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Claude Skills by qhjqhj00
github.com/qhjqhj007,572 skills29 installs10,436 views
- Omniview Zero Shot EvalThis evaluation probes the viewpoint invariance and robustness of vision-language pre-training models. It measures how well models maintain classification accuracy on clean data, common out-of-distribution shifts, and specifically challenging viewpoint-variant images compared to standard baselines. Use when the user wants to benchmark on ImageNet-1K, ImageNet-V+, ImageNet-V, OOD-CV, MIRO, or asks about evaluating this task. Reports Top-1 accuracy.Votes: 0GitHub stars: 3
- Omniworld Game EvalEvaluates 3D geometric foundation models on monocular and video depth estimation, and tests camera-controlled video generation models on their ability to follow camera trajectories while maintaining video quality across diverse, dynamic environments. Use when the user wants to benchmark on OmniWorld-Game, or asks about evaluating this task. Reports FVD.Votes: 0GitHub stars: 3
- Oms Keyword Gen EvalEvaluates an LLM agent's ability to generate high-quality, multi-objective ad keywords from product descriptions. It probes lexical and semantic alignment with product info, real-world campaign performance (clicks, conversions, cost), and the model's capacity for self-reflective refinement over multiple generation rounds. Use when the user wants to benchmark on OKG Benchmark Dataset, or asks about evaluating this task. Reports ROUGE-1.Votes: 0GitHub stars: 3
- On Device Llm Energy EvalEvaluates the trade-offs between inference speed, energy consumption, latency, and generation quality of various LLM architectures and quantization schemes running on mobile hardware. It specifically measures how model size, sparsity (MoE), and compression formats impact physical battery drain and user-perceived responsiveness. Use when the user wants to benchmark on Summarization Task (On-Device Profiling), or asks about evaluating this task. Reports Energy per Token (Joules).Votes: 0GitHub stars: 3
- One Shot Doc Ie EvalThis benchmark evaluates a system's ability to perform one-shot information extraction from document images. It measures how accurately the model can extract specific entity values (e.g., dates, amounts, names) from unseen test documents after being shown only a single training example, with optional supplementary documents for refinement. Use when the user wants to benchmark on Doctor's Bills, Patent (Ghega), or asks about evaluating this task. Reports extraction accuracy.Votes: 0GitHub stars: 3
- One Shot Il Robot Manipulation EvalThis evaluation probes a robot's ability to generalize a single kinesthetic demonstration to novel object poses and orientations using unseen object pose estimation for trajectory transfer. It measures how robustly different pose estimation methods enable successful completion of everyday manipulation tasks in real-world settings. Use when the user wants to benchmark on Custom 10-task real-world manipulation set, or asks about evaluating this task. Reports success rate (%).Votes: 0GitHub stars: 3
- Onechart Se EvalEvaluates a model's ability to extract structured information from chart images, including textual OCR accuracy and precise numerical value parsing. It tests the model's capacity to convert visual chart elements into a standardized Python-dict representation, handling both annotated and unannotated charts across multiple languages and rendering styles. Use when the user wants to benchmark on ChartQA-SE, PlotQA-SE, ChartX-SE, ChartY-en, ChartY-zh, or asks about evaluating this task. Reports SC...Votes: 0GitHub stars: 3
- Oneig Bench EvalMeasures broader image generation capabilities across five axes: alignment, text rendering, reasoning, style, and diversity. Use when the user wants to benchmark on OneIG-Bench, or asks about evaluating this task. Reports Overall.Votes: 0GitHub stars: 3
- Onerec Think EvalEvaluates a generative recommendation model's ability to perform sequential next-item prediction and in-text reasoning for user preference alignment. It probes the model's capacity to generate interpretable reasoning paths alongside item recommendations, and measures ranking accuracy on standard recommendation benchmarks. Use when the user wants to benchmark on Amazon Beauty, Amazon Toys, Amazon Sports, or asks about evaluating this task. Reports R@K / NDCG@K.Votes: 0GitHub stars: 3
- Onethinker EvalEvaluates a unified multimodal reasoning model's ability to perform visual understanding tasks across both static images and videos. It probes capabilities in question answering, captioning, spatial and temporal grounding, object tracking, and segmentation. Use when the user wants to benchmark on MMMU, MathVista, MathVerse, MMBench, MMStar, ScienceQA, AI2D, MMT-Bench, VideoMMMU, MMVU, VideoMME, VideoHolmes, LongVideoBench, LongVideo-Reason, VideoMathQA, MMSci-Caption, MMT-Caption, VideoMMLU-C...Votes: 0GitHub stars: 3
- Online Ctr EvalThis protocol evaluates an online feature interaction detection method integrated into click-through rate (CTR) prediction models. It measures predictive accuracy on streaming ad click data using chronological splits to simulate real-time recommendation scenarios. Use when the user wants to benchmark on Avazu, Criteo, Taobao, or asks about evaluating this task. Reports AUC, logloss.Votes: 0GitHub stars: 3
