panics, hardware lockups, or disabled node auto-repair. Use when nodes stop heartbeating beyond the node auto-repair threshold and pods remain stuck in Terminating. Don't use for healthy nodes, pod-only application crashes, or routine GKE upgrades.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-ai-troubleshooting-node-unresponsive-timeout\u002FSKILL.md",[],[257,211,243,213,258,237,259,260,222,261,225,262,210,263,264,265,266,267,268,217],"gke ai troubleshooting node unresponsive timeout","timeout","kernel","only","auto","gke upgrades","status","routine","stop","repair","posting","diagnose",{"skillNo":270,"name":271,"nameZh":13,"description":272,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":273,"categories":274,"keywords":275,"publishedAt":228},"01M6992TQ9V7XW5BB9F3Q9NNW1","gke-ai-troubleshooting-jobset-interruption","Diagnoses GKE JobSet interruptions, restarts, and preemptions for AI\u002FML training workloads autonomously. Use when troubleshooting JobSet restart loops, spot VM preemptions, node readiness failures, host VM issues, or coordinator worker crashes. Don't use for general GKE cluster creation, basic workload deployment, or non-JobSet application issues.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-ai-troubleshooting-jobset-interruption\u002FSKILL.md",[],[276,211,213,237,239,277,278,279,280,222,225,281,243,244,282,246,217,283,284,221],"gke ai troubleshooting jobset interruption","training","issues","worker","coordinator","loops","restart","basic","deployment",{"skillNo":286,"name":287,"nameZh":13,"description":288,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":289,"categories":290,"keywords":291,"publishedAt":228},"01M6992S3G6YRFRG97SABMXHSP","gke-ai-troubleshooting-handle-disruption-gpu-tpu","Diagnoses, predicts, and mitigates node disruptions during Compute Engine host maintenance and hardware or software maintenance events for GPU and TPU workloads on GKE. Use when diagnosing node disruptions, predicting host maintenance events on GPU\u002FTPU nodepools, inspecting node interruption PromQL metrics, auditing node taints, or configuring workload protection strategies (graceful termination, opportunistic maintenance, PodDisruptionBudgets). Don't use for general GKE cluster creation, network policy configuration, or non-disruption workload deployment.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgke-ai-troubleshooting-handle-disruption-gpu-tpu\u002FSKILL.md",[],[292,293,210,211,213,294,246,239,217,284,221,222,223,225,226,295,296,297,298,299],"gke ai troubleshooting handle disruption gpu tpu","gpu","policy","compute","engine","strategies","gke cluster creation","maintenance",{"skillNo":301,"name":302,"nameZh":13,"description":303,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":304,"categories":305,"keywords":313,"publishedAt":228},"01M6992Q3688MT3K904X0RCTAK","gemini-live-api","Generates a Gemini LiveAPI client service class in the user's chosen programming language. Use when the user wants to build, scaffold, or integrate a client that connects to the Gemini Enterprise LiveAPI websocket endpoint, handles session setup\u002Fresumption, bearer token refresh, and sending\u002Freceiving `ClientMessage`\u002F`ServerMessage` protos. Don't use for general (non-live, non-bidirectional) Gemini API usage such as one-shot `generateContent`, embeddings, image\u002Fvideo generation, or fine-tuning — use the `gemini-api` skill for those.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgemini-live-api\u002FSKILL.md",[306,309,312],{"categoryNo":307,"categoryName":308,"taxonomyType":6,"taxonomyTypeDesc":7},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":310,"categoryName":311,"taxonomyType":6,"taxonomyTypeDesc":7},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":52,"categoryName":55,"taxonomyType":53,"taxonomyTypeDesc":54},[314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,217,331,332,225],"gemini live api","gemini","api","live","gemini api","image","Video","video generation","tuning","token","embeddings","integrate","endpoint","as","usage","websocket","programming language","session","Enterprise",{"skillNo":334,"name":335,"nameZh":13,"description":336,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":337,"categories":338,"keywords":344,"publishedAt":228},"01M6992MZKK60M1V7RZ8XZGT0Y","gemini-interactions-api","Guides the usage of Gemini Interactions API on Gemini Enterprise Agent Platform. Use when the user wants to use the stateful, server-managed Interactions API for multi-turn conversations, background execution, streaming, structured output, and function calling on the Agent Platform.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgemini-interactions-api\u002FSKILL.md",[339,340,343],{"categoryNo":307,"categoryName":308,"taxonomyType":6,"taxonomyTypeDesc":7},{"categoryNo":341,"categoryName":342,"taxonomyType":6,"taxonomyTypeDesc":7},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":52,"categoryName":55,"taxonomyType":53,"taxonomyTypeDesc":54},[315,345,316,346,347,348,349,225,350,351,352,353,328,354,217,332,355,356,223,357],"gemini interactions api","gemini enterprise agent platform","stateful","conversations","managed","guides","streaming","server","structured output","platform","execution","calling","function calling",{"skillNo":359,"name":360,"nameZh":13,"description":361,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":362,"categories":363,"keywords":367,"publishedAt":228},"01M6992J7RHD08G4K3YK2WQBNT","gemini-api","Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform. Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK. Covers SDK usage (Python, JS\u002FTS, Go, Java, C#), capabilities like multimodal inputs, tools, media generation, caching, batch prediction, and Live API.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgemini-api\u002FSKILL.md",[364,365,366],{"categoryNo":307,"categoryName":308,"taxonomyType":6,"taxonomyTypeDesc":7},{"categoryNo":341,"categoryName":342,"taxonomyType":6,"taxonomyTypeDesc":7},{"categoryNo":52,"categoryName":55,"taxonomyType":53,"taxonomyTypeDesc":54},[315,318,316,142,368,369,370,371,372,373,350,374,375,376,377,317,378,328,354,379,380,225],"go","google cloud","Media","caching","batch","capabilities","credentials","active","ai sdk","multimodal","Tools","environment","prediction",{"skillNo":382,"name":383,"nameZh":13,"description":384,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":385,"categories":386,"keywords":390,"publishedAt":228},"01M6992GB2DPQV9V403P9PA3MZ","gemini-agents-api","Manages custom Agent resources on Gemini Enterprise Agent Platform. Use when the user wants to programmatically create, configure, list, update, or delete stateful, server-managed Agent resources (including mounting files, skills, and tools) before executing conversations.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fgemini-agents-api\u002FSKILL.md",[387,388,389],{"categoryNo":307,"categoryName":308,"taxonomyType":6,"taxonomyTypeDesc":7},{"categoryNo":341,"categoryName":342,"taxonomyType":6,"taxonomyTypeDesc":7},{"categoryNo":52,"categoryName":55,"taxonomyType":53,"taxonomyTypeDesc":54},[391,315,316,346,347,238,348,349,392,225,393,394,352,395,396,397,378,354,332,398],"gemini agents api","files","configure","create","delete","update","list","custom",{"skillNo":400,"name":401,"nameZh":13,"description":402,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":403,"categories":404,"keywords":405,"publishedAt":228},"01M6992BJXH42PBTF7MK38FMVN","firebase-basics","Provides foundational Firebase CLI setup, CLI installation, version checks (`firebase-tools@latest --version`), CLI login (including --no-localhost), project creation, project selection (`firebase use`), and app config file downloads (`google-services.json`, `GoogleService-Info.plist`). Use ONLY for CLI login, project creation\u002Fswitching, or downloading app config files. Don't use for Firebase Hosting deploy, Firestore, Auth, App Hosting, Data Connect, Crashlytics, or Remote Config.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Ffirebase-basics\u002FSKILL.md",[],[406,249,407,142,408,409,410,411,239,412,217,413,414,260,415,392,223,416,417,418,419,420],"firebase basics","firebase","cli","app","plist","downloads","auth","project","selection","remote","connect","switching","version","file","firestore",{"skillNo":422,"name":423,"nameZh":13,"description":424,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":425,"categories":426,"keywords":428,"publishedAt":228},"01M6991W5FVJ49YSNGJ6HV3W7E","developer-device-platform-basics","Provides guidance and instructions on managing remote devices on Developer Device Platform (DDP). Use when reserving remote Android devices, establishing connection tunnels, checking session status, or extending\u002Fcancelling leases. Don't use for iOS or local device\u002Fhardware inquiries.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fdeveloper-device-platform-basics\u002FSKILL.md",[427],{"categoryNo":52,"categoryName":55,"taxonomyType":53,"taxonomyTypeDesc":54},[429,354,249,430,431,432,217,433,331,434,415,223,225,435,436,437,438,263,439,440],"developer device platform basics","device","developer","android","devices","local","DDP","instructions","ios","guidance","connection","hardware",{"skillNo":442,"name":443,"nameZh":13,"description":444,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":445,"categories":446,"keywords":448,"publishedAt":228},"01M6991TNK0T794RRKD2J3W2GV","detection-engineering-coverage-evaluation","Automates the end-to-end detection engineering workflow in Google SecOps using MCP tools. Use when fetching threat intelligence from blogs, generating Threat Detection Opportunities (TDOs), simulating attacker behavior with synthetic UDM events, evaluating rule coverage, and deploying gap-closing rules. Don't use for standalone YARA-L 2.0 rule authoring\u002Ftuning (use secops-detection-engineering), threat hunting, or SOC investigation.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fdetection-engineering-coverage-evaluation\u002FSKILL.md",[447],{"categoryNo":341,"categoryName":342,"taxonomyType":6,"taxonomyTypeDesc":7},[449,450,451,452,453,454,142,455,456,457,458,459,460,225,461,462,463,464,465,466,467,468],"detection engineering coverage evaluation","coverage","Evaluation","detection","engineering","detection engineering","MCP","security","detection-engineering","standalone","end","google secops","workflow","soc","secops","synthetic","threat hunting","threat intelligence","closing","hunting",{"skillNo":470,"name":471,"nameZh":13,"description":472,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":473,"categories":474,"keywords":475,"publishedAt":228},"01M6991RZ0Z5P26PNGPVCD895J","dbt-sf-to-bq-translator","Translates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to BigQuery on Google Cloud. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fdbt-sf-to-bq-translator\u002FSKILL.md",[],[476,477,478,479,480,369,481,217,482,483,223,484,485,486,487,488,489,490,491,492,493,225,494,495,496,142,497,247],"dbt sf to bq translator","sf","bq","translator","dbt","translation","snowflake dbt","snowflake","deduplication","workflows","type","queries","placeholder","sql migrations","migrations","ast","models","json extraction","casting","sql","config","macro",{"skillNo":499,"name":500,"nameZh":13,"description":501,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":502,"categories":503,"keywords":507,"publishedAt":228},"01M6991MH38TD2VKSS81CV5MXR","datalineage-bigquery-asset-impact-analysis","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.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fdatalineage-bigquery-asset-impact-analysis\u002FSKILL.md",[504],{"categoryNo":505,"categoryName":506,"taxonomyType":6,"taxonomyTypeDesc":7},"01JBSEEDCAT000000000000013","分析与可视化",[508,509,510,511,512,513,142,514,369,515,516,517,518,392,225,519,520,521,522,523],"datalineage bigquery asset impact analysis","impact","analysis","asset","impact analysis","bigquery","data analysis","lineage","broken","downstream impact","Cloud Storage","updates","affected","storage","data","radius",{"skillNo":525,"name":526,"nameZh":13,"description":527,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":528,"categories":529,"keywords":530,"publishedAt":228},"01M6KZKY5083H4M9WH0FA2P3WC","compute-on-demand-reservations","Inspects, verifies, and manages Google Compute Engine (GCE) On-Demand reservations and analyzes consumed versus available reserved capacity. Use when checking active reservation capacity, determining whether reserved VMs are available or exhausted, verifying reservation targeting (automatic versus specific reservation), or sharing reservations across projects. Don't use for Spot capacity advice (use compute-spot-capacity-advice) or future calendar bookings (use compute-future-reservations).","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fcompute-on-demand-reservations\u002FSKILL.md",[],[531,295,532,533,217,534,223,535,536,296,537,538,539,375,540,541,542,142,543,225],"reservations","compute on demand reservations","projects","targeting","capacity","across","bookings","vms","spot","advice","sharing","calendar","automatic",{"skillNo":545,"name":546,"nameZh":13,"description":547,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":548,"categories":549,"keywords":550,"publishedAt":228},"01M6KZKRDK05VJ8X8QWPP6AZMR","compute-future-reservations","Evaluates future GPU and TPU reservation feasibility and calendar-mode booking options in Google Compute Engine (GCE). Use when planning future AI\u002FML training runs, checking reservation availability windows, verifying accelerator cluster topologies, or assessing future capacity lead times. Don't use for active on-demand reservations (use compute-on-demand-reservations), immediate Spot capacity queries (use compute-spot-capacity-advice), or Standard Future Reservations (--reservation-mode=DEFAULT).","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fcompute-future-reservations\u002FSKILL.md",[],[551,531,295,277,552,553,210,554,375,555,542,142,556,557,246,216,558,217,223,559,225,560],"compute future reservations","mode","default","options","booking","feasibility","windows","planning","ml","lead",{"skillNo":562,"name":563,"nameZh":13,"description":564,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":565,"categories":566,"keywords":567,"publishedAt":228},"01M6991D44HXCFXQDZV8YHQBEA","cloud-trace-querying","Query Cloud Trace spans, filter by latency thresholds or error status, correlate distributed traces with Cloud Logging, and diagnose latency bottlenecks across Google Cloud services. Use when investigating slow requests, analyzing trace hierarchies, or resolving latency regressions. Do NOT use for querying non-GCP telemetry or database query optimization outside Cloud Trace.","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fcloud-trace-querying\u002FSKILL.md",[],[568,569,570,142,369,571,572,225,573,574,536,575,576,263,577,578,579,580,581,582],"cloud trace querying","cloud","trace","error","database","query","filter","gcp","analyzing","query optimization","logging","database query","do","thresholds","latency",{"skillNo":584,"name":585,"nameZh":13,"description":586,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":142,"ownerAvatarUrl":144,"ownerAccountNo":141,"ownerDisplayName":143,"repositoryNo":202,"starsCount":203,"licenseSpdxId":204,"licenseFileUrl":13,"sourceHtmlUrl":587,"categories":588,"keywords":592,"publishedAt":228},"01M699194KR4DV0C1RS0RR7CKW","cloud-run-basics","Manages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications responding to HTTP requests (services), run event-triggered or scheduled tasks (jobs), or handle always-on pull-based background processing (worker pools).","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fcloud\u002Fcloud-run-basics\u002FSKILL.md",[589],{"categoryNo":590,"categoryName":591,"taxonomyType":6,"taxonomyTypeDesc":7},"01JBSEEDCAT000000000000011","运维与部署",[593,594,249,595,569,596,597,598,599,279,223,600,601,225,602,603,604,605,606,607],"cloud run basics","cloud run","run","pull","processing","event","applications","http","scheduled","you","scheduled tasks","tasks","services","requests","deploy",217,18]