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Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.",{"claudeCode":15},"simpo-training",{"basePath":520,"githubOwner":20,"githubRepo":21,"locale":22,"slug":521,"type":23},"06-post-training/simpo","simpo",{"evaluate":523,"extract":527},{"promptVersionExtension":26,"promptVersionScoring":27,"score":112,"tags":524,"targetMarket":35,"tier":526},[228,525,64,293,453],"alignment","community",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[525,64,228,293,453],{"evaluatedAt":531,"extractAt":45,"updatedAt":531},1778696064925,{"_creationTime":533,"_id":534,"community":535,"display":536,"identity":540,"providers":543,"relations":548,"tags":549,"workflow":550},1778695116697.1812,"k175q9zh1nmf95xv33pe6z59w986mf29",{"reviewCount":11},{"description":537,"installMethods":538,"name":539,"sourceUrl":17},"Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.",{"claudeCode":15},"slime-rl-training",{"basePath":541,"githubOwner":20,"githubRepo":21,"locale":22,"slug":542,"type":23},"06-post-training/slime","slime",{"evaluate":544,"extract":547},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":545,"targetMarket":35,"tier":36},[454,482,481,546,453],"llm-training",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[546,482,453,454,481],{"evaluatedAt":551,"extractAt":45,"updatedAt":551},1778696094856,{"_creationTime":553,"_id":554,"community":555,"display":556,"identity":560,"providers":562,"relations":567,"tags":568,"workflow":569},1778695116697.1814,"k172mjb2axx0ngv0y12ecz1fnn86nsd9",{"reviewCount":11},{"description":557,"installMethods":558,"name":559,"sourceUrl":17},"Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.",{"claudeCode":15},"torchforge",{"basePath":561,"githubOwner":20,"githubRepo":21,"locale":22,"slug":559,"type":23},"06-post-training/torchforge",{"evaluate":563,"extract":566},{"promptVersionExtension":26,"promptVersionScoring":27,"score":28,"tags":564,"targetMarket":35,"tier":526},[454,118,565,160,34,384],"rl",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[160,34,118,454,384,565],{"evaluatedAt":570,"extractAt":45,"updatedAt":570},1778696118410,{"_creationTime":572,"_id":573,"community":574,"display":575,"identity":579,"providers":582,"relations":591,"tags":592,"workflow":593},1778695116697.1816,"k1765mxtemzz43015pwfy17tfs86n8hx",{"reviewCount":11},{"description":576,"installMethods":577,"name":578,"sourceUrl":17},"Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.",{"claudeCode":15},"fine-tuning-with-trl",{"basePath":580,"githubOwner":20,"githubRepo":21,"locale":22,"slug":581,"type":23},"06-post-training/trl-fine-tuning","trl-fine-tuning",{"evaluate":583,"extract":590},{"promptVersionExtension":26,"promptVersionScoring":27,"score":28,"tags":584,"targetMarket":35,"tier":36},[453,456,454,64,585,586,587,455,502,588,589],"sft","dpo","ppo","preference-alignment","huggingface-transformers",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[586,64,455,589,453,587,588,454,502,585,456],{"evaluatedAt":594,"extractAt":45,"updatedAt":594},1778696138274,{"_creationTime":596,"_id":597,"community":598,"display":599,"identity":603,"providers":606,"relations":610,"tags":611,"workflow":612},1778695116697.182,"k174px7xdx61w93gnasqyzyyc186mrjh",{"reviewCount":11},{"description":600,"installMethods":601,"name":602,"sourceUrl":17},"Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.",{"claudeCode":15},"verl-rl-training",{"basePath":604,"githubOwner":20,"githubRepo":21,"locale":22,"slug":605,"type":23},"06-post-training/verl","verl",{"evaluate":607,"extract":609},{"promptVersionExtension":26,"promptVersionScoring":27,"score":87,"tags":608,"targetMarket":35,"tier":36},[454,502,546,605,160],{"commitSha":38},{"parentExtensionId":5,"repoId":41},[160,546,454,502,605],{"evaluatedAt":613,"extractAt":45,"updatedAt":613},1778696160348,{"_creationTime":615,"_id":616,"community":617,"display":618,"identity":622,"providers":624,"relations":636,"tags":637,"workflow":638},1778695116697.1821,"k17abs34xk3q5kcbdbwtfyc07586nhnr",{"reviewCount":11},{"description":619,"installMethods":620,"name":621,"sourceUrl":17},"Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.",{"claudeCode":15},"constitutional-ai",{"basePath":623,"githubOwner":20,"githubRepo":21,"locale":22,"slug":621,"type":23},"07-safety-alignment/constitutional-ai",{"evaluate":625,"extract":635},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":626,"targetMarket":35,"tier":36},[627,621,628,629,630,631,632,633,634],"safety-alignment","rlif","self-critique","harmlessness","anthropic","ai-safety","rl-from-ai-feedback","claude",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[632,631,634,621,630,633,628,627,629],{"evaluatedAt":639,"extractAt":45,"updatedAt":639},1778696177284,{"_creationTime":641,"_id":642,"community":643,"display":644,"identity":648,"providers":650,"relations":660,"tags":661,"workflow":662},1778695116697.1824,"k176mrzrexp63nsd7hk2q29m4586mp5j",{"reviewCount":11},{"description":645,"installMethods":646,"name":647,"sourceUrl":17},"Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.",{"claudeCode":15},"llamaguard",{"basePath":649,"githubOwner":20,"githubRepo":21,"locale":22,"slug":647,"type":23},"07-safety-alignment/llamaguard",{"evaluate":651,"extract":659},{"promptVersionExtension":26,"promptVersionScoring":27,"score":112,"tags":652,"targetMarket":35,"tier":36},[627,647,653,654,655,656,657,658,632],"content-moderation","meta","guardrails","safety-classification","input-filtering","output-filtering",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[632,653,655,657,647,654,658,627,656],{"evaluatedAt":663,"extractAt":45,"updatedAt":663},1778696196077,{"_creationTime":665,"_id":666,"community":667,"display":668,"identity":672,"providers":675,"relations":684,"tags":685,"workflow":686},1778695116697.1826,"k175f7ghabj66hrrzzj3j91shs86n98d",{"reviewCount":11},{"description":669,"installMethods":670,"name":671,"sourceUrl":17},"NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.",{"claudeCode":15},"NeMo Guardrails",{"basePath":673,"githubOwner":20,"githubRepo":21,"locale":22,"slug":674,"type":23},"07-safety-alignment/nemo-guardrails","nemo-guardrails",{"evaluate":676,"extract":683},{"promptVersionExtension":26,"promptVersionScoring":27,"score":335,"tags":677,"targetMarket":35,"tier":36},[627,674,678,679,655,680,681,682],"nvidia","jailbreak-detection","colang","runtime-safety","llm-security",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[680,655,679,682,674,678,681,627],{"evaluatedAt":687,"extractAt":45,"updatedAt":687},1778696220016,{"_creationTime":689,"_id":690,"community":691,"display":692,"identity":696,"providers":699,"relations":708,"tags":709,"workflow":710},1778695116697.1829,"k17dqmn88r6143c75adk6b21mn86nxy9",{"reviewCount":11},{"description":693,"installMethods":694,"name":695,"sourceUrl":17},"Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, \u003C1% FPR. Fast (\u003C2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.",{"claudeCode":15},"Prompt Guard",{"basePath":697,"githubOwner":20,"githubRepo":21,"locale":22,"slug":698,"type":23},"07-safety-alignment/prompt-guard","prompt-guard",{"evaluate":700,"extract":707},{"promptVersionExtension":26,"promptVersionScoring":27,"score":701,"tags":702,"targetMarket":35,"tier":36},100,[627,703,679,704,705,706],"prompt-injection","input-validation","security","content-filtering",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[706,704,679,703,627,705],{"evaluatedAt":711,"extractAt":45,"updatedAt":711},1778696253838,{"_creationTime":713,"_id":714,"community":715,"display":716,"identity":720,"providers":723,"relations":727,"tags":728,"workflow":729},1778695116697.183,"k179mj61wk9j8xghbmjpc2z9pn86mdpg",{"reviewCount":11},{"description":717,"installMethods":718,"name":719,"sourceUrl":17},"Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.",{"claudeCode":15},"HuggingFace Accelerate",{"basePath":721,"githubOwner":20,"githubRepo":21,"locale":22,"slug":722,"type":23},"08-distributed-training/accelerate","accelerate",{"evaluate":724,"extract":726},{"promptVersionExtension":26,"promptVersionScoring":27,"score":335,"tags":725,"targetMarket":35,"tier":36},[160,184,118,230,34],{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[230,160,184,34,118],{"evaluatedAt":730,"extractAt":45,"updatedAt":730},1778696274398,{"_creationTime":732,"_id":733,"community":734,"display":735,"identity":739,"providers":742,"relations":749,"tags":750,"workflow":751},1778695116697.1833,"k179xtxadmmmtm224gv1h5c5bn86mcad",{"reviewCount":11},{"description":736,"installMethods":737,"name":738,"sourceUrl":17},"Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.",{"claudeCode":15},"Megatron-Core LLM Training",{"basePath":740,"githubOwner":20,"githubRepo":21,"locale":22,"slug":741,"type":23},"08-distributed-training/megatron-core","megatron-core",{"evaluate":743,"extract":748},{"promptVersionExtension":26,"promptVersionScoring":27,"score":112,"tags":744,"targetMarket":35,"tier":36},[741,546,160,745,746,747],"nvidia-h100","model-parallelism","production",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[160,546,741,746,745,747],{"evaluatedAt":752,"extractAt":45,"updatedAt":752},1778696292960,{"_creationTime":754,"_id":755,"community":756,"display":757,"identity":761,"providers":763,"relations":768,"tags":769,"workflow":770},1778695116697.1836,"k1794hb1vknsfyyc622b09en2x86mwmp",{"reviewCount":11},{"description":758,"installMethods":759,"name":760,"sourceUrl":17},"Adds PyTorch FSDP2 (fully_shard) to training scripts with correct init, sharding, mixed precision/offload config, and distributed checkpointing. Use when models exceed single-GPU memory or when you need DTensor-based sharding with