- Online Matching Fairness EvalEvaluates online matching algorithms for maximizing individual and group fairness among offline agents, as well as weighted matching performance, under dynamic arrival constraints. Use when the user wants to benchmark on Chicago ride-hailing dataset, Network Data Repository (socfb-Caltech36, socfb-Reed98, econ-because, econ-mbeaflw), Synthetic bipartite graphs, or asks about evaluating this task. Reports CR1.Votes: 0GitHub stars: 3
- Online Video Understanding EvalEvaluates a model's ability to perform real-time, streaming video understanding and long-horizon reasoning. It probes low-latency perception, temporal alignment, evidence-aligned response timing, and causal reasoning across short to hour-long video sequences. Use when the user wants to benchmark on StreamingBench, OVOBench, RTVBench, OVBench, VideoMME, MLVU, LongVideoBench, LVBench, or asks about evaluating this task. Reports QA accuracy.Votes: 0GitHub stars: 3
- Only Connect Wall EvalEvaluates creative problem-solving and associative reasoning by testing whether models can correctly group words and identify connections, specifically probing susceptibility to cognitive fixation effects when misleading red herring clues are present. Use when the user wants to benchmark on Only Connect Wall (OCW), or asks about evaluating this task. Reports grouping_evaluation.Votes: 0GitHub stars: 3
- Onlysports Benchmark EvalEvaluates a sports-domain language model's generation capability on sports-specific tasks and its zero-shot commonsense reasoning performance on general benchmarks. Use when the user wants to benchmark on OnlySports Benchmark, HellaSwag, PIQA, ARC-challenge, ARC-easy, or asks about evaluating this task. Reports OS-acc.Votes: 0GitHub stars: 3
- Ontology Alignment EvalThis benchmark evaluates the accuracy of mapping OpenAlex paper topics to terms across 13 scientific ontologies. It probes a method's ability to perform semantic and lexical alignment between informal topic labels and formal domain-specific vocabularies. Use when the user wants to benchmark on Ontology Alignment Gold Standard, or asks about evaluating this task. Reports F1.Votes: 0GitHub stars: 3
- Ontology Subsumption Inference EvalEvaluates large language models' ability to perform ontology subsumption inference by framing it as a binary natural language inference task. The model must predict whether a hypothesis concept subsumes a premise concept based on verbalized OWL axioms. Use when the user wants to benchmark on biMNLI, Schema.org (Atomic SI), DOID (Atomic SI), FoodOn (Atomic SI), GO (Atomic SI), FoodOn (Complex SI), GO (Complex SI), or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Ontourl EvalThis benchmark evaluates large language models' ability to understand, reason over, and learn from symbolic ontologies. It probes hierarchical classification, logical inference, and structured knowledge construction across multiple domains and concept depths. Use when the user wants to benchmark on OntoURL, or asks about evaluating this task. Reports Accuracy.Votes: 0GitHub stars: 3
- Ood Detection 3d Medical EvalEvaluates the ability of out-of-distribution (OOD) detection methods to identify distribution shifts in 3D medical image segmentation. It measures how well models distinguish in-distribution scans from clinically anomalous or shifted OOD scans, highlighting the limitations of deep learning-based detectors compared to simpler intensity-based baselines. Use when the user wants to benchmark on 3D CT datasets, 3D MRI datasets, or asks about evaluating this task. Reports FPR at 95% TPR (FPR95).Votes: 0GitHub stars: 3
- Ood Detection Cxr EvalEvaluates a model's ability to distinguish in-distribution chest X-rays from out-of-distribution medical images (e.g., knee, hand, or general radiographs) while maintaining classification accuracy on chest diseases. Use when the user wants to benchmark on CXR14, IRMA, MURA, Bone Age, or asks about evaluating this task. Reports AUC.Votes: 0GitHub stars: 3
- Ood Detection EvalEvaluates a model's ability to distinguish between in-distribution (ID) samples and out-of-distribution (OOD) samples. It measures how well the model's decision boundaries separate known classes from unknown data distributions using various synthetic and natural OOD benchmarks. Use when the user wants to benchmark on SVHN, CIFAR-10, CIFAR-100, TinyImageNet, TinyImageNet-crop, TinyImageNet-resize, LSUN-crop, LSUN-resize, iSUN, or asks about evaluating this task. Reports TNR@TPR95.Votes: 0GitHub stars: 3
- Ood Detection Histopathology EvalEvaluates the ability of uncertainty estimation methods to distinguish in-distribution clinical histopathology samples from out-of-distribution samples and to provide well-calibrated confidence scores for selective prediction. It probes how different methods maintain predictive accuracy and calibration under varying degrees of data shift. Use when the user wants to benchmark on CIFAR-10, Hospital 4, Hospital 5, Cancer type, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Ood Mol Opt EvalOut-of-domain molecular property prediction and Bayesian optimization for molecular design. Tests transferability of learned representations to novel tasks. Use when the user wants to benchmark on Out-of-domain molecular design tasks, or asks about evaluating this task. Reports Top performing molecule property.Votes: 0GitHub stars: 3
