[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DYEXFSBVQCWH4MGAE58ZWK":3,"skill-same:01M6DYEXFSBVQCWH4MGAE58ZWK":92},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":59,"ownerHtmlUrl":60,"ownerBlogUrl":61,"ownerDescription":62,"updatedAt":63,"install":64,"resources":77},"01M6DYEXFSBVQCWH4MGAE58ZWK","mlnet",null,"Use ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations. USE FOR: ML.NET integration; local model training or retraining; inference pipelines, model loading, evaluation, and deployment review. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.","managedcode\u002Fdotnet-skills","managedcode","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F78253589?v=4","01M6DYDJR3EG8KB5G4PC65XCTT","Managed Code","01M6DYDJX0ZT432N04NYJW89DN",512,"MIT","https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fcatalog\u002FFrameworks\u002FML.NET\u002Fskills\u002Fmlnet\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"data preparation","when","inspect","loading","relevant","commands","local","do","files","builder","unrelated","integrate","use","edit","review","model","learning","applications","machine-learning","Evaluation","repository","lint","run","tasks","deployment","training","ml","test","cli","2026-11-10 10:12:30",false,"---\nname: mlnet\ndescription: \"Use ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations. USE FOR: ML.NET integration; local model training or retraining; inference pipelines, model loading, evaluation, and deployment review. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.\"\ncompatibility: \"Requires ML.NET, Model Builder, or ML.NET CLI scenarios.\"\n---\n\n# ML.NET\n\n## Trigger On\n\n- integrating machine learning into a .NET application\n- training or retraining ML.NET models from local data\n- reviewing inference pipelines, model loading, or AutoML-generated code\n\n## Workflow\n\n1. Start from the prediction task and data quality, not the algorithm or package list.\n2. Separate training code from inference code so the production path stays lean and predictable.\n3. Review feature engineering, normalization, label quality, and evaluation metrics before trusting model output.\n4. Use Model Builder or the ML.NET CLI when they speed up exploration, but inspect the generated C# before treating it as production architecture.\n5. Plan how the model is loaded, versioned, and refreshed in the application lifecycle.\n6. Validate with representative datasets and explicit evaluation, not only with a sample that happens to run.\n\n## Deliver\n\n- ML.NET pipelines that fit the prediction task\n- production-usable inference integration\n- evaluation evidence tied to the business scenario\n\n## Validate\n\n- model quality is measured, not assumed\n- training and inference responsibilities are separated\n- deployment and versioning expectations are explicit\n\n## References\n\n- [patterns.md](references\u002Fpatterns.md) - Data loading, training pipelines, evaluation metrics, deployment strategies, and feature engineering patterns\n- [examples.md](references\u002Fexamples.md) - Complete examples for sentiment analysis, price prediction, image classification, anomaly detection, recommendations, clustering, fraud detection, text classification, object detection, and AutoML\n","# ML.NET\n\n## Trigger On\n\n- integrating machine learning into a .NET application\n- training or retraining ML.NET models from local data\n- reviewing inference pipelines, model loading, or AutoML-generated code\n\n## Workflow\n\n1. Start from the prediction task and data quality, not the algorithm or package list.\n2. Separate training code from inference code so the production path stays lean and predictable.\n3. Review feature engineering, normalization, label quality, and evaluation metrics before trusting model output.\n4. Use Model Builder or the ML.NET CLI when they speed up exploration, but inspect the generated C# before treating it as production architecture.\n5. Plan how the model is loaded, versioned, and refreshed in the application lifecycle.\n6. Validate with representative datasets and explicit evaluation, not only with a sample that happens to run.\n\n## Deliver\n\n- ML.NET pipelines that fit the prediction task\n- production-usable inference integration\n- evaluation evidence tied to the business scenario\n\n## Validate\n\n- model quality is measured, not assumed\n- training and inference responsibilities are separated\n- deployment and versioning expectations are explicit\n\n## References\n\n- [patterns.md](references\u002Fpatterns.md) - Data loading, training pipelines, evaluation metrics, deployment strategies, and feature engineering patterns\n- [examples.md](references\u002Fexamples.md) - Complete examples for sentiment analysis, price prediction, image classification, anomaly detection, recommendations, clustering, fraud detection, text classification, object detection, and AutoML\n",{"name":5,"description":7,"compatibility":53},"Requires ML.NET, Model Builder, or ML.NET CLI scenarios.","main","497cc9aba79a931ac6fb8d36a48cc22fe3d6d74b","catalog\u002FFrameworks\u002FML.NET\u002Fskills\u002Fmlnet",41,"https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills","https:\u002F\u002Fskills.managed-code.com","https:\u002F\u002Fgithub.com\u002Fmanagedcode","https:\u002F\u002Fwww.managed-code.com","Open Source Community for .NET Developers: Reliable, Actively Maintained, and Community-Driven. Made in Ukraine 🇺🇦 with ❤️","2026-11-11 19:34:28",{"source":8,"skillName":5,"commands":65,"prompt":75,"zipAvailable":76},[66,69,72],{"tool":67,"command":68},"npx","npx skills add managedcode\u002Fdotnet-skills --skill mlnet",{"tool":70,"command":71},"bunx","bunx skills add managedcode\u002Fdotnet-skills --skill mlnet",{"tool":73,"command":74},"pnpm","pnpm dlx skills add managedcode\u002Fdotnet-skills --skill mlnet","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fcatalog\u002FFrameworks\u002FML.NET\u002Fskills\u002Fmlnet\u002FSKILL.md",true,[78,84,88],{"path":79,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":82,"sourceHtmlUrl":83},"manifest.json","REFERENCE","参考文档",81,"https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fcatalog\u002FFrameworks\u002FML.NET\u002Fskills\u002Fmlnet\u002Fmanifest.json",{"path":85,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fexamples.md",29435,"https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fcatalog\u002FFrameworks\u002FML.NET\u002Fskills\u002Fmlnet\u002Freferences\u002Fexamples.md",{"path":89,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Fpatterns.md",19098,"https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fcatalog\u002FFrameworks\u002FML.NET\u002Fskills\u002Fmlnet\u002Freferences\u002Fpatterns.md",[93,124,147,176,186,213],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":109,"publishedAt":48},"01M6DYQE1Y9FHAQHEDTVFQ3447","xtwitter-scraper","Use XTwitterScraper when a .NET application needs typed access to Xquik's X automation REST API for tweet search, user lookup, posting workflows, extraction jobs, monitors, webhooks, or giveaway draws. USE FOR: integrating the XTwitterScraper NuGet package; reviewing Xquik API client usage; modeling API-key configuration, pagination, binary exports, raw responses, retries, and webhook\u002Fevent flows in .NET. DO NOT USE FOR: unrelated stacks; direct browser automation; generic social-media strategy; or code that does not call Xquik. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.","https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fcatalog\u002FLibraries\u002FXTwitterScraper\u002Fskills\u002Fxtwitter-scraper\u002FSKILL.md",[99,104],{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":105,"categoryName":106,"taxonomyType":107,"taxonomyTypeDesc":108},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[110,111,112,41,113,114,115,39,116,117,118,119,32,29,120,27,121,24,122,123],"xtwitter scraper","browser automation","workflows","code","browser","search","api","Media","api client","access","webhooks","social","modeling","webhook",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":136,"publishedAt":48},"01M6DYQ9S4PE1H6G8MCH9T21HR","sep","Use Sep for high-performance separated-value parsing and writing in .NET, including delimiter inference, explicit parser\u002Fwriter options, and low-allocation row\u002Fcolumn workflows. USE FOR: delimited data needs are performance-sensitive and allocation-aware; project needs explicit control over separator inference, escaping, trimming, and header behavior;. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.","https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fcatalog\u002FLibraries\u002FSep\u002Fskills\u002Fsep\u002FSKILL.md",[130,133],{"categoryNo":131,"categoryName":132,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":134,"categoryName":135,"taxonomyType":107,"taxonomyTypeDesc":108},"01JBSEEDCAT000000000000036","内容创作者",[126,137,138,112,42,41,39,139,140,141,32,142,29,27,26,143,144,145,24,146],"writing","performance","data","parsing","parser","context","writer","options","project","high-performance",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":153,"publishedAt":48},"01M6DZS2DNEPXCGGF6SSCW6G5V","webgpu-threejs-tsl","Comprehensive guide for developing WebGPU-enabled Three.js applications using TSL (Three.js Shading Language). Covers WebGPU renderer setup, TSL syntax and node materials, compute shaders, post-processing effects, and WGSL integration. Use this skill when working with Three.js WebGPU, TSL shaders, node materials, or GPU compute in Three.js.","https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fexternal-sources\u002Fupstreams\u002Fwebgpu-claude-skill\u002Fskills\u002Fwebgpu-threejs-tsl\u002FSKILL.md",[],[154,155,156,157,158,159,160,36,161,162,163,164,31,165,166,167,168,169,170,171,172,173,174,175,20],"webgpu","threejs","webgpu threejs tsl","setup","shaders","gpu","integration","js","node","syntax","guide","language","three","compute","post","processing","with","renderer","for","materials","wgsl","effects",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":180,"categories":181,"keywords":182,"publishedAt":48},"01M6DZS0BQXW2B56A289XF1X76","roslynk","Use Roslynk for semantic inspection and edits of *.cs, *.razor and *.cshtml solution files.","https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fexternal-sources\u002Fupstreams\u002Froslynk\u002Fskills\u002Froslynk\u002FSKILL.md",[],[178,31,27,183,184,172,185],"semantic","razor","inspection",{"skillNo":187,"name":188,"nameZh":6,"description":189,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":190,"categories":191,"keywords":192,"publishedAt":48},"01M6DZRW6MEJCVW80K3S4XV60K","system-text-json-net11","Imperative guidance for the System.Text.Json APIs added in .NET 11: the built-in `JsonNamingPolicy.PascalCase` naming policy, and the strongly-typed generic `JsonSerializerOptions.GetTypeInfo` and `JsonSerializerOptions.TryGetTypeInfo` metadata accessors (generic overloads that return a typed `JsonTypeInfo`). USE ONLY when the user is targeting net11.0 or later and needs PascalCase JSON property or dictionary-key names without writing a custom naming policy, a strongly-typed generic `JsonTypeInfo` instead of the non-generic `JsonTypeInfo`, or a no-throw way to probe whether a type's serialization metadata is resolved. DO NOT USE when the target is earlier than net11.0, the requested behavior uses an established pre-net11 naming policy, or the user explicitly selected another JSON library.","https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fexternal-sources\u002Fupstreams\u002Fdotnet-skills\u002Fplugins\u002Fdotnet11\u002Fskills\u002Fsystem-text-json-net11\u002FSKILL.md",[],[193,194,195,196,137,197,198,199,200,201,202,31,203,204,205,206,26,207,208,209,210,172,211,212,20],"system","json","text","system text json net11","policy","metadata","library","serialization","behavior","user","only","probe","guidance","later","type","dictionary","naming","targeting","custom","generic",{"skillNo":214,"name":215,"nameZh":6,"description":216,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":217,"categories":218,"keywords":221,"publishedAt":48},"01M6DZRRJP3S662NFSG1RSPQQ7","process-api-net11","Provides guidance on the new System.Diagnostics.Process APIs introduced in .NET 11: Process.Run, Process.RunAndCaptureText, Process.StartAndForget, Process.ReadAllText\u002FBytes\u002FLines, KillOnParentExit, InheritedHandles, and StartDetached. USE FOR: starting, orchestrating, or capturing output from external processes in applications targeting net11.0 or later. DO NOT USE FOR: applications targeting .NET 10 or earlier, or basic pre-.NET 11 Process.Start usage without new APIs.","https:\u002F\u002Fgithub.com\u002Fmanagedcode\u002Fdotnet-skills\u002Fblob\u002Fmain\u002Fexternal-sources\u002Fupstreams\u002Fdotnet-skills\u002Fplugins\u002Fdotnet11\u002Fskills\u002Fprocess-api-net11\u002FSKILL.md",[219,220],{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},{"categoryNo":105,"categoryName":106,"taxonomyType":107,"taxonomyTypeDesc":108},[116,222,223,224,41,225,36,193,31,205,206,26,226,227,228,229,210,172,230],"process","process api net11","diagnostics","start","basic","processes","output","usage","new"]