DeviceMesh.",{"claudeCode":15},"pytorch-fsdp2",{"basePath":762,"githubOwner":20,"githubRepo":21,"locale":22,"slug":760,"type":23},"08-distributed-training/pytorch-fsdp2",{"evaluate":764,"extract":767},{"promptVersionExtension":26,"promptVersionScoring":27,"score":112,"tags":765,"targetMarket":35,"tier":36},[118,162,160,230,766],"gpu-memory",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[230,160,162,766,118],{"evaluatedAt":771,"extractAt":45,"updatedAt":771},1778696310901,{"_creationTime":773,"_id":774,"community":775,"display":776,"identity":780,"providers":782,"relations":787,"tags":788,"workflow":789},1778695116697.1838,"k17c6fx43mgkj95s4yzww50w5s86nb65",{"reviewCount":11},{"description":777,"installMethods":778,"name":779,"sourceUrl":17},"High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.",{"claudeCode":15},"pytorch-lightning",{"basePath":781,"githubOwner":20,"githubRepo":21,"locale":22,"slug":779,"type":23},"08-distributed-training/pytorch-lightning",{"evaluate":783,"extract":786},{"promptVersionExtension":26,"promptVersionScoring":27,"score":87,"tags":784,"targetMarket":35,"tier":36},[118,785,116,160,34,230],"lightning",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[230,160,785,34,118,116],{"evaluatedAt":790,"extractAt":45,"updatedAt":790},1778696329359,{"_creationTime":792,"_id":793,"community":794,"display":795,"identity":799,"providers":801,"relations":806,"tags":807,"workflow":808},1778695116697.184,"k179my3n83rwjxbt99x4qc3b0586mew8",{"reviewCount":11},{"description":796,"installMethods":797,"name":798,"sourceUrl":17},"Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.",{"claudeCode":15},"ray-train",{"basePath":800,"githubOwner":20,"githubRepo":21,"locale":22,"slug":798,"type":23},"08-distributed-training/ray-train",{"evaluate":802,"extract":805},{"promptVersionExtension":26,"promptVersionScoring":27,"score":87,"tags":803,"targetMarket":35,"tier":36},[160,32,433,118,434,184,804],"hyperparameter-tuning",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[160,184,804,32,118,433,434],{"evaluatedAt":809,"extractAt":45,"updatedAt":809},1778696346883,{"_creationTime":811,"_id":812,"community":813,"display":814,"identity":818,"providers":821,"relations":829,"tags":830,"workflow":831},1778695116697.1843,"k178f3yq9mbbnwm0ss7erfrzqh86mfk3",{"reviewCount":11},{"description":815,"installMethods":816,"name":817,"sourceUrl":17},"Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.",{"claudeCode":15},"Lambda Labs GPU Cloud",{"basePath":819,"githubOwner":20,"githubRepo":21,"locale":22,"slug":820,"type":23},"09-infrastructure/lambda-labs","lambda-labs",{"evaluate":822,"extract":828},{"promptVersionExtension":26,"promptVersionScoring":27,"score":335,"tags":823,"targetMarket":35,"tier":36},[824,825,826,34,116,827],"infrastructure","gpu","cloud","inference",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[826,825,827,824,34,116],{"evaluatedAt":832,"extractAt":45,"updatedAt":832},1778696367251,{"_creationTime":834,"_id":835,"community":836,"display":837,"identity":841,"providers":844,"relations":849,"tags":850,"workflow":851},1778695116697.1846,"k175v5fe4bt509v7dma1w27wbd86mwg7",{"reviewCount":11},{"description":838,"installMethods":839,"name":840,"sourceUrl":17},"Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.",{"claudeCode":15},"modal-serverless-gpu",{"basePath":842,"githubOwner":20,"githubRepo":21,"locale":22,"slug":843,"type":23},"09-infrastructure/modal","modal",{"evaluate":845,"extract":848},{"promptVersionExtension":26,"promptVersionScoring":27,"score":112,"tags":846,"targetMarket":35,"tier":36},[824,847,825,826,68,34],"serverless",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[826,68,825,824,34,847],{"evaluatedAt":852,"extractAt":45,"updatedAt":852},1778696387488,{"_creationTime":854,"_id":855,"community":856,"display":857,"identity":861,"providers":864,"relations":870,"tags":871,"workflow":872},1778695116697.1848,"k17cn5wpxpdj7qzt0hbg76ft3h86ma5f",{"reviewCount":11},{"description":858,"installMethods":859,"name":860,"sourceUrl":17},"Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.",{"claudeCode":15},"skypilot-multi-cloud-orchestration",{"basePath":862,"githubOwner":20,"githubRepo":21,"locale":22,"slug":863,"type":23},"09-infrastructure/skypilot","skypilot",{"evaluate":865,"extract":869},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":866,"targetMarket":35,"tier":36},[824,867,32,825,868,863,34],"multi-cloud","cost-optimization",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[868,825,824,34,867,32,863],{"evaluatedAt":873,"extractAt":45,"updatedAt":873},1778696405741,{"_creationTime":875,"_id":876,"community":877,"display":878,"identity":882,"providers":885,"relations":891,"tags":892,"workflow":893},1778695116697.185,"k177ps6tw2gbfd0md9vsmhykjh86n0bh",{"reviewCount":11},{"description":879,"installMethods":880,"name":881,"sourceUrl":17},"Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.",{"claudeCode":15},"AWQ Quantization",{"basePath":883,"githubOwner":20,"githubRepo":21,"locale":22,"slug":884,"type":23},"10-optimization/awq","awq",{"evaluate":886,"extract":890},{"promptVersionExtension":26,"promptVersionScoring":27,"score":112,"tags":887,"targetMarket":35,"tier":36},[293,888,228,827,825,889,117],"quantization","memory",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[825,827,228,889,293,117,888],{"evaluatedAt":894,"extractAt":45,"updatedAt":894},1778696438044,{"_creationTime":896,"_id":897,"community":898,"display":899,"identity":903,"providers":906,"relations":912,"tags":913,"workflow":914},1778695116697.1853,"k179hzvd8yk53mkqeba9pstyrx86n62y",{"reviewCount":11},{"description":900,"installMethods":901,"name":902,"sourceUrl":17},"Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.",{"claudeCode":15},"quantizing-models-bitsandbytes",{"basePath":904,"githubOwner":20,"githubRepo":21,"locale":22,"slug":905,"type":23},"10-optimization/bitsandbytes","bitsandbytes",{"evaluate":907,"extract":911},{"promptVersionExtension":26,"promptVersionScoring":27,"score":335,"tags":908,"targetMarket":35,"tier":36},[293,888,228,230,825,909,910],"memory-management","transformers",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[230,825,228,909,293,888,910],{"evaluatedAt":915,"extractAt":45,"updatedAt":915},1778696452835,{"_creationTime":917,"_id":918,"community":919,"display":920,"identity":924,"providers":927,"relations":933,"tags":934,"workflow":935},1778695116697.1855,"k17beccgqkx3ktzgbtmhj7efkd86n29f",{"reviewCount":11},{"description":921,"installMethods":922,"name":923,"sourceUrl":17},"Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.",{"claudeCode":15},"Flash Attention",{"basePath":925,"githubOwner":20,"githubRepo":21,"locale":22,"slug":926,"type":23},"10-optimization/flash-attention","flash-attention",{"evaluate":928,"extract":932},{"promptVersionExtension":26,"promptVersionScoring":27,"score":112,"tags":929,"targetMarket":35,"tier":36},[293,926,115,825,930,931],"performance","memory-efficiency",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[926,825,931,293,930,115],{"evaluatedAt":936,"extractAt":45,"updatedAt":936},1778696468496,{"_creationTime":938,"_id":939,"community":940,"display":941,"identity":945,"providers":948,"relations":955,"tags":956,"workflow":957},1778695116697.1858,"k17cfm34t6mxkmnmp1248gbkbs86nfm3",{"reviewCount":11},{"description":942,"installMethods":943,"name":944,"sourceUrl":17},"GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.",{"claudeCode":15},"GGUF Quantization",{"basePath":946,"githubOwner":20,"githubRepo":21,"locale":22,"slug":947,"type":23},"10-optimization/gguf","gguf",{"evaluate":949,"extract":954},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":950,"targetMarket":35,"tier":36},[947,888,951,952,953],"llama-cpp","cpu-inference","model-optimization",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[952,947,951,953,888],{"evaluatedAt":958,"extractAt":45,"updatedAt":958},1778696516392,{"_creationTime":960,"_id":961,"community":962,"display":963,"identity":967,"providers":969,"relations":977,"tags":978,"workflow":979},1778695116697.186,"k175f144wetzvs2x9fpvhqpmw586mp67",{"reviewCount":11},{"description":964,"installMethods":965,"name":966,"sourceUrl":17},"Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with \u003C2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.",{"claudeCode":15},"gptq",{"basePath":968,"githubOwner":20,"githubRepo":21,"locale":22,"slug":966,"type":23},"10-optimization/gptq",{"evaluate":970,"extract":976},{"promptVersionExtension":26,"promptVersionScoring":27,"score":335,"tags":971,"targetMarket":35,"tier":36},[293,966,888,972,453,274,973,974,65,975],"4-bit","consumer-gpus","fast-inference","group-wise-quantization",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[972,973,974,966,975,65,274,293,453,888],{"evaluatedAt":980,"extractAt":45,"updatedAt":980},1778696534192,{"_creationTime":982,"_id":983,"community":984,"display":985,"identity":989,"providers":992,"relations":996,"tags":997,"workflow":998},1778695116697.1863,"k174tqnyexpy6sn7ehrznn5g8986n0my",{"reviewCount":11},{"description":986,"installMethods":987,"name":988,"sourceUrl":17},"Half-Quadratic Quantization for LLMs without calibration data. 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Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.",{"claudeCode":15},"ML Training Recipes",{"basePath":1009,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1010,"type":23},"10-optimization/ml-training-recipes","ml-training-recipes",{"evaluate":1012,"extract":1017},{"promptVersionExtension":26,"promptVersionScoring":27,"score":87,"tags":1013,"targetMarket":35,"tier":36},[118,116,293,228,1014,1015,1016],"vision","biomedical","debugging",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1015,1016,228,293,118,116,1014],{"evaluatedAt":1021,"extractAt":45,"updatedAt":1021},1778696571220,{"_creationTime":1023,"_id":1024,"community":1025,"display":1026,"identity":1030,"providers":1033,"relations":1043,"tags":1044,"workflow":1045},1778695116697.1868,"k1711yt90txbs2bxwzc8nn8f6h86nnyh",{"reviewCount":11},{"description":1027,"installMethods":1028,"name":1029,"sourceUrl":17},"Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.",{"claudeCode":15},"BigCode Evaluation