- Ood Pointcloud Seg EvalEvaluates a model's ability to detect out-of-distribution (OOD) inputs in 3D point cloud semantic segmentation. It probes domain shift robustness (indoor vs outdoor scenes) and sensor failure simulation (missing color channels) by measuring uncertainty-based OOD scores against in-distribution data. Use when the user wants to benchmark on Semantic3D, S3DIS, Semantic3D (no color), or asks about evaluating this task. Reports AUROC.Votes: 0GitHub stars: 3
- Oodis EvalEvaluates a model's ability to detect and segment anomalous objects (out-of-distribution instances) in real-world driving scenes. It measures instance-level segmentation and object detection performance on rare, unpredictable obstacles that are not part of the standard in-distribution classes. Use when the user wants to benchmark on OoDIS Benchmark, or asks about evaluating this task. Reports AP.Votes: 0GitHub stars: 3
- Opcode Malware Classification EvalThis evaluation probes the ability of machine learning and deep learning models to classify malware into specific families based on their assembly-level instruction sequences (opcodes). It compares traditional feature-engineering approaches using 1-gram and 2-gram n-grams against an end-to-end 1D CNN that processes raw opcode sequences. Use when the user wants to benchmark on OpCode Malware Dataset, or asks about evaluating this task. Reports Accuracy.Votes: 0GitHub stars: 3
- Open Asr Leaderboard EvalEvaluates multilingual speech recognition models on transcription accuracy and inference efficiency across short-form, long-form, and diverse language settings. It standardizes text normalization and reports both word error rate and inverse real-time factor to enable fair accuracy–efficiency comparisons. Use when the user wants to benchmark on Short-form English, Multilingual, Long-form, or asks about evaluating this task. Reports WER.Votes: 0GitHub stars: 3
- Open Domain Continual Learning EvalEvaluates open-domain continual learning (ODCL) in vision-language models by measuring how well a model adapts to a stream of new image classification domains while preserving previously learned knowledge and zero-shot capabilities on unseen domains. Use when the user wants to benchmark on Aircraft, Caltech101, CIFAR100, DTD, EuroSAT, Flowers, Food, MNIST, OxfordPet, StanfordCars, SUN397, or asks about evaluating this task. Reports Avg.Votes: 0GitHub stars: 3
- Open Domain Qa EvalEvaluates open-domain question answering systems on their ability to retrieve relevant context and generate accurate answers across straightforward (OLTP) and synthesis-heavy (OLAP) queries. It measures factual correctness against reference answers and assesses multi-dimensional answer quality (comprehensiveness, diversity, empowerment) for open-ended questions. Use when the user wants to benchmark on HotPotQA, MSMarco, Microsoft Earnings Call Transcripts, Kevin Scott Podcast Transcripts, or ...Votes: 0GitHub stars: 3
- Open Finllm Leaderboard EvalEvaluates the capability of financial LLMs and agents across seven core financial task categories, including information extraction, sentiment analysis, question answering, text generation, risk management, forecasting, and decision-making. The benchmark aggregates 42 existing financial datasets to provide a standardized comparison of model performance and compliance readiness. Use when the user wants to benchmark on Open FinLLM Leaderboard (42 financial datasets), or asks about evaluating th...Votes: 0GitHub stars: 3
- Open Llm Leaderboard EvalThis benchmark evaluates large language models on their ability to answer open-style questions across diverse knowledge and reasoning domains. It specifically probes whether models rely on selection bias and random guessing in multiple-choice formats versus genuinely understanding and generating correct open-ended responses. Use when the user wants to benchmark on MMLU, ARC, MedMCQA, PIQA, CommonsenseQA, OpenBookQA, RACE, WinoGrande, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Open Sdi EvalEvaluates a model's ability to localize manipulated regions in images generated by diffusion models, measuring both pixel-level segmentation precision and image-level binary detection capability across multiple unseen generators. Use when the user wants to benchmark on OpenSDI, or asks about evaluating this task. Reports pixel-level F1.Votes: 0GitHub stars: 3
- Open Set Malware EvalEvaluates a model's ability to classify malware into known families while simultaneously detecting instances belonging to novel, unseen families in an open-set scenario. Use when the user wants to benchmark on BIG 2015, Mailing, MAL-100, or asks about evaluating this task. Reports classification accuracy ($C_{Acc}$).Votes: 0GitHub stars: 3
- Open Set Recognition EvalThis evaluation probes a model's ability to correctly classify instances into known classes while accurately detecting and rejecting instances from unknown, unseen classes. It measures both outlier detection capability via ROC analysis and multi-class recognition performance including an explicit unknown category. Use when the user wants to benchmark on MNIST, MS Challenge, Android Genome, or asks about evaluