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Learning","资料检索、知识整理与学习辅导",5528,[],{"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",4154,229,11,7,829340,"2026-11-14 04:29:39",{"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",171,23,6,343689,"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 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04:13:45",{"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",674,17,232203,"2026-11-14 03:25:15",{"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. 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Use when training large MoE models with FP8\u002FINT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fmiles\u002FSKILL.md",[],[213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232],"training","rl","miles rl training","miles","FP8","Enterprise","SGLang","Transformers","learning","router","ready","alignment","slime","synthetic","megatron","Megatron-LM","throughput","production-ready","for","when","2026-11-10 10:12:30",{"skillNo":235,"name":236,"nameZh":13,"description":237,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":238,"categories":239,"keywords":240,"publishedAt":233},"01M6XG71DGBHT3N3HN35AFA4AW","llama-cpp","Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1\u002FM2\u002FM3 Macs, AMD\u002FIntel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fllama-cpp\u002FSKILL.md",[],[241,242,243,244,245,246,247,248,249,226,250,251,252,253,254,255,256,257,258,232],"cpp","Llama","llama cpp","memory","pytorch","NVIDIA","Inference Serving","apple","bit","silicon","CPU","GGUF","llama.cpp","CPU Inference","Edge Deployment","Non-NVIDIA","AMD GPUs","Intel GPUs",{"skillNo":260,"name":261,"nameZh":13,"description":262,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":263,"categories":264,"keywords":268,"publishedAt":233},"01M6XG6XB6A0C6TBYDVXERH6WD","groq-inference","Ultra-fast LLM inference on custom LPU hardware. OpenAI-compatible API at api.groq.com. Lowest latency in the industry (500-1000+ tok\u002Fs). Supports chat completions, vision, audio (Whisper STT + TTS), tool calling, JSON mode, and streaming. Free tier available. Inference only — no training.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fgroq\u002FSKILL.md",[265],{"categoryNo":266,"categoryName":267,"taxonomyType":53,"taxonomyTypeDesc":54},"01JBSEEDCAT000000000000016","图像与多媒体",[269,270,271,213,122,272,273,274,275,276,277,278,279,280,226,281,282,283,284,285],"inference","groq","groq inference","streaming","api","Audio","only","whisper","vision","free-tier","stt","free","mode","at","ultra","LPU","Low-Latency",{"skillNo":287,"name":288,"nameZh":13,"description":289,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":290,"categories":291,"keywords":292,"publishedAt":233},"01M6XG6NRWCNZ4JMFS10EXCQZK","gguf-quantization","GGUF format and llama.cpp quantization for efficient CPU\u002FGPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fml-inference\u002Fgguf\u002FSKILL.md",[],[293,294,252,295,296,241,297,298,299,300,248,249,226,250,251,253,254,301,231,232],"gguf quantization","Quantization","python","ml","Optimization","apple silicon","model","use","Model Compression",{"skillNo":303,"name":276,"nameZh":13,"description":304,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":305,"categories":306,"keywords":308,"publishedAt":233},"01M6XG6H6EM1RJEFBVN8TDEPRR","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\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fwhisper\u002FSKILL.md",[307],{"categoryNo":266,"categoryName":267,"taxonomyType":53,"taxonomyTypeDesc":54},[276,122,309,310,220,311,312,274,313,299,314,315,316,317,226,318,319,320,321,231],"speech","multimodal","ASR","Tools","text","speech recognition","general","multilingual","translation","english","identification","processing","Audio Processing",{"skillNo":323,"name":324,"nameZh":13,"description":325,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":326,"categories":327,"keywords":329,"publishedAt":233},"01M6XG67GRW8ZYF6V099HJGS0F","stable-diffusion-image-generation","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\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fstable-diffusion\u002FSKILL.md",[328],{"categoryNo":266,"categoryName":267,"taxonomyType":53,"taxonomyTypeDesc":54},[330,331,332,333,334,335,310,220,312,313,336,300,277,337,338,317,226,339,340,232],"image","Image Generation","Stable Diffusion","stable diffusion image generation","generation","diffusion","computer","images","Art","Accelerate","custom",{"skillNo":342,"name":343,"nameZh":13,"description":344,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":345,"categories":346,"keywords":347,"publishedAt":233},"01M6XG630KEY37P1PKK2CEFSR7","sentencepiece","Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences\u002Fsec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fsentencepiece\u002FSKILL.md",[],[343,142,244,348,220,312,313,316,349,350,351,226,352,353,354,355,356,357,358,232],"support","deterministic","algorithms","cjk","Tokenization","BPE","Unigram","Language-Independent","CJK Languages","Unicode","you",{"skillNo":360,"name":361,"nameZh":13,"description":362,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":363,"categories":364,"keywords":365,"publishedAt":233},"01M6XG5XQH3AZDQQXCFB6SD7FK","sentence-transformers","Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fsentence-transformers\u002FSKILL.md",[],[220,366,330,367,368,369,370,371,372,310,373,312,313,338,316,374,375,226,376,231],"sentence transformers","rag","semantic search","tasks","framework","search","embeddings","clustering","domain","domain-specific","embedding",{"skillNo":378,"name":379,"nameZh":13,"description":380,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":381,"categories":382,"keywords":384,"publishedAt":233},"01M6XG5Q3ZVCR7RSCT5N89R70C","segment-anything-model","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\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fsegment-anything\u002FSKILL.md",[383],{"categoryNo":266,"categoryName":267,"taxonomyType":53,"taxonomyTypeDesc":54},[299,385,386,387,330,388,310,389,220,312,390,336,277,337,391,392,226,358,231,232],"segment anything model","segment","segment anything","generate","ZeRO","segmentation","as","boxes",{"skillNo":394,"name":395,"nameZh":13,"description":396,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":397,"categories":398,"keywords":399,"publishedAt":233},"01M6XG5JMAGQGRRS4PFMACHG8A","qdrant-vector-search","High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fqdrant\u002FSKILL.md",[],[371,400,401,402,403,404,405,406,367,368,372,312,226,407,408,409,231,410,232,411],"vector","Vector Search","Qdrant","qdrant vector search","performance","rust","storage","high-performance","Hybrid Search","Scalable","hybrid","search engine",{"skillNo":413,"name":414,"nameZh":13,"description":415,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":416,"categories":417,"keywords":421,"publishedAt":233},"01M6XG5ETF6CQKAD226K108G8M","pinecone","Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (\u003C100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fpinecone\u002FSKILL.md",[418],{"categoryNo":419,"categoryName":420,"taxonomyType":53,"taxonomyTypeDesc":54},"01JBSEEDCAT000000000000010","后端与接口",[414,367,368,422,423,371,400,312,424,425,426,427,428,226,429,408,430,282,410,431],"infrastructure","database","vector database","auto","scaling","latency","recommendations","Managed Service","Auto-Scaling","managed",{"skillNo":433,"name":434,"nameZh":13,"description":435,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":436,"categories":437,"keywords":438,"publishedAt":233},"01M6XG58C0G9AD43TDZ1BH8TWR","outlines","Guarantee valid JSON\u002FXML\u002Fcode structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Foutlines\u002FSKILL.md",[],[439,440,441,348,442,443,444,445,446,447,448,220,449,312,450,451,452,453,226,231],"Outlines","safe","valid","json schema","code","library","Structured Generation","Pydantic","Local Models","Grammar-Based Generation","Type Safety","Safety","grammar","structure","local",{"skillNo":455,"name":456,"nameZh":13,"description":457,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":458,"categories":459,"keywords":460,"publishedAt":233},"01M6XG5362C2YB1Z57EZSNP2RF","nemo-guardrails","NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input\u002Foutput validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fnemo-guardrails\u002FSKILL.md",[],[461,462,463,464,465,466,467,468,469,470,450,471,223,224,226,472,473,230,474,475],"NeMo","Guardrails","NeMo Guardrails","Safety Alignment","Jailbreak Detection","Colang","Runtime Safety","Hallucination Detection","PII Filtering","pii","rails","LLM Applications","fact-checking","features","dsl",{"skillNo":477,"name":478,"nameZh":13,"description":479,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":480,"categories":481,"keywords":482,"publishedAt":233},"01M6XG4VSQ2WZQ2J54V6B05B0X","long-context","Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Flong-context\u002FSKILL.md",[],[483,484,485,486,487,488,489,372,220,312,490,491,226,320,492,493,494,495,496,232,497],"context","long context","long","llms","windows","documents","yarn","flash","strategies","Emerging Techniques","implementing","extend","interpolation","limits","extending",{"skillNo":499,"name":500,"nameZh":13,"description":501,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":502,"categories":503,"keywords":507,"publishedAt":233},"01M6XG4NEMAC27EAW99QTPKJAD","llm-as-judge-evaluation","Evaluate LLM outputs using frontier models as judges. Use for pairwise model comparison, quality scoring with custom rubrics, and automated evaluation pipelines. Covers position bias mitigation, statistical significance, and generating preference data for DPO\u002FRLHF.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fllm-as-judge-evaluation\u002FSKILL.md",[504],{"categoryNo":505,"categoryName":506,"taxonomyType":53,"taxonomyTypeDesc":54},"01JBSEEDCAT000000000000015","视觉设计",[508,391,509,510,511,512,122,513,312,299,514,515,226,516,517,518,519,520,521,522],"Evaluation","judge","llm as judge evaluation","design","assessment","data","rubric","scoring","quality scoring","LLM-as-Judge","Pairwise Comparison","Quality Assessment","Rubric Design","Model Comparison","Automated Evaluation",{"skillNo":524,"name":525,"nameZh":13,"description":526,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":527,"categories":528,"keywords":530,"publishedAt":233},"01M6XG4F0ADHQGPCVK3C1QRNGQ","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\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fllava\u002FSKILL.md",[529],{"categoryNo":266,"categoryName":267,"taxonomyType":53,"taxonomyTypeDesc":54},[525,330,531,369,310,220,312,532,533,534,535,277,226,536,537,538,539,540,541,231],"assistant","tuning","analysis","question","clip","Vision-Language","Visual Question Answering","Image Chat","image understanding","conversations","pillow",{"skillNo":543,"name":544,"nameZh":13,"description":545,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":546,"categories":547,"keywords":552,"publishedAt":233},"01M6XG47EZ594HHEHZ62HAG3CT","llamaindex","Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fllamaindex\u002FSKILL.md",[548,551],{"categoryNo":549,"categoryName":550,"taxonomyType":53,"taxonomyTypeDesc":54},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":23,"categoryName":24,"taxonomyType":6,"taxonomyTypeDesc":7},[553,554,367,400,310,555,556,300,557,558,559,472,560,561,562,563,564,565,566,231],"LlamaIndex","connectors","applications","knowledge","document","indexing","chatbots","Document Ingestion","Vector Indices","Query Engines","Knowledge Retrieval","Data Framework","Private Data","q&a",{"skillNo":568,"name":569,"nameZh":13,"description":570,"descriptionZh":13,"repositoryFullName":202,"ownerLogin":203,"ownerAvatarUrl":204,"ownerAccountNo":205,"ownerDisplayName":206,"repositoryNo":207,"starsCount":208,"licenseSpdxId":209,"licenseFileUrl":13,"sourceHtmlUrl":571,"categories":572,"keywords":574,"publishedAt":233},"01M6XG42TY15XSA3YQPKHE8X7B","llamaguard","Meta's 7-8B specialized moderation model for LLM input\u002Foutput filtering. 6 safety categories - violence\u002Fhate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fllamaguard\u002FSKILL.md",[573],{"categoryNo":30,"categoryName":31,"taxonomyType":6,"taxonomyTypeDesc":7},[575,220,464,576,577,462,578,579,580,581,463,312,450,299,582,224,583,226,584,585],"LlamaGuard","Content Moderation","Meta","Safety Classification","Input Filtering","Output Filtering","AI Safety","specialized","self","input","categories",10826,18]