Harness",{"basePath":1031,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1032,"type":23},"11-evaluation/bigcode-evaluation-harness","bigcode-evaluation-harness",{"evaluate":1034,"extract":1042},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1035,"targetMarket":35,"tier":36},[1036,1037,1038,1039,1040,1041,117],"evaluation","code-generation","benchmarking","code-models","pass-k","bigcode",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1038,1041,1037,1039,1036,1040,117],{"evaluatedAt":1046,"extractAt":45,"updatedAt":1046},1778696587680,{"_creationTime":1048,"_id":1049,"community":1050,"display":1051,"identity":1055,"providers":1057,"relations":1063,"tags":1064,"workflow":1065},1778695116697.187,"k17acdb8pcw896dyrhxbx3ka6186nk86",{"reviewCount":11},{"description":1052,"installMethods":1053,"name":1054,"sourceUrl":17},"Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.",{"claudeCode":15},"lm-evaluation-harness",{"basePath":1056,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1054,"type":23},"11-evaluation/lm-evaluation-harness",{"evaluate":1058,"extract":1062},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1059,"targetMarket":35,"tier":36},[1036,228,1038,1060,1061],"academic","model-quality",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1060,1038,1036,228,1061],{"evaluatedAt":1066,"extractAt":45,"updatedAt":1066},1778696605768,{"_creationTime":1068,"_id":1069,"community":1070,"display":1071,"identity":1075,"providers":1078,"relations":1085,"tags":1086,"workflow":1087},1778695116697.1873,"k179196ne4yxz34r35wtwykzwh86mxtx",{"reviewCount":11},{"description":1072,"installMethods":1073,"name":1074,"sourceUrl":17},"Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.",{"claudeCode":15},"nemo-evaluator-sdk",{"basePath":1076,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1077,"type":23},"11-evaluation/nemo-evaluator","nemo-evaluator",{"evaluate":1079,"extract":1084},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1080,"targetMarket":35,"tier":36},[1036,228,1038,678,1081,1082,1083,480],"ne-mo","docker","slurm",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1038,1082,480,1036,228,1081,678,1083],{"evaluatedAt":1088,"extractAt":45,"updatedAt":1088},1778696624094,{"_creationTime":1090,"_id":1091,"community":1092,"display":1093,"identity":1096,"providers":1098,"relations":1107,"tags":1108,"workflow":1109},1778695116697.1875,"k17csgsgze3c135qze92egp8gx86ma52",{"reviewCount":11},{"description":1094,"installMethods":1095,"name":951,"sourceUrl":17},"Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.",{"claudeCode":15},{"basePath":1097,"githubOwner":20,"githubRepo":21,"locale":22,"slug":951,"type":23},"12-inference-serving/llama-cpp",{"evaluate":1099,"extract":1106},{"promptVersionExtension":26,"promptVersionScoring":27,"score":112,"tags":1100,"targetMarket":35,"tier":36},[1101,951,952,1102,1103,947,888,1104,1105],"inference-serving","apple-silicon","edge-deployment","non-nvidia","gpu-inference",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1102,952,1103,947,1105,1101,951,1104,888],{"evaluatedAt":1110,"extractAt":45,"updatedAt":1110},1778696642747,{"_creationTime":1112,"_id":1113,"community":1114,"display":1115,"identity":1119,"providers":1121,"relations":1129,"tags":1130,"workflow":1131},1778695116697.1877,"k1736tx4m13qnnxj4vwpwm8tsx86n4xc",{"reviewCount":11},{"description":1116,"installMethods":1117,"name":1118,"sourceUrl":17},"Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.",{"claudeCode":15},"SGLang",{"basePath":1120,"githubOwner":20,"githubRepo":21,"locale":22,"slug":481,"type":23},"12-inference-serving/sglang",{"evaluate":1122,"extract":1128},{"promptVersionExtension":26,"promptVersionScoring":27,"score":87,"tags":1123,"targetMarket":35,"tier":36},[1101,228,1124,1125,1126,930,68,1127],"structured-generation","prefix-caching","radixattention","api",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1127,68,1101,228,930,1125,1126,1124],{"evaluatedAt":1132,"extractAt":45,"updatedAt":1132},1778696659588,{"_creationTime":1134,"_id":1135,"community":1136,"display":1137,"identity":1141,"providers":1143,"relations":1151,"tags":1152,"workflow":1153},1778695116697.188,"k173qdxjdamys2gqnh6takycgh86m8aj",{"reviewCount":11},{"description":1138,"installMethods":1139,"name":1140,"sourceUrl":17},"Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.",{"claudeCode":15},"tensorrt-llm",{"basePath":1142,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1140,"type":23},"12-inference-serving/tensorrt-llm",{"evaluate":1144,"extract":1150},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1145,"targetMarket":35,"tier":36},[1101,1140,678,1146,1147,1148,747,478,479,1149],"inference-optimization","high-throughput","low-latency","multi-gpu",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[478,1147,1146,1101,479,1148,1149,678,747,1140],{"evaluatedAt":1154,"extractAt":45,"updatedAt":1154},1778696687593,{"_creationTime":1156,"_id":1157,"community":1158,"display":1159,"identity":1163,"providers":1165,"relations":1169,"tags":1170,"workflow":1171},1778695116697.1882,"k171v3d0cnswa9b5090933zp1n86mxnp",{"reviewCount":11},{"description":1160,"installMethods":1161,"name":1162,"sourceUrl":17},"Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.",{"claudeCode":15},"vLLM - High-Performance LLM Serving",{"basePath":1164,"githubOwner":20,"githubRepo":21,"locale":22,"slug":504,"type":23},"12-inference-serving/vllm",{"evaluate":1166,"extract":1168},{"promptVersionExtension":26,"promptVersionScoring":27,"score":335,"tags":1167,"targetMarket":35,"tier":36},[504,1101,228,1127,888,747],{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1127,1101,228,747,888,504],{"evaluatedAt":1172,"extractAt":45,"updatedAt":1172},1778696705273,{"_creationTime":1174,"_id":1175,"community":1176,"display":1177,"identity":1181,"providers":1184,"relations":1190,"tags":1191,"workflow":1192},1778695116697.1885,"k1765y0yyt4as5mxmz16k6eb8x86nwph",{"reviewCount":11},{"description":1178,"installMethods":1179,"name":1180,"sourceUrl":17},"Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform",{"claudeCode":15},"MLflow",{"basePath":1182,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1183,"type":23},"13-mlops/mlflow","mlflow",{"evaluate":1185,"extract":1189},{"promptVersionExtension":26,"promptVersionScoring":27,"score":28,"tags":1186,"targetMarket":35,"tier":36},[34,1183,1187,1188,68,117,187],"experiment-tracking","model-registry",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[68,1187,187,1183,34,1188,117],{"evaluatedAt":1193,"extractAt":45,"updatedAt":1193},1778696726048,{"_creationTime":1195,"_id":1196,"community":1197,"display":1198,"identity":1202,"providers":1205,"relations":1213,"tags":1214,"workflow":1215},1778695116697.189,"k177zc80prpahv37wcqgyw5d6x86m8pr",{"reviewCount":11},{"description":1199,"installMethods":1200,"name":1201,"sourceUrl":17},"Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit",{"claudeCode":15},"TensorBoard",{"basePath":1203,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1204,"type":23},"13-mlops/tensorboard","tensorboard",{"evaluate":1206,"extract":1212},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1207,"targetMarket":35,"tier":36},[34,1204,1208,1209,1210,1187,1211],"visualization","training-metrics","model-debugging","profiling",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1187,34,1210,1211,1204,1209,1208],{"evaluatedAt":1216,"extractAt":45,"updatedAt":1216},1778696759551,{"_creationTime":1218,"_id":1219,"community":1220,"display":1221,"identity":1225,"providers":1227,"relations":1231,"tags":1232,"workflow":1233},1778695116697.1892,"k17733znhx837s51nbm7r7nyhn86n8t9",{"reviewCount":11},{"description":1222,"installMethods":1223,"name":1224,"sourceUrl":17},"Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform",{"claudeCode":15},"weights-and-biases",{"basePath":1226,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1224,"type":23},"13-mlops/weights-and-biases",{"evaluate":1228,"extract":1230},{"promptVersionExtension":26,"promptVersionScoring":27,"score":28,"tags":1229,"targetMarket":35,"tier":36},[34,1224,1187,804,1188],{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1187,804,34,1188,1224],{"evaluatedAt":1234,"extractAt":45,"updatedAt":1234},1778696776588,{"_creationTime":1236,"_id":1237,"community":1238,"display":1239,"identity":1243,"providers":1246,"relations":1255,"tags":1256,"workflow":1257},1778695116697.1897,"k177w9yxe1dtpqe7t6pbkjknqx86nw5a",{"reviewCount":11},{"description":1240,"installMethods":1241,"name":1242,"sourceUrl":17},"Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.",{"claudeCode":15},"AutoGPT",{"basePath":1244,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1245,"type":23},"14-agents/autogpt","autogpt",{"evaluate":1247,"extract":1254},{"promptVersionExtension":26,"promptVersionScoring":27,"score":112,"tags":1248,"targetMarket":35,"tier":36},[1249,1250,1251,1252,1253],"agents","autonomous-agents","workflow-automation","ai-platform","development-toolkit",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1249,1252,1250,1253,1251],{"evaluatedAt":1258,"extractAt":45,"updatedAt":1258},1778696812699,{"_creationTime":1260,"_id":1261,"community":1262,"display":1263,"identity":1267,"providers":1270,"relations":1278,"tags":1279,"workflow":1280},1778695116697.19,"k1763mjye43bd8nxcne5cd55kh86mjap",{"reviewCount":11},{"description":1264,"installMethods":1265,"name":1266,"sourceUrl":17},"Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.",{"claudeCode":15},"crewai-multi-agent",{"basePath":1268,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1269,"type":23},"14-agents/crewai","crewai",{"evaluate":1271,"extract":1277},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1272,"targetMarket":35,"tier":36},[1249,1273,32,1274,1275,117,1276],"multi-agent","collaboration","workflows","ai",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1249,1276,1274,1273,32,117,1275],{"evaluatedAt":1281,"extractAt":45,"updatedAt":1281},1778696829041,{"_creationTime":1283,"_id":1284,"community":1285,"display":1286,"identity":1290,"providers":1293,"relations":1300,"tags":1301,"workflow":1302},1778695116697.1902,"k179tsjf4daszf4tmn0kadsr7986m8sc",{"reviewCount":11},{"description":1287,"installMethods":1288,"name":1289,"sourceUrl":17},"Framework for building LLM-powered applications with agents, chains, and RAG. 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Best for rapid prototyping and production deployments.",{"claudeCode":15},"LangChain",{"basePath":1291,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1292,"type":23},"14-agents/langchain","langchain",{"evaluate":1294,"extract":1299},{"promptVersionExtension":26,"promptVersionScoring":27,"score":87,"tags":1295,"targetMarket":35,"tier":36},[1249,1292,1296,1297,1298,117],"rag","llm-applications","framework",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1249,1298,1292,1297,117,1296],{"evaluatedAt":1303,"extractAt":45,"updatedAt":1303},1778696851211,{"_creationTime":1305,"_id":1306,"community":1307,"display":1308,"identity":1312,"providers":1315,"relations":1322,"tags":1323,"workflow":1324},1778695116697.1904,"k17d7gy0fbbpjcbqrsen95ky5186m2zr",{"reviewCount":11},{"description":1309,"installMethods":1310,"name":1311,"sourceUrl":17},"Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. 