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04:17:09",{"accountNo":154,"login":155,"displayName":156,"avatarUrl":157,"accountType":107,"accountTypeDesc":108,"profileDescription":158,"skillCount":159,"repoCount":160,"professionCount":52,"scenarioCount":53,"totalStars":161,"updatedAt":162},"01M6AC83DS133TPXFSP1DHNCC6","github","GitHub","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","How people build software.",940,28,238441,"2026-11-13 03:57:35",{"accountNo":164,"login":165,"displayName":166,"avatarUrl":167,"accountType":107,"accountTypeDesc":108,"profileDescription":13,"skillCount":168,"repoCount":169,"professionCount":52,"scenarioCount":53,"totalStars":170,"updatedAt":171},"01M697XGDPXJAVQ8391V2BCYTE","anthropics","Anthropic","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F76263028?v=4",674,17,231826,"2026-11-13 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Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33418,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fobservability-phoenix\u002FSKILL.md",[],[212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234],"observability","observability phoenix","research","debugging","tracing","monitoring","Evaluation","llm ops","Phoenix","Arize","OpenTelemetry","real-time","open","observability platform","time","platform","LLM Applications","insights","orchestra","for","source","when","running","2026-11-10 10:12:30",{"skillNo":237,"name":238,"nameZh":13,"description":239,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":240,"categories":241,"keywords":245,"publishedAt":235},"01M6ADVTMWQKDMKJN7Q6W9QZ02","observability-langsmith","LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fobservability-langsmith\u002FSKILL.md",[242],{"categoryNo":243,"categoryName":244,"taxonomyType":6,"taxonomyTypeDesc":7},"01JBSEEDCAT000000000000008","测试与质量",[212,246,247,248,214,215,216,217,218,219,249,250,251,252,225,227,253,228,230,231,254,233],"observability langsmith","LangSmith","testing","applications","model","ops","use","systematic","outputs",{"skillNo":256,"name":257,"nameZh":13,"description":258,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":259,"categories":260,"keywords":267,"publishedAt":235},"01M6ADVQPFHGEWS27TM4MMT0AC","multimodal-whisper","OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmultimodal-whisper\u002FSKILL.md",[261,264],{"categoryNo":262,"categoryName":263,"taxonomyType":6,"taxonomyTypeDesc":7},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":265,"categoryName":266,"taxonomyType":6,"taxonomyTypeDesc":7},"01JBSEEDCAT000000000000023","Skill与智能体开发",[268,269,270,214,125,271,272,273,274,275,250,276,277,278,279,280,281,282,283,230,231],"multimodal","multimodal whisper","whisper","speech","Transformers","ASR","Audio","text","speech recognition","general","multilingual","translation","english","identification","processing","Audio Processing",{"skillNo":285,"name":286,"nameZh":13,"description":287,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":288,"categories":289,"keywords":294,"publishedAt":235},"01M6ADVMBYXQ7NEYH4NH4WCD3E","multimodal-stable-diffusion","State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmultimodal-stable-diffusion\u002FSKILL.md",[290,293],{"categoryNo":291,"categoryName":292,"taxonomyType":6,"taxonomyTypeDesc":7},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":262,"categoryName":263,"taxonomyType":6,"taxonomyTypeDesc":7},[268,295,296,297,214,298,299,300,301,302,303,304,272,275,305,252,306,307,308,279,309,310,230,311,233],"multimodal stable diffusion","diffusion","image","state","HuggingFace","Image Generation","Stable Diffusion","Diffusers","Text-to-Image","Computer Vision","computer","vision","images","Art","Accelerate","with","custom",{"skillNo":313,"name":314,"nameZh":13,"description":315,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":316,"categories":317,"keywords":320,"publishedAt":235},"01M6ADVHF9CKKF5H7SSMBNPZ7Y","multimodal-segment-anything","Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmultimodal-segment-anything\u002FSKILL.md",[318,319],{"categoryNo":262,"categoryName":263,"taxonomyType":6,"taxonomyTypeDesc":7},{"categoryNo":265,"categoryName":266,"taxonomyType":6,"taxonomyTypeDesc":7},[268,321,322,323,297,214,324,325,326,272,327,250,305,306,307,328,329,330,331,230,231,233],"multimodal segment anything","segment","segment anything","generate","transfer","ZeRO","segmentation","as","boxes","Zero-Shot","you",{"skillNo":333,"name":334,"nameZh":13,"description":335,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":336,"categories":337,"keywords":339,"publishedAt":235},"01M6ADVEQ7F4DFPB4Y6DMQ33HT","multimodal-llava","Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna\u002FLLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmultimodal-llava\u002FSKILL.md",[338],{"categoryNo":262,"categoryName":263,"taxonomyType":6,"taxonomyTypeDesc":7},[268,340,341,297,342,214,343,344,272,345,346,347,348,306,349,350,351,352,353,354,230,231],"multimodal llava","llava","assistant","tasks","Llama","tuning","analysis","question","clip","Vision-Language","Visual Question Answering","Image Chat","image understanding","conversations","pillow",{"skillNo":356,"name":357,"nameZh":13,"description":358,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":359,"categories":360,"keywords":363,"publishedAt":235},"01M6ADVC6X4S555XFFBKPTZJR1","multimodal-clip","OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmultimodal-clip\u002FSKILL.md",[361,362],{"categoryNo":262,"categoryName":263,"taxonomyType":6,"taxonomyTypeDesc":7},{"categoryNo":80,"categoryName":81,"taxonomyType":58,"taxonomyTypeDesc":59},[268,364,348,297,214,343,125,365,366,326,367,272,368,345,275,250,306,277,349,352,354,230,231],"multimodal clip","search","content","Fine-Tuning","Content Moderation",{"skillNo":370,"name":371,"nameZh":13,"description":372,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":373,"categories":374,"keywords":376,"publishedAt":235},"01M6ADV9CRNXW75AW6YDWRA6DX","multimodal-blip-2","Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmultimodal-blip-2\u002FSKILL.md",[375],{"categoryNo":262,"categoryName":263,"taxonomyType":6,"taxonomyTypeDesc":7},[268,377,378,297,379,380,214,381,298,382,326,272,275,347,306,308,383,384,331,354,385,233],"multimodal blip 2","blip 2","llms","performance","training","framework","bridging","Training Framework","captioning",{"skillNo":387,"name":388,"nameZh":13,"description":389,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":390,"categories":391,"keywords":393,"publishedAt":235},"01M6ADV6JPD6RFRQVCQA14E0PW","multimodal-audiocraft","PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmultimodal-audiocraft\u002FSKILL.md",[392],{"categoryNo":262,"categoryName":263,"taxonomyType":6,"taxonomyTypeDesc":7},[268,394,395,214,396,397,324,398,272,274,399,275,400,401,402,403,404,331,405,406,233],"multimodal audiocraft","audiocraft","pytorch","create","library","music","Audio Generation","Text-to-Music","Text-to-Audio","MusicGen","music generation","audiogen","effects",{"skillNo":408,"name":409,"nameZh":13,"description":410,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":411,"categories":412,"keywords":413,"publishedAt":235},"01M6ADV3NTTD3M01D8DYVJ8XBE","model-architecture-torchtitan","Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-torchtitan\u002FSKILL.md",[],[414,415,250,416,417,214,418,396,381,419,420,421,344,422,423,424,425,426,427,428,230,311,429,233],"architecture","model architecture torchtitan","Model Architecture","torchtitan","distributed","compile","tensor","Distributed Training","pipeline","parallel","context","FSDP2","native","pp","at","checkpointing",{"skillNo":431,"name":432,"nameZh":13,"description":433,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":434,"categories":435,"keywords":436,"publishedAt":235},"01M6ADV0SAG64Q9DZCRQQ1D15X","model-architecture-rwkv","RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fmodel-architecture-rwkv\u002FSKILL.md",[],[414,437,250,416,438,439,440,214,441,442,443,444,445,446,272,423,424,447,448,226,449,450,451,428,230,452],"model architecture rwkv","rwkv","linux","inference","windows","linear","gpt","cache","office","NeMo","up","complexity","project","infer","2025","hybrid",{"skillNo":454,"name":455,"nameZh":13,"description":456,"descriptionZh":13,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":13,"sourceHtmlUrl":457,"categories":458,"keywords":459,"publishedAt":235},"01M6ADTYES2V3GWF8F21JY3Q2P","model-architecture-nanogpt","Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. 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