[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6BY4SYVCNFDV3Z64PFAKS86":3,"skill-same:01M6BY4SYVCNFDV3Z64PFAKS86":46},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":27,"repositoryHtmlUrl":28,"repositoryHomepageUrl":29,"ownerHtmlUrl":30,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":31,"install":32,"resources":45},"01M6BY4SYVCNFDV3Z64PFAKS86","current-time",null,"Get the current local time, date and day of the week.","google-ai-edge\u002Flitert-samples","google-ai-edge","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F150697620?v=4","01M6BY4NHMZF88PJAC1E1QJY8N","01M6BY4NNXP8PC95Y6CABCJXP9",492,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fsamples\u002Flitert\u002Fflutter\u002Fassets\u002Fskills\u002Fcurrent-time\u002FSKILL.md",[],[18],"current time","2026-11-10 10:12:30",false,"---\nname: current-time\ndescription: Get the current local time, date and day of the week.\n---\n# Current time\n\n## Instructions\nCall the `run_intent` tool with intent `current_time` and parameters {}.\nThen tell the user the time from the result in one short sentence.\n","# Current time\n\n## Instructions\nCall the `run_intent` tool with intent `current_time` and parameters {}.\nThen tell the user the time from the result in one short sentence.\n",{"name":5,"description":7},"main","9467001474dc4e072ebc588e5eb9b5fdcf63abbc","samples\u002Flitert\u002Fflutter\u002Fassets\u002Fskills\u002Fcurrent-time",134,"https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples","https:\u002F\u002Fdevelopers.google.com\u002Fedge\u002Flitert","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge","2026-11-11 08:30:40",{"source":8,"skillName":5,"commands":33,"prompt":43,"zipAvailable":44},[34,37,40],{"tool":35,"command":36},"npx","npx skills add google-ai-edge\u002Flitert-samples --skill current-time",{"tool":38,"command":39},"bunx","bunx skills add google-ai-edge\u002Flitert-samples --skill current-time",{"tool":41,"command":42},"pnpm","pnpm dlx skills add google-ai-edge\u002Flitert-samples --skill current-time","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fsamples\u002Flitert\u002Fflutter\u002Fassets\u002Fskills\u002Fcurrent-time\u002FSKILL.md",true,[],[47,64,87,117,134,153],{"skillNo":48,"name":49,"nameZh":6,"description":50,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":51,"categories":52,"keywords":53,"publishedAt":19},"01M6BY5T6RQENC1DMWZ8NZ8JFF","on-device-verification","Prove a converted or quantized LiteRT model on the actual device via the CompiledModel API - confirm GPU residency, compare device output against the source model, and diagnose device-only failures such as silent CPU fallback, whole-graph compile ceilings, and fp16 range breaks. Use after conversion or quantization, when device output is wrong or NaN, when a clean graph fails to compile only on device, or when GPU and CPU outputs are suspiciously identical.","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fskills\u002Fon-device-verification\u002FSKILL.md",[],[54,55,56,57,58,59,60,61,62,63],"on device verification","verification","graph","api","FP16","compile","diagnose","model","litert","gpu",{"skillNo":65,"name":66,"nameZh":6,"description":67,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":68,"categories":69,"keywords":70,"publishedAt":19},"01M6BY5F447DRN3YDJVRWDG9MZ","litert-google-tensor","Compiles LiteRT (.tflite) models ahead-of-time for Google Tensor TPUs (Tensor G5 on Pixel 10, Tensor G6 on Pixel 11) locally on Linux x86_64 or remotely via Google Colab (--colab on macOS, Windows, or ARM hosts), benchmarks them on a USB-attached Pixel with LiteRT's benchmark_model (NPU latency, peak memory, TPU tile time, TPU energy, optional CPU baseline), and packages compiled models for an Android Gradle app as a Google Play AI pack with the Tensor NPU runtime module and a Kotlin loader, applies it to the app and verifies it on a Pixel. Use when the user asks to compile, AOT-compile, or prepare a .tflite model for the Pixel TPU\u002FNPU, check whether a .tflite is already Tensor-compiled, measure on-device NPU performance of a compiled model, or package or deploy a compiled model into their Android app (or replace the one it ships). Don't use for Qualcomm, MediaTek, or Samsung NPUs, GPU-only benchmarking, LiteRT-LM or LLM (.litertlm) models, on-device JIT compilation, non-Gradle builds, or publishing to Google Play.","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fskills\u002Flitert-google-tensor\u002FSKILL.md",[],[71,72,73,62,74,59,75,61,76,77,78,79,80,81,82,83,84,63,85,86],"google","litert google tensor","tensor","performance","memory","windows","package","benchmark","linux","baseline","tpu","benchmarking","packages","android","play","kotlin",{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":98,"publishedAt":19},"01M6BY5BY9H6SDG9GRRTT04EWA","litert-conversion-workflow","Convert a Hugging Face LLM or vision-language model checkpoint into a .litertlm bundle that runs on the LiteRT-LM runtime with verified quality - classify the architecture against known runtime walls, pick the recipe family (dense, reasoning, hybrid SSM, VLM), export, quantize, gate the result against the source model, and publish. Use when converting a new LLM or VLM to LiteRT-LM, when a converted bundle crashes on the first message or dies at engine creation, when a quantized model answers worse than its source, or when deciding whether a model is convertible at all.","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fskills\u002Flitert-conversion-workflow\u002FSKILL.md",[93],{"categoryNo":94,"categoryName":95,"taxonomyType":96,"taxonomyTypeDesc":97},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[99,100,62,101,102,103,104,105,106,107,108,109,110,111,112,113,114,61,115,116],"conversion","workflow","litert conversion workflow","source","face","hugging face","verified","export","quantize","at","result","classify","message","creation","architecture","pick","publish","recipe",{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":123,"publishedAt":19},"01M6BY5835F3RMMW9KCCZ006JM","litert-compiled-model-migration","Rapidly migrate an Android application from legacy TensorFlow Lite (TFLite) to the modern LiteRT CompiledModel API (2.x) in Open Source GitHub repositories, keeping the TensorFlow Lite Support Library as LiteRT Support (litert-support 2.3.0). Covers buffer reuse, NPU JIT compilation, a reference implementation, and a two-stage verification gate (compile check, then an on-device parity test against a golden output).","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fskills\u002Flitert-compiled-model-migration\u002FSKILL.md",[],[124,61,62,59,125,126,127,128,129,130,131,55,84,57,132,133],"litert compiled model migration","tensorflow","open","library","jit","support","github","test","migrate","open source",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":140,"publishedAt":19},"01M6BY55P8GVX66H0FG8JHXHWF","gpu-clean-conversion","Convert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device.","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fskills\u002Fgpu-clean-conversion\u002FSKILL.md",[],[99,141,63,142,143,104,105,144,145,146,147,116,148,149,62,57,150,151,152,61],"clean","gpu clean conversion","output","CPU","via","with","use","as","back","convert","pytorch","correct",{"skillNo":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":159,"publishedAt":19},"01M6BY52X9KX3VRXQJS622DZH3","compiled-model-app-scaffolding","Build a new Android app (Kotlin, Compose) around a verified LiteRT model using the CompiledModel API - the app architecture, the inference-layer lifecycle rules, model delivery, and the UI traps that masquerade as model bugs. Use when turning a converted and device-verified model into a demo or product app, when an app's inference layer leaks memory or blocks the UI, or when a model that verified clean looks wrong inside an app.","https:\u002F\u002Fgithub.com\u002Fgoogle-ai-edge\u002Flitert-samples\u002Fblob\u002Fmain\u002Fskills\u002Fcompiled-model-app-scaffolding\u002FSKILL.md",[],[61,160,62,161,75,162,84,163,164,86,165,113,57],"compiled model app scaffolding","product","lifecycle","demo","inference","ui"]