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Best for data-centric LLM applications.",{"claudeCode":15},"LlamaIndex",{"basePath":1313,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1314,"type":23},"14-agents/llamaindex","llamaindex",{"evaluate":1316,"extract":1321},{"promptVersionExtension":26,"promptVersionScoring":27,"score":112,"tags":1317,"targetMarket":35,"tier":36},[1296,1297,1318,1319,1320,1249],"document-ingestion","vector-indices","query-engines",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1249,1318,1297,1320,1296,1319],{"evaluatedAt":1325,"extractAt":45,"updatedAt":1325},1778696868984,{"_creationTime":1327,"_id":1328,"community":1329,"display":1330,"identity":1334,"providers":1336,"relations":1347,"tags":1348,"workflow":1349},1778695116697.1907,"k17d4xyt96jm8skj4t4rrwfaes86npdy",{"reviewCount":11},{"description":1331,"installMethods":1332,"name":1333,"sourceUrl":17},"Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.",{"claudeCode":15},"chroma",{"basePath":1335,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1333,"type":23},"15-rag/chroma",{"evaluate":1337,"extract":1346},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1338,"targetMarket":35,"tier":36},[1296,1333,1339,1340,1341,1342,1343,1344,1345],"vector-database","embeddings","semantic-search","open-source","self-hosted","document-retrieval","metadata-filtering",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1333,1344,1340,1345,1342,1296,1343,1341,1339],{"evaluatedAt":1350,"extractAt":45,"updatedAt":1350},1778696884117,{"_creationTime":1352,"_id":1353,"community":1354,"display":1355,"identity":1359,"providers":1361,"relations":1370,"tags":1371,"workflow":1372},1778695116697.191,"k17647xpe7a17vwe4zcvgr328n86mttd",{"reviewCount":11},{"description":1356,"installMethods":1357,"name":1358,"sourceUrl":17},"Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.",{"claudeCode":15},"faiss",{"basePath":1360,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1358,"type":23},"15-rag/faiss",{"evaluate":1362,"extract":1369},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1363,"targetMarket":35,"tier":36},[1296,1358,1364,1365,1366,405,1367,1368],"similarity-search","vector-search","facebook-ai","billion-scale","k-nn",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1367,1366,1358,405,1368,1296,1364,1365],{"evaluatedAt":1373,"extractAt":45,"updatedAt":1373},1778696898243,{"_creationTime":1375,"_id":1376,"community":1377,"display":1378,"identity":1382,"providers":1384,"relations":1391,"tags":1392,"workflow":1393},1778695116697.1912,"k17epqqa0kaec4jvczzw2kmqe186mynw",{"reviewCount":11},{"description":1379,"installMethods":1380,"name":1381,"sourceUrl":17},"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.",{"claudeCode":15},"pinecone",{"basePath":1383,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1381,"type":23},"15-rag/pinecone",{"evaluate":1385,"extract":1390},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1386,"targetMarket":35,"tier":36},[1296,1381,1339,1387,847,1388,747,1389],"managed-service","hybrid-search","auto-scaling",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1389,1388,1387,1381,747,1296,847,1339],{"evaluatedAt":1394,"extractAt":45,"updatedAt":1394},1778696915439,{"_creationTime":1396,"_id":1397,"community":1398,"display":1399,"identity":1403,"providers":1406,"relations":1410,"tags":1411,"workflow":1412},1778695116697.1914,"k179s0d87zk0pj5e5qe6nqatbh86n4by",{"reviewCount":11},{"description":1400,"installMethods":1401,"name":1402,"sourceUrl":17},"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.",{"claudeCode":15},"Qdrant Vector Search",{"basePath":1404,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1405,"type":23},"15-rag/qdrant","qdrant",{"evaluate":1407,"extract":1409},{"promptVersionExtension":26,"promptVersionScoring":27,"score":112,"tags":1408,"targetMarket":35,"tier":36},[1296,1365,1405,1341,1340,1364,747],{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1340,747,1405,1296,1341,1364,1365],{"evaluatedAt":1413,"extractAt":45,"updatedAt":1413},1778696934816,{"_creationTime":1415,"_id":1416,"community":1417,"display":1418,"identity":1422,"providers":1424,"relations":1431,"tags":1432,"workflow":1433},1778695116697.1917,"k17fqrntdzvsm1n3cg0dmpbb8586m8sp",{"reviewCount":11},{"description":1419,"installMethods":1420,"name":1421,"sourceUrl":17},"Framework for state-of-the-art sentence, text, and image embeddings. 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Best for production embedding generation.",{"claudeCode":15},"sentence-transformers",{"basePath":1423,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1421,"type":23},"15-rag/sentence-transformers",{"evaluate":1425,"extract":1430},{"promptVersionExtension":26,"promptVersionScoring":27,"score":1426,"tags":1427,"targetMarket":35,"tier":36},94,[1340,1428,1296,209,1429,117,230],"semantic-similarity","multimodal",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[230,1340,209,1429,117,1296,1428],{"evaluatedAt":1434,"extractAt":45,"updatedAt":1434},1778696951240,{"_creationTime":1436,"_id":1437,"community":1438,"display":1439,"identity":1443,"providers":1446,"relations":1455,"tags":1456,"workflow":1457},1778695116697.192,"k175whngr5067490h8frd0fv2986nhje",{"reviewCount":11},{"description":1440,"installMethods":1441,"name":1442,"sourceUrl":17},"Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming",{"claudeCode":15},"DSPy",{"basePath":1444,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1445,"type":23},"16-prompt-engineering/dspy","dspy",{"evaluate":1447,"extract":1454},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1448,"targetMarket":35,"tier":36},[1449,1445,1450,1296,1249,1451,1452,1453],"prompt-engineering","declarative-programming","prompt-optimization","lm-programming","stanford-nlp",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1249,1450,1445,1452,1449,1451,1296,1453],{"evaluatedAt":1458,"extractAt":45,"updatedAt":1458},1778696973241,{"_creationTime":1460,"_id":1461,"community":1462,"display":1463,"identity":1467,"providers":1469,"relations":1478,"tags":1479,"workflow":1480},1778695116697.1921,"k176ntgt0hy47s5ks62yw864w986mtmq",{"reviewCount":11},{"description":1464,"installMethods":1465,"name":1466,"sourceUrl":17},"Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework",{"claudeCode":15},"guidance",{"basePath":1468,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1466,"type":23},"16-prompt-engineering/guidance",{"evaluate":1470,"extract":1477},{"promptVersionExtension":26,"promptVersionScoring":27,"score":87,"tags":1471,"targetMarket":35,"tier":36},[1449,1466,1472,1473,1474,1475,1476],"constrained-generation","structured-output","json-validation","grammar","llm-workflows",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1472,1475,1466,1474,1476,1449,1473],{"evaluatedAt":1481,"extractAt":45,"updatedAt":1481},1778696989725,{"_creationTime":1483,"_id":1484,"community":1485,"display":1486,"identity":1490,"providers":1493,"relations":1503,"tags":1504,"workflow":1505},1778695116697.1924,"k1714hg6p3104jz49mqr6yn47s86nz7n",{"reviewCount":11},{"description":1487,"installMethods":1488,"name":1489,"sourceUrl":17},"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 - 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dottxt.ai's structured generation library",{"claudeCode":15},"outlines",{"basePath":1516,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1514,"type":23},"16-prompt-engineering/outlines",{"evaluate":1518,"extract":1523},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1519,"targetMarket":35,"tier":36},[1449,1124,1520,1496,1521,1146,1522],"json","local-models","data-validation",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1522,1146,1520,1521,1449,1496,1124],{"evaluatedAt":1527,"extractAt":45,"updatedAt":1527},1778697030986,{"_creationTime":1529,"_id":1530,"community":1531,"display":1532,"identity":1536,"providers":1539,"relations":1548,"tags":1549,"workflow":1550},1778695116697.1929,"k176f565j7tyetjxk9sgbjcqp186ndk5",{"reviewCount":11},{"description":1533,"installMethods":1534,"name":1535,"sourceUrl":17},"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.",{"claudeCode":15},"LangSmith Observability",{"basePath":1537,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1538,"type":23},"17-observability/langsmith","langsmith",{"evaluate":1540,"extract":1547},{"promptVersionExtension":26,"promptVersionScoring":27,"score":87,"tags":1541,"targetMarket":35,"tier":36},[1542,1538,1543,1036,1544,1545,1016,1546],"observability","tracing","monitoring","llmops","testing",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1016,1036,1538,1545,1544,1542,1546,1543],{"evaluatedAt":1551,"extractAt":45,"updatedAt":1551},1778697046926,{"_creationTime":1553,"_id":1554,"community":1555,"display":1556,"identity":1560,"providers":1563,"relations":1568,"tags":1569,"workflow":1570},1778695116697.193,"k17ekv2rgcxsb3nz7mhx9xwjz586m8rc",{"reviewCount":11},{"description":1557,"installMethods":1558,"name":1559,"sourceUrl":17},"Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.",{"claudeCode":15},"Phoenix",{"basePath":1561,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1562,"type":23},"17-observability/phoenix","phoenix",{"evaluate":1564,"extract":1567},{"promptVersionExtension":26,"promptVersionScoring":27,"score":112,"tags":1565,"targetMarket":35,"tier":36},[1542,1562,1543,1036,1544,1545,1566],"otel",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1036,1545,1544,1542,1566,1562,1543],{"evaluatedAt":1571,"extractAt":45,"updatedAt":1571},1778697063356,{"_creationTime":1573,"_id":1574,"community":1575,"display":1576,"identity":1580,"providers":1583,"relations":1592,"tags":1593,"workflow":1594},1778695116697.1934,"k173jbad5atp31e1sv0k5rg20986n03g",{"reviewCount":11},{"description":1577,"installMethods":1578,"name":1579,"sourceUrl":17},"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.",{"claudeCode":15},"audiocraft-audio-generation",{"basePath":1581,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1582,"type":23},"18-multimodal/audiocraft","audiocraft",{"evaluate":1584,"extract":1591},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1585,"targetMarket":35,"tier":36},[1429,1586,1587,1588,118,1589,1590],"audio-generation","text-to-music","text-to-audio","musicgen","audiogen",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1586,1590,1429,1589,118,1588,1587],{"evaluatedAt":1595,"extractAt":45,"updatedAt":1595},1778697080825,{"_creationTime":1597,"_id":1598,"community":1599,"display":1600,"identity":1604,"providers":1607,"relations":1615,"tags":1616,"workflow":1617},1778695116697.1936,"k173nxmkvfxt0ce7mxj78mx0fs86ngfd",{"reviewCount":11},{"description":1601,"installMethods":1602,"name":1603,"sourceUrl":17},"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.",{"claudeCode":15},"blip-2-vision-language",{"basePath":1605,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1606,"type":23},"18-multimodal/blip-2","blip-2",{"evaluate":1608,"extract":1614},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1609,"targetMarket":35,"tier":36},[1429,1610,1611,1612,1613],"vision-language","image-captioning","vqa","zero-shot",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1611,1429,1610,1612,1613],{"evaluatedAt":1618,"extractAt":45,"updatedAt":1618},1778697096822,{"_creationTime":1620,"_id":1621,"community":1622,"display":1623,"identity":1627,"providers":1629,"relations":1637,"tags":1638,"workflow":1639},1778695116697.1938,"k176y5p43xd35tm5nmzbyxe39x86md3j",{"reviewCount":11},{"description":1624,"installMethods":1625,"name":1626,"sourceUrl":17},"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.",{"claudeCode":15},"clip",{"basePath":1628,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1626,"type":23},"18-multimodal/clip",{"evaluate":1630,"extract":1636},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1631,"targetMarket":35,"tier":526},[1429,1626,1610,1613,1632,1633,1634,1635],"image-classification","openai","image-search","cross-modal-retrieval",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1626,1635,1632,1634,1429,1633,1610,1613],{"evaluatedAt":1640,"extractAt":45,"updatedAt":1640},1778697115985,{"_creationTime":1642,"_id":1643,"community":1644,"display":1645,"identity":1649,"providers":1652,"relations":1660,"tags":1661,"workflow":1662},1778695116697.194,"k175kv1whd1wnmz0tsxpyrsh0s86nhqz",{"reviewCount":11},{"description":1646,"installMethods":1647,"name":1648,"sourceUrl":17},"Evaluates NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments. Use when setting up cosmos-policy for robot manipulation evaluation, running headless GPU evaluations with EGL rendering, or profiling inference latency on cluster or local GPU machines.",{"claudeCode":15},"evaluating-cosmos-policy",{"basePath":1650,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1651,"type":23},"18-multimodal/cosmos-policy","cosmos-policy",{"evaluate":1653,"extract":1659},{"promptVersionExtension":26,"promptVersionScoring":27,"score":335,"tags":1654,"targetMarket":35,"tier":36},[1651,1655,1656,1036,1211,1657,1658],"robotics","simulation","libero","robocasa",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1651,1036,1657,1211,1658,1655,1656],{"evaluatedAt":1663,"extractAt":45,"updatedAt":1663},1778697132693,{"_creationTime":1665,"_id":1666,"community":1667,"display":1668,"identity":1672,"providers":1674,"relations":1681,"tags":1682,"workflow":1683},1778695116697.1943,"k171rd0hzevc8vzgq5ysxp86y586my9a",{"reviewCount":11},{"description":1669,"installMethods":1670,"name":1671,"sourceUrl":17},"Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA 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.",{"claudeCode":15},"llava",{"basePath":1673,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1671,"type":23},"18-multimodal/llava",{"evaluate":1675,"extract":1680},{"promptVersionExtension":26,"promptVersionScoring":27,"score":28,"tags":1676,"targetMarket":35,"tier":36},[1429,1610,1677,1678,1612,1679],"image-analysis","conversational-ai","instruction-tuning",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1678,1677,1679,1429,1610,1612],{"evaluatedAt":1684,"extractAt":45,"updatedAt":1684},1778697156302,{"_creationTime":1686,"_id":1687,"community":1688,"display":1689,"identity":1693,"providers":1696,"relations":1703,"tags":1704,"workflow":1705},1778695116697.1946,"k1781e28f4apa5vr58aen64trh86ng1t",{"reviewCount":11},{"description":1690,"installMethods":1691,"name":1692,"sourceUrl":17},"Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging norm stats and GPU memory issues.",{"claudeCode":15},"OpenPI Fine-Tuning and Serving",{"basePath":1694,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1695,"type":23},"18-multimodal/openpi","openpi",{"evaluate":1697,"extract":1702},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1698,"targetMarket":35,"tier":36},[1695,1699,1655,1700,118,64,1701],"physical-intelligence","jax","policy-serving",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[64,1700,1695,1699,1701,118,1655],{"evaluatedAt":1706,"extractAt":45,"updatedAt":1706},1778697178772,{"_creationTime":1708,"_id":1709,"community":1710,"display":1711,"identity":1715,"providers":1718,"relations":1725,"tags":1726,"workflow":1727},1778695116697.1948,"k17eakr1pvtg3b61k8wytq66f586mjb9",{"reviewCount":11},{"description":1712,"installMethods":1713,"name":1714,"sourceUrl":17},"Fine-tunes and evaluates OpenVLA-OFT and OpenVLA-OFT+ policies for robot action generation with continuous action heads, LoRA adaptation, and FiLM conditioning on LIBERO simulation and ALOHA real-world setups. Use when reproducing OpenVLA-OFT paper results, training custom VLA action heads (L1 or diffusion), deploying server-client inference for ALOHA, or debugging normalization, LoRA merge, and cross-GPU issues.",{"claudeCode":15},"OpenVLA-OFT Fine-tuning and Evaluation",{"basePath":1716,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1717,"type":23},"18-multimodal/openvla-oft","openvla-oft",{"evaluate":1719,"extract":1724},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1720,"targetMarket":35,"tier":36},[1655,64,1721,1722,65,1656,1723],"openvla","action-generation","real-world-deployment",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1722,64,65,1721,1723,1655,1656],{"evaluatedAt":1728,"extractAt":45,"updatedAt":1728},1778697204840,{"_creationTime":1730,"_id":1731,"community":1732,"display":1733,"identity":1737,"providers":1740,"relations":1747,"tags":1748,"workflow":1749},1778695116697.195,"k17b19qdkvp8hejjtcea8mhcqh86nkjy",{"reviewCount":11},{"description":1734,"installMethods":1735,"name":1736,"sourceUrl":17},"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.",{"claudeCode":15},"segment-anything-model",{"basePath":1738,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1739,"type":23},"18-multimodal/segment-anything","segment-anything",{"evaluate":1741,"extract":1746},{"promptVersionExtension":26,"promptVersionScoring":27,"score":87,"tags":1742,"targetMarket":35,"tier":36},[1743,1744,1745,1429,230],"image-segmentation","computer-vision","zero-shot-learning",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1744,230,1743,1429,1745],{"evaluatedAt":1750,"extractAt":45,"updatedAt":1750},1778697221812,{"_creationTime":1752,"_id":1753,"community":1754,"display":1755,"identity":1759,"providers":1762,"relations":1768,"tags":1769,"workflow":1770},1778695116697.1953,"k179zmw8s6dtx47x9dw6p6vzzn86nzns",{"reviewCount":11},{"description":1756,"installMethods":1757,"name":1758,"sourceUrl":17},"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.",{"claudeCode":15},"stable-diffusion-image-generation",{"basePath":1760,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1761,"type":23},"18-multimodal/stable-diffusion","stable-diffusion",{"evaluate":1763,"extract":1767},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1764,"targetMarket":35,"tier":36},[1765,1761,184,1766,1429,117],"image-generation","diffusers",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1766,184,1765,1429,117,1761],{"evaluatedAt":1771,"extractAt":45,"updatedAt":1771},1778697269373,{"_creationTime":1773,"_id":1774,"community":1775,"display":1776,"identity":1780,"providers":1783,"relations":1793,"tags":1794,"workflow":1795},1778695116697.1956,"k172v56etdnpjkpvsffadw2p6d86nhwm",{"reviewCount":11},{"description":1777,"installMethods":1778,"name":1779,"sourceUrl":17},"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.",{"claudeCode":15},"Whisper",{"basePath":1781,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1782,"type":23},"18-multimodal/whisper","whisper",{"evaluate":1784,"extract":1792},{"promptVersionExtension":26,"promptVersionScoring":27,"score":335,"tags":1785,"targetMarket":35,"tier":36},[1782,1786,1787,1429,209,1633,1788,1789,1790,1791],"speech-recognition","asr","speech-to-text","transcription","translation","audio-processing",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1787,1791,209,1429,1633,1786,1788,1789,1790,1782],{"evaluatedAt":1796,"extractAt":45,"updatedAt":1796},1778697289684,{"_creationTime":1798,"_id":1799,"community":1800,"display":1801,"identity":1805,"providers":1807,"relations":1814,"tags":1815,"workflow":1816},1778695116697.1958,"k17eh55r6q5rr0f4ab6dvgcaqn86mj71",{"reviewCount":11},{"description":1802,"installMethods":1803,"name":1804,"sourceUrl":17},"Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.",{"claudeCode":15},"knowledge-distillation",{"basePath":1806,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1804,"type":23},"19-emerging-techniques/knowledge-distillation",{"evaluate":1808,"extract":1813},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1809,"targetMarket":35,"tier":36},[1804,1810,1811,228,1812],"model-compression","teacher-student","emerging-techniques",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1812,1804,228,1810,1811],{"evaluatedAt":1817,"extractAt":45,"updatedAt":1817},1778697308723,{"_creationTime":1819,"_id":1820,"community":1821,"display":1822,"identity":1825,"providers":1827,"relations":1837,"tags":1838,"workflow":1839},1778695116697.196,"k179k4wsgxzdnqddccjpwrgbdd86n72h",{"reviewCount":11},{"description":1823,"installMethods":1824,"name":92,"sourceUrl":17},"