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Best for production embedding generation.","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\u002Fllm-tools\u002Fsentence-transformers\u002FSKILL.md",[],[212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231],"Transformers","sentence transformers","image","rag","semantic search","tasks","framework","search","embeddings","multimodal","clustering","Tools","text","Art","multilingual","domain","domain-specific","synthetic","embedding","for","2026-11-10 10:12:30",{"skillNo":234,"name":235,"nameZh":18,"description":236,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":237,"categories":238,"keywords":242,"publishedAt":232},"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",[239],{"categoryNo":240,"categoryName":241,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000016","图像与多媒体",[243,244,245,246,214,247,221,248,212,223,249,250,251,252,253,254,229,255,231,256],"model","segment anything model","segment","segment anything","generate","ZeRO","segmentation","computer","vision","images","as","boxes","you","when",{"skillNo":258,"name":259,"nameZh":18,"description":260,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":261,"categories":262,"keywords":263,"publishedAt":232},"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",[],[219,264,265,266,267,268,269,270,215,216,220,223,229,271,272,273,231,274,256,275],"vector","Vector Search","Qdrant","qdrant vector search","performance","rust","storage","high-performance","Hybrid Search","Scalable","hybrid","search engine",{"skillNo":277,"name":278,"nameZh":18,"description":279,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":280,"categories":281,"keywords":285,"publishedAt":232},"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",[282],{"categoryNo":283,"categoryName":284,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000010","后端与接口",[278,215,216,286,287,219,264,223,288,289,290,291,292,229,293,272,294,295,274,296],"infrastructure","database","vector database","auto","scaling","latency","recommendations","Managed Service","Auto-Scaling","at","managed",{"skillNo":298,"name":299,"nameZh":18,"description":300,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":301,"categories":302,"keywords":303,"publishedAt":232},"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",[],[304,305,306,307,308,309,310,311,312,313,314,212,315,223,316,317,318,319,229,231],"Outlines","safe","valid","support","json schema","code","library","Structured Generation","Pydantic","Local Models","Grammar-Based Generation","Type Safety","Safety","grammar","structure","local",{"skillNo":321,"name":322,"nameZh":18,"description":323,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":324,"categories":325,"keywords":326,"publishedAt":232},"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",[],[327,328,329,330,331,332,333,334,335,336,316,337,338,339,229,340,341,342,343,344],"NeMo","Guardrails","NeMo Guardrails","Safety Alignment","Jailbreak Detection","Colang","Runtime Safety","Hallucination Detection","PII Filtering","pii","rails","ready","alignment","LLM Applications","fact-checking","production-ready","features","dsl",{"skillNo":346,"name":347,"nameZh":18,"description":348,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":349,"categories":350,"keywords":351,"publishedAt":232},"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",[],[352,353,354,355,356,357,358,220,212,223,359,360,229,361,362,363,364,365,366,256,367],"context","long context","long","llms","windows","documents","yarn","flash","strategies","processing","Emerging Techniques","implementing","extend","interpolation","limits","extending",{"skillNo":369,"name":370,"nameZh":18,"description":371,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":372,"categories":373,"keywords":377,"publishedAt":232},"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",[374],{"categoryNo":375,"categoryName":376,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000015","视觉设计",[378,253,379,380,381,382,125,383,223,243,384,385,229,386,387,388,389,390,391,392],"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":394,"name":395,"nameZh":18,"description":396,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":397,"categories":398,"keywords":400,"publishedAt":232},"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",[399],{"categoryNo":240,"categoryName":241,"taxonomyType":58,"taxonomyTypeDesc":59},[395,214,401,217,221,212,223,402,403,404,405,251,229,406,407,408,409,410,411,231],"assistant","tuning","analysis","question","clip","Vision-Language","Visual Question Answering","Image Chat","image understanding","conversations","pillow",{"skillNo":413,"name":414,"nameZh":18,"description":415,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":416,"categories":417,"keywords":422,"publishedAt":232},"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",[418,421],{"categoryNo":419,"categoryName":420,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":28,"categoryName":29,"taxonomyType":11,"taxonomyTypeDesc":12},[423,424,215,264,221,425,426,427,428,429,430,340,431,432,433,434,435,436,437,231],"LlamaIndex","connectors","applications","knowledge","use","document","indexing","chatbots","Document Ingestion","Vector Indices","Query Engines","Knowledge Retrieval","Data Framework","Private Data","q&a",{"skillNo":439,"name":440,"nameZh":18,"description":441,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":442,"categories":443,"keywords":445,"publishedAt":232},"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",[444],{"categoryNo":35,"categoryName":36,"taxonomyType":11,"taxonomyTypeDesc":12},[446,212,330,447,448,328,449,450,451,452,329,223,316,243,453,339,454,229,455,456],"LlamaGuard","Content Moderation","Meta","Safety Classification","Input Filtering","Output Filtering","AI Safety","specialized","self","input","categories",{"skillNo":458,"name":459,"nameZh":18,"description":460,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":461,"categories":462,"keywords":466,"publishedAt":232},"01M6XG3YPSPE8RYYW1FPGT7VF7","langsmith-observability","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\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Flangsmith\u002FSKILL.md",[463],{"categoryNo":464,"categoryName":465,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000008","测试与质量",[467,468,469,470,471,472,473,378,223,425,243,427,474,475,476,229,340,231,477,256],"observability","LangSmith","langsmith observability","testing","debugging","tracing","monitoring","observability platform","platform","systematic","outputs",{"skillNo":479,"name":480,"nameZh":18,"description":481,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":482,"categories":483,"keywords":487,"publishedAt":232},"01M6XG3QDE0B1VJ6E9YZ0QFAZ7","langchain","Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Flangchain\u002FSKILL.md",[484],{"categoryNo":485,"categoryName":486,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000009","前端开发",[480,145,488,489,215,490,125,218,264,223,491,492,493,404,494,495,229,496,340,231],"memory","react","core","Providers","integrations","vector stores","autonomous","tool calling","prototyping",{"skillNo":498,"name":499,"nameZh":18,"description":500,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":501,"categories":502,"keywords":504,"publishedAt":232},"01M6XG3K0J6FP8P5RGCQ9WW9VT","instructor","Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Finstructor\u002FSKILL.md",[503],{"categoryNo":28,"categoryName":29,"taxonomyType":11,"taxonomyTypeDesc":12},[499,505,125,506,310,383,312,315,223,316,507,508,509,510,511,229,512,513,514,515],"structured data","streaming","parsing","prompt","results","type","structured output","output","with","retry","parse",{"skillNo":517,"name":518,"nameZh":18,"description":519,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":520,"categories":521,"keywords":522,"publishedAt":232},"01M6XG3BV0TEM7X49HFJ69VJYH","huggingface-tokenizers","Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in \u003C20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding\u002Ftruncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhuggingface-tokenizers\u002FSKILL.md",[],[523,524,268,269,525,212,223,339,229,271,526,527,528,529,530,531,532,255,533,256],"HuggingFace","huggingface tokenizers","training","Tokenization","BPE","WordPiece","Unigram","Fast Tokenization","Custom Tokenizer","Alignment Tracking","track",{"skillNo":535,"name":536,"nameZh":18,"description":537,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":538,"categories":539,"keywords":544,"publishedAt":232},"01M6XG3648QV2GNERDTSZ2YYPR","hugging-face-tool-builder","Use this skill when the user wants to build tool\u002Fscripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated\u002Fautomated. This Skill creates a reusable script to fetch, enrich or process data.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-tool-builder\u002FSKILL.md",[540,541],{"categoryNo":283,"categoryName":284,"taxonomyType":58,"taxonomyTypeDesc":59},{"categoryNo":542,"categoryName":543,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000023","Skill与智能体开发",[545,546,547,548,549,550,523,551,552,553,427,554,555,556,229,557,558,559,256,560],"tool","hugging face","builder","hugging face tool builder","face","MCP","api","fetch","process","reusable","build","task","enrich","calls","achieve","useful",{"skillNo":562,"name":563,"nameZh":18,"description":564,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":565,"categories":566,"keywords":567,"publishedAt":232},"01M6XG30REFJ011PH4ABWBE9HQ","hugging-face-cli","Execute Hugging Face Hub operations using the `hf` CLI. Use when the user needs to download models\u002Fdatasets\u002Fspaces, upload files to Hub repositories, create repos, manage local cache, or run compute jobs on HF infrastructure. Covers authentication, file transfers, repository creation, cache operations, and cloud compute.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fhugging-face-cli\u002FSKILL.md",[],[568,546,569,549,570,286,571,572,573,574,212,223,243,575,319,576,229,577,256,578,579],"cli","hugging face cli","repos","create","run","authentication","repository","files","creation","transfers","Model Download","Cloud Compute",{"skillNo":581,"name":582,"nameZh":18,"description":583,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":584,"categories":585,"keywords":590,"publishedAt":232},"01M6XG2W4RX55MNV6BER5JNZ1A","guidance","Control LLM output with regex and grammars, guarantee valid JSON\u002FXML\u002Fcode generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fguidance\u002FSKILL.md",[586,589],{"categoryNo":587,"categoryName":588,"taxonomyType":58,"taxonomyTypeDesc":59},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":542,"categoryName":543,"taxonomyType":58,"taxonomyTypeDesc":59},[582,306,591,309,218,592,212,223,317,508,593,594,595,511,229,596,597,598,599,600],"workflows","Code Generation","enforcement","enforce","regex","Constrained Generation","JSON Validation","Microsoft Research","Format Enforcement","formats",10822,18]