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.",{"claudeCode":15},{"basePath":1826,"githubOwner":20,"githubRepo":21,"locale":22,"slug":92,"type":23},"19-emerging-techniques/long-context",{"evaluate":1828,"extract":1836},{"promptVersionExtension":26,"promptVersionScoring":27,"score":112,"tags":1829,"targetMarket":35,"tier":36},[1812,92,1830,1831,1832,1833,1834,1835,228],"rope","yarn","alibi","position-interpolation","positional-encoding","transformer-models",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1832,1812,228,92,1833,1834,1830,1835,1831],{"evaluatedAt":1840,"extractAt":45,"updatedAt":1840},1778697331128,{"_creationTime":1842,"_id":1843,"community":1844,"display":1845,"identity":1849,"providers":1851,"relations":1856,"tags":1857,"workflow":1858},1778695116697.1963,"k1744nb0bbyzx51yehxz6jh9as86mtar",{"reviewCount":11},{"description":1846,"installMethods":1847,"name":1848,"sourceUrl":17},"Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.",{"claudeCode":15},"model-merging",{"basePath":1850,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1848,"type":23},"19-emerging-techniques/model-merging",{"evaluate":1852,"extract":1855},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1853,"targetMarket":35,"tier":36},[1812,1848,1854,228,251],"mergekit",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[251,1812,228,1854,1848],{"evaluatedAt":1859,"extractAt":45,"updatedAt":1859},1778697347593,{"_creationTime":1861,"_id":1862,"community":1863,"display":1864,"identity":1868,"providers":1870,"relations":1878,"tags":1879,"workflow":1880},1778695116697.1965,"k176h98y72khpmx6167b5v7fw586ngzh",{"reviewCount":11},{"description":1865,"installMethods":1866,"name":1867,"sourceUrl":17},"Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.",{"claudeCode":15},"model-pruning",{"basePath":1869,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1867,"type":23},"19-emerging-techniques/model-pruning",{"evaluate":1871,"extract":1877},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1872,"targetMarket":35,"tier":36},[1867,1873,1146,1874,1875,1876,251],"llm-compression","wanda","sparsegpt","sparsity",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[251,1146,1873,1867,1875,1876,1874],{"evaluatedAt":1881,"extractAt":45,"updatedAt":1881},1778697364320,{"_creationTime":1883,"_id":1884,"community":1885,"display":1886,"identity":1890,"providers":1893,"relations":1902,"tags":1903,"workflow":1904},1778695116697.1968,"k172vdev3fzycdefapcv87gnqh86nwg8",{"reviewCount":11},{"description":1887,"installMethods":1888,"name":1889,"sourceUrl":17},"Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.",{"claudeCode":15},"MoE Training",{"basePath":1891,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1892,"type":23},"19-emerging-techniques/moe-training","moe-training",{"evaluate":1894,"extract":1901},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1895,"targetMarket":35,"tier":36},[1896,477,1897,184,1898,1899,1900],"mixture-of-experts","deepspeed","model-training","sparse-architectures","expert-parallelism",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1897,1900,184,1896,1898,477,1899],{"evaluatedAt":1905,"extractAt":45,"updatedAt":1905},1778697383263,{"_creationTime":1907,"_id":1908,"community":1909,"display":1910,"identity":1914,"providers":1917,"relations":1924,"tags":1925,"workflow":1926},1778695116697.197,"k1745rycgcgf0vcxm45n3r9x1h86mnbk",{"reviewCount":11},{"description":1911,"installMethods":1912,"name":1913,"sourceUrl":17},"Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.",{"claudeCode":15},"Speculative Decoding",{"basePath":1915,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1916,"type":23},"19-emerging-techniques/speculative-decoding","speculative-decoding",{"evaluate":1918,"extract":1923},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1919,"targetMarket":35,"tier":36},[1812,1916,1920,1921,1146,1922,930],"medusa","lookahead-decoding","llm-inference",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1812,1146,1922,1921,1920,930,1916],{"evaluatedAt":1927,"extractAt":45,"updatedAt":1927},1778697400190,{"_creationTime":1929,"_id":1930,"community":1931,"display":1932,"identity":1936,"providers":1939,"relations":1950,"tags":1951,"workflow":1952},1778695116697.1973,"k17f0ys3spnm7kccbpgnr3axmn86naty",{"reviewCount":11},{"description":1933,"installMethods":1934,"name":1935,"sourceUrl":17},"Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.",{"claudeCode":15},"Academic Plotting for ML Papers",{"basePath":1937,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1938,"type":23},"20-ml-paper-writing/academic-plotting","academic-plotting",{"evaluate":1940,"extract":1949},{"promptVersionExtension":26,"promptVersionScoring":27,"score":1426,"tags":1941,"targetMarket":35,"tier":36},[1942,1208,1943,1944,1945,1946,1947,1948],"academic-writing","matplotlib","seaborn","plotting","figures","diagrams","gemini",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1942,1947,1946,1948,1943,1945,1944,1208],{"evaluatedAt":1953,"extractAt":45,"updatedAt":1953},1778697420942,{"_creationTime":1955,"_id":1956,"community":1957,"display":1958,"identity":1962,"providers":1965,"relations":1976,"tags":1977,"workflow":1978},1778695116697.1975,"k173mxkapfej2d5crv2zbwvc9986mx6e",{"reviewCount":11},{"description":1959,"installMethods":1960,"name":1961,"sourceUrl":17},"Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. For systems venues (OSDI, NSDI, ASPLOS, SOSP), use systems-paper-writing instead.",{"claudeCode":15},"ML Paper Writing",{"basePath":1963,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1964,"type":23},"20-ml-paper-writing/ml-paper-writing","ml-paper-writing",{"evaluate":1966,"extract":1975},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1967,"targetMarket":35,"tier":36},[1942,1968,1969,1970,384,1971,1972,1973,1974],"paper-writing","latex","citations","neurips","icml","iclr","acl",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1942,1974,1970,1973,1972,1969,1971,1968,384],{"evaluatedAt":1979,"extractAt":45,"updatedAt":1979},1778697448935,{"_creationTime":1981,"_id":1982,"community":1983,"display":1984,"identity":1988,"providers":1991,"relations":1999,"tags":2000,"workflow":2001},1778695116697.1978,"k179rr3dr4mna3h8mah8sjs7b986n6qs",{"reviewCount":11},{"description":1985,"installMethods":1986,"name":1987,"sourceUrl":17},"Generates conference presentation slides (Beamer LaTeX PDF and editable PPTX) from a compiled paper with speaker notes and talk script. Use when preparing oral talks, spotlight presentations, or invited talks for ML and systems conferences.",{"claudeCode":15},"Presenting Conference Talks",{"basePath":1989,"githubOwner":20,"githubRepo":21,"locale":22,"slug":1990,"type":23},"20-ml-paper-writing/presenting-conference-talks","presenting-conference-talks",{"evaluate":1992,"extract":1998},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":1993,"targetMarket":35,"tier":36},[1994,1995,1969,1996,1997,1060,384],"presentation","slides","beamer","pptx",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[1060,1996,1969,1997,1994,384,1995],{"evaluatedAt":2002,"extractAt":45,"updatedAt":2002},1778697466182,{"_creationTime":2004,"_id":2005,"community":2006,"display":2007,"identity":2011,"providers":2013,"relations":2023,"tags":2024,"workflow":2025},1778695116697.198,"k17fmx7dfr9ms2cav9ryh5je4n86m9zr",{"reviewCount":11},{"description":2008,"installMethods":2009,"name":2010,"sourceUrl":17},"Comprehensive guide for writing systems papers targeting OSDI, SOSP, ASPLOS, NSDI, and EuroSys. Provides paragraph-level structural blueprints, writing patterns, venue-specific checklists, reviewer guidelines, LaTeX templates, and conference deadlines. Use this skill for all systems conference paper writing.",{"claudeCode":15},"systems-paper-writing",{"basePath":2012,"githubOwner":20,"githubRepo":21,"locale":22,"slug":2010,"type":23},"20-ml-paper-writing/systems-paper-writing",{"evaluate":2014,"extract":2022},{"promptVersionExtension":26,"promptVersionScoring":27,"score":87,"tags":2015,"targetMarket":35,"tier":36},[2010,2016,2017,2018,2019,1942,2020,2021],"osdi","sosp","asplos","nsdi","latex-templates","research-guidance",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1942,2018,2020,2019,2016,2021,2017,2010],{"evaluatedAt":2026,"extractAt":45,"updatedAt":2026},1778697480560,{"_creationTime":2028,"_id":2029,"community":2030,"display":2031,"identity":2035,"providers":2037,"relations":2046,"tags":2047,"workflow":2048},1778695116697.1982,"k17fghhjxs589kwp3n8bfe5k3986npq8",{"reviewCount":11},{"description":2032,"installMethods":2033,"name":2034,"sourceUrl":17},"Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.",{"claudeCode":15},"brainstorming-research-ideas",{"basePath":2036,"githubOwner":20,"githubRepo":21,"locale":22,"slug":2034,"type":23},"21-research-ideation/brainstorming-research-ideas",{"evaluate":2038,"extract":2045},{"promptVersionExtension":26,"promptVersionScoring":27,"score":335,"tags":2039,"targetMarket":35,"tier":36},[2040,2041,2042,2043,2044],"research-ideation","brainstorming","problem-discovery","creative-thinking","research-strategy",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[2041,2043,2042,2040,2044],{"evaluatedAt":2049,"extractAt":45,"updatedAt":2049},1778697494484,{"_creationTime":2051,"_id":2052,"community":2053,"display":2054,"identity":2058,"providers":2060,"relations":2067,"tags":2068,"workflow":2069},1778695116697.1985,"k1786s6ztsjv890tw1jh26dj2d86nhvn",{"reviewCount":11},{"description":2055,"installMethods":2056,"name":2057,"sourceUrl":17},"Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.",{"claudeCode":15},"creative-thinking-for-research",{"basePath":2059,"githubOwner":20,"githubRepo":21,"locale":22,"slug":2057,"type":23},"21-research-ideation/creative-thinking-for-research",{"evaluate":2061,"extract":2066},{"promptVersionExtension":26,"promptVersionScoring":27,"score":59,"tags":2062,"targetMarket":35,"tier":36},[2063,384,2064,2065,1276],"ideation","cognition","creativity",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[1276,2064,2065,2063,384],{"evaluatedAt":2070,"extractAt":45,"updatedAt":2070},1778697507567,{"_creationTime":2072,"_id":2073,"community":2074,"display":2075,"identity":2079,"providers":2082,"relations":2091,"tags":2092,"workflow":2093},1778695116697.1987,"k170jwbjz32kb7h63rzk4jkh7n86njdj",{"reviewCount":11},{"description":2076,"installMethods":2077,"name":2078,"sourceUrl":17},"Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.",{"claudeCode":15},"ara-compiler",{"basePath":2080,"githubOwner":20,"githubRepo":21,"locale":22,"slug":2081,"type":23},"22-agent-native-research-artifact/compiler","compiler",{"evaluate":2083,"extract":2090},{"promptVersionExtension":26,"promptVersionScoring":27,"score":112,"tags":2084,"targetMarket":35,"tier":36},[2085,2086,2087,2088,2089],"research-artifacts","knowledge-extraction","paper-ingestion","exploration-graph","provenance",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[2088,2086,2087,2089,2085],{"evaluatedAt":2094,"extractAt":45,"updatedAt":2094},1778697525867,{"_creationTime":2096,"_id":2097,"community":2098,"display":2099,"identity":2103,"providers":2106,"relations":2113,"tags":2114,"workflow":2115},1778695116697.199,"k17cex5hqwje7qgvts5evct1d186nd4m",{"reviewCount":11},{"description":2100,"installMethods":2101,"name":2102,"sourceUrl":17},"Records research provenance as a post-task epilogue, scanning conversation history at the end of a coding or research session to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara/ directory with user-vs-AI provenance tags. Use as a session epilogue — never during execution — to maintain a faithful, auditable trace of how a research project actually evolved.",{"claudeCode":15},"ARA Research Manager",{"basePath":2104,"githubOwner":20,"githubRepo":21,"locale":22,"slug":2105,"type":23},"22-agent-native-research-artifact/research-manager","research-manager",{"evaluate":2107,"extract":2112},{"promptVersionExtension":26,"promptVersionScoring":27,"score":701,"tags":2108,"targetMarket":35,"tier":36},[384,2089,2109,2110,2111],"knowledge-management","session-logging","ara",{"commitSha":38,"license":39},{"parentExtensionId":5,"repoId":41},[2111,2109,2089,384,2110],{"evaluatedAt":2116,"extractAt":45,"updatedAt":2116},1778697541177,{"_creationTime":2118,"_id":2119,"community":2120,"display":2121,"identity":2125,"providers":2128,"relations":2138,"tags":2139,"workflow":2140},1778695116697.1992,"k178yp9mdsy744tn2f06fzcy4d86my02",{"reviewCount":11},{"description":2122,"installMethods":2123,"name":2124,"sourceUrl":17},"Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.",{"claudeCode":15},"ara-rigor-reviewer",{"basePath":2126,"githubOwner":20,"githubRepo":21,"locale":22,"slug":2127,"type":23},"22-agent-native-research-artifact/rigor-reviewer","rigor-reviewer",{"evaluate":2129,"extract":2137},{"promptVersionExtension":26,"promptVersionScoring":27,"score":112,"tags":2130,"targetMarket":35,"tier":36},[2111,2131,2132,2133,2134,2135,2136],"epistemic-review","research-rigor","peer-review","scoring","audit","falsifiability",{"commitSha":38},{"parentExtensionId":5,"repoId":41},[2111,2135,2131,2136,2133,2132,2134],{"evaluatedAt":2141,"extractAt":45,"updatedAt":2141},1778697555484,{"reviewCount":11},{"description":2144,"installMethods":2145,"name":2146,"sourceUrl":17},"LLM architectures and implementations including LitGPT, Mamba, NanoGPT, RWKV, and TorchTitan. Use when implementing, training, or understanding transformer and alternative architectures.",{"claudeCode":21},"Agent-Native Research Artifact (ARA) Tooling",{"_creationTime":2148,"_id":2149,"extensionId":5,"locale":22,"result":2150,"trustSignals":2364,"workflow":2381},1778695555085.806,"kn75pe22axntshhwyvpkn4dvpn86m278",{"checks":2151,"evaluatedAt":2336,"extensionSummary":2337,"features":2338,"nonGoals":2343,"practices":2347,"prerequisites":2351,"promptVersionExtension":26,"promptVersionScoring":27,"purpose":2352,"rationale":2353,"score":59,"summary":2354,"tags":2355,"targetMarket":35,"tier":36,"useCases":2358,"workflow":2363},[2152,2157,2160,2163,2167,2170,2175,2179,2182,2185,2189,2193,2196,2200,2203,2206,2209,2212,2215,2218,2222,2226,2230,2234,2238,2241,2244,2247,2251,2254,2257,2260,2263,2266,2269,2273,2277,2281,2284,2288,2291,2294,2297,2300,2303,2306,2309,2312,2315,2318,2322,2325,2328,2332],{"category":2153,"check":2154,"severity":2155,"summary":2156},"Practical Utility","Problem relevance","pass","The description clearly articulates the problem of conducting AI research autonomously, highlighting the need for structured artifacts, provenance tracking, and epistemic review.",{"category":2153,"check":2158,"severity":2155,"summary":2159},"Unique selling proposition","The plugin offers significant value beyond default LLM behavior by providing specialized tooling for generating structured research artifacts, recording session provenance, and performing rigorous epistemic review, which are not standard LLM capabilities.",{"category":2153,"check":2161,"severity":2155,"summary":2162},"Production readiness","The bundled skills, as described in the extensive README and categorized structure, cover the full lifecycle of AI research, suggesting a production-ready state for its intended use cases.",{"category":2164,"check":2165,"severity":2155,"summary":2166},"Scope","Single responsibility principle","The plugin focuses on Agent-Native Research Artifact (ARA) tooling, encompassing compilation of research inputs, provenance recording, and epistemic review, which are coherently related aspects of managing research artifacts.",{"category":2164,"check":2168,"severity":2155,"summary":2169},"Description quality","The displayed description is concise, readable, and accurately reflects the plugin's stated purpose of compiling research inputs into structured artifacts and performing reviews.",{"category":2171,"check":2172,"severity":2173,"summary":2174},"Invocation","Scoped tools","not_applicable","This is a plugin, not a collection of individual skills with discrete tools. The evaluation of individual tools is deferred to the skills themselves.",{"category":2176,"check":2177,"severity":2173,"summary":2178},"Documentation","Configuration & parameter reference","The plugin itself does not appear to have user-configurable parameters beyond potentially those of the underlying Claude Code agent. Specific skill configurations would be documented within their respective SKILL.md files.",{"category":2164,"check":2180,"severity":2173,"summary":2181},"Tool naming","This is a plugin, not a collection of individual tools with user-facing names. Tool naming is evaluated at the skill level.",{"category":2164,"check":2183,"severity":2173,"summary":2184},"Minimal I/O surface","This is a plugin. The I/O surface of its constituent skills would be evaluated individually.",{"category":2186,"check":2187,"severity":2155,"summary":2188},"License","License usability","The extension is licensed under the MIT License, as indicated by the bundled LICENSE file and README. This is a permissive open-source license.",{"category":2190,"check":2191,"severity":2155,"summary":2192},"Maintenance","Commit recency","The last commit was on April 28, 2026, which is well within the last 3 months, indicating active maintenance.",{"category":2190,"check":2194,"severity":2155,"summary":2195},"Dependency Management","The repository includes a `package.json` and `pnpm-lock.yaml` (implied by `pnpm` installer), indicating proper dependency management.",{"category":2197,"check":2198,"severity":2173,"summary":2199},"Security","Secret Management","No evidence suggests the plugin itself handles secrets directly; such concerns would apply to individual skills if they interact with external services.",{"category":2197,"check":2201,"severity":2155,"summary":2202},"Injection","The README and skill structure imply a focus on processing research inputs as data, and the 'Agent-Native Research Artifact' concept suggests a structured approach to handling inputs, mitigating injection risks.",{"category":2197,"check":2204,"severity":2155,"summary":2205},"Transitive Supply-Chain Grenades","The skills are bundled within the repository, and installation methods focus on direct inclusion rather than runtime fetching of executable code or instructions from external URLs.",{"category":2197,"check":2207,"severity":2155,"summary":2208},"Sandbox Isolation","The plugin architecture, relying on Claude Code's sandboxing and skills being contained within the repository, suggests adherence to sandbox isolation principles.",{"category":2197,"check":2210,"severity":2155,"summary":2211},"Sandbox escape primitives","No evidence of detached-process spawns or deny-retry loops is apparent in the plugin's described functionality or structure.",{"category":2197,"check":2213,"severity":2155,"summary":2214},"Data Exfiltration","The plugin's purpose is to process research data, not exfiltrate confidential information. No outbound calls for telemetry or undisclosed purposes are indicated.",{"category":2197,"check":2216,"severity":2155,"summary":2217},"Hidden Text Tricks","The provided source code and README do not contain any evidence of hidden steering tricks, invisible characters, or obfuscated content designed to mislead the model.",{"category":2219,"check":2220,"severity":2155,"summary":2221},"Hooks","Opaque code execution","The plugin structure, with skills residing in source files and clear installation methods, does not suggest opaque code execution like base64 payloads or runtime fetched scripts.",{"category":2223,"check":2224,"severity":2155,"summary":2225},"Portability","Structural Assumption","The plugin's design focuses on processing research artifacts and leveraging agent capabilities, with no apparent assumptions about user-specific project organization outside its scope.",{"category":2227,"check":2228,"severity":2155,"summary":2229},"Trust","Issues Attention","In the last 90 days, 4 issues were opened and 8 were closed, indicating a closure rate of 66.7% (8 / (4+8)), which meets the threshold for 'pass'.",{"category":2231,"check":2232,"severity":2155,"summary":2233},"Versioning","Release Management","The repository includes a `package.json` with a version field and tags releases, and the npm package version is actively updated, ensuring clear versioning.",{"category":2235,"check":2236,"severity":2173,"summary":2237},"Code Execution","Validation","This check applies to individual tools. Validation within specific skills would need to be assessed individually.",{"category":2197,"check":2239,"severity":2155,"summary":2240},"Unguarded Destructive Operations","The plugin's focus on research artifact compilation and review implies read-heavy operations. Any destructive actions would be within individual skills and expected to be guarded.",{"category":2235,"check":2242,"severity":2173,"summary":2243},"Error Handling","Error handling is relevant to individual skills. The plugin's role as an orchestrator does not inherently require its own error handling primitives.",{"category":2235,"check":2245,"severity":2173,"summary":2246},"Logging","Logging is typically handled by individual skills or the agent environment. The plugin itself does not have a primary function that necessitates separate logging.",{"category":2248,"check":2249,"severity":2173,"summary":2250},"Compliance","GDPR","The plugin processes research artifacts, which may contain personal data, but it does not appear to submit this data to third parties without sanitization. Specific skills would need individual review.",{"category":2248,"check":2252,"severity":2155,"summary":2253},"Target market","The extension's scope is global, focusing on AI research methodologies applicable worldwide, with no explicit regional limitations mentioned.",{"category":2223,"check":2255,"severity":2155,"summary":2256},"Runtime stability","The plugin is designed to work with Claude Code and other compatible agents, with installation methods supporting various environments, suggesting good runtime stability.",{"category":2176,"check":2258,"severity":2155,"summary":2259},"README","The README is comprehensive, clearly stating the plugin's purpose, features, and installation instructions.",{"category":2164,"check":2261,"severity":2173,"summary":2262},"Tool surface size","This is a plugin. The number of tools is determined by the individual skills bundled within it.",{"category":2171,"check":2264,"severity":2173,"summary":2265},"Overlapping near-synonym tools","This is a plugin. Overlapping tools would be assessed at the skill level.",{"category":2176,"check":2267,"severity":2155,"summary":2268},"Phantom features","All advertised features related to ARA tooling are reflected in the skill categories and descriptions provided in the README.",{"category":2270,"check":2271,"severity":2155,"summary":2272},"Install","Installation instruction","The README provides clear, copy-pasteable installation instructions for both human users (npx command) and AI agents, along with alternative Claude Code marketplace installation steps.",{"category":2274,"check":2275,"severity":2173,"summary":2276},"Errors","Actionable error messages","Error message actionability would be evaluated at the individual skill level. The plugin itself does not present direct error paths to the user.",{"category":2278,"check":2279,"severity":2155,"summary":2280},"Execution","Pinned dependencies","The presence of a lockfile (implied by `pnpm` installer) and the structure suggest pinned dependencies are utilized.",{"category":2164,"check":2282,"severity":2173,"summary":2283},"Dry-run preview","Dry-run functionality would be a feature of individual skills, not the plugin itself. The focus on artifact compilation suggests a preference for non-destructive operations.",{"category":2285,"check":2286,"severity":2173,"summary":2287},"Protocol","Idempotent retry & timeouts","These aspects are relevant to individual skills and their interactions, not the plugin as a whole.",{"category":2248,"check":2289,"severity":2155,"summary":2290},"Telemetry opt-in","No mention of telemetry collection is made in the README; the focus is on local research artifact generation, implying telemetry would be opt-in if present.",{"category":2171,"check":2292,"severity":2155,"summary":2293},"Name collisions","The plugin focuses on ARA tooling and is presented as a distinct offering, with no obvious name collisions with Claude Code built-ins or other core functionalities.",{"category":2171,"check":2295,"severity":2173,"summary":2296},"Hooks-off mechanism","The provided README does not detail a specific 'hooks-off' mechanism for the plugin itself, which might be managed at the agent level or within individual skills if applicable.",{"category":2171,"check":2298,"severity":2173,"summary":2299},"Hook matcher tightness","This is a plugin, and its hook usage is not detailed in the provided README. Individual skills would be assessed for hook tightness.",{"category":2197,"check":2301,"severity":2173,"summary":2302},"Hook security","No specific hooks are detailed for the plugin, so this check is not applicable at the plugin level.",{"category":2219,"check":2304,"severity":2173,"summary":2305},"Silent prompt rewriting","The plugin does not appear to utilize UserPromptSubmit hooks for prompt rewriting.",{"category":2197,"check":2307,"severity":2173,"summary":2308},"Permission Hook","No specific PermissionRequest hooks are detailed for the plugin.",{"category":2248,"check":2310,"severity":2173,"summary":2311},"Hook privacy","No hooks are detailed for the plugin that would involve logging or telemetry data transmission.",{"category":2235,"check":2313,"severity":2173,"summary":2314},"Hook dependency","No specific hooks are detailed for the plugin.",{"category":2176,"check":2316,"severity":2155,"summary":2317},"Feature Transparency","Critical functionality related to ARA tooling is explained in the README, and the plugin's structure implies transparency.",{"category":2319,"check":2320,"severity":2155,"summary":2321},"Convention","Layout convention adherence","The repository structure, with skills organized into categorized directories and a clear README, adheres to standard conventions.",{"category":2319,"check":2323,"severity":2173,"summary":2324},"Plugin state","The plugin does not appear to manage persistent state beyond what might be handled by individual skills or the agent environment.",{"category":2197,"check":2326,"severity":2173,"summary":2327},"Keychain-stored secrets","The plugin does not appear to handle secrets directly, thus not requiring keychain storage.",{"category":2329,"check":2330,"severity":2155,"summary":2331},"Dependencies","Tagged release sourcing","The plugin itself is bundled in the repository, and its dependencies (like the npm package) are sourced from published packages with versioning.",{"category":2333,"check":2334,"severity":2155,"summary":2335},"Installation","Clean uninstall","The installation method via `npx` and Claude Code marketplace implies standard uninstall procedures that should remove bundled components without leaving background daemons.",1778695553547,"This plugin provides tooling for creating Agent-Native Research Artifacts (ARAs) from various research inputs, recording session provenance, and performing epistemic reviews. 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Use when aligning models with human preferences, training reward models, or large-scale RL training.",{"claudeCode":21},[2472,2473],{"path":2433,"priority":2431},{"path":2435,"priority":2436},{"basePath":2384,"description":2475,"displayName":627,"installMethods":2476,"rationale":2440,"selectedPaths":2477,"source":2437,"sourceLanguage":22,"type":2385},"AI safety and content moderation including Constitutional AI, LlamaGuard, NeMo Guardrails, and Prompt Guard. Use when implementing safety filters, content moderation, or prompt injection detection.",{"claudeCode":21},[2478,2479],{"path":2433,"priority":2431},{"path":2435,"priority":2436},{"basePath":2384,"description":2481,"displayName":160,"installMethods":2482,"rationale":2440,"selectedPaths":2483,"source":2437,"sourceLanguage":22,"type":2385},"Multi-GPU and multi-node training including DeepSpeed, PyTorch FSDP, Accelerate, Megatron-Core, PyTorch Lightning, and Ray Train. Use when training large models across GPUs.",{"claudeCode":21},[2484,2485],{"path":2433,"priority":2431},{"path":2435,"priority":2436},{"basePath":2384,"description":2487,"displayName":824,"installMethods":2488,"rationale":2440,"selectedPaths":2489,"source":2437,"sourceLanguage":22,"type":2385},"GPU cloud and compute orchestration including Modal, Lambda Labs, and SkyPilot. Use when deploying training jobs or managing GPU resources.",{"claudeCode":21},[2490,2491],{"path":2433,"priority":2431},{"path":2435,"priority":2436},{"basePath":2384,"description":2493,"displayName":293,"installMethods":2494,"rationale":2440,"selectedPaths":2495,"source":2437,"sourceLanguage":22,"type":2385},"Model optimization and quantization including Flash Attention, bitsandbytes, GPTQ, AWQ, GGUF, and HQQ. 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Use when extracting structured data or constraining LLM outputs.",{"claudeCode":21},[2532,2533],{"path":2433,"priority":2431},{"path":2435,"priority":2436},{"basePath":2384,"description":2535,"displayName":1542,"installMethods":2536,"rationale":2440,"selectedPaths":2537,"source":2437,"sourceLanguage":22,"type":2385},"LLM application monitoring including LangSmith and Phoenix. Use when debugging LLM apps or monitoring production systems.",{"claudeCode":21},[2538,2539],{"path":2433,"priority":2431},{"path":2435,"priority":2436},{"basePath":2384,"description":2541,"displayName":1429,"installMethods":2542,"rationale":2440,"selectedPaths":2543,"source":2437,"sourceLanguage":22,"type":2385},"Vision, audio, and multimodal models including CLIP, Whisper, LLaVA, BLIP-2, Segment Anything, Stable Diffusion, AudioCraft, Cosmos Policy, OpenPI, and OpenVLA-OFT. Use when working with images, audio, multimodal tasks, or vision-language-action robot policies.",{"claudeCode":21},[2544,2545],{"path":2433,"priority":2431},{"path":2435,"priority":2436},{"basePath":2384,"description":2547,"displayName":1812,"installMethods":2548,"rationale":2440,"selectedPaths":2549,"source":2437,"sourceLanguage":22,"type":2385},"Advanced ML techniques including MoE Training, Model Merging, Long Context, Speculative Decoding, Knowledge Distillation, and Model Pruning. Use when implementing cutting-edge optimization or architecture techniques.",{"claudeCode":21},[2550,2551],{"path":2433,"priority":2431},{"path":2435,"priority":2436},{"basePath":2384,"description":2553,"displayName":2554,"installMethods":2555,"rationale":2440,"selectedPaths":2556,"source":2437,"sourceLanguage":22,"type":2385},"Autonomous research orchestration using a two-loop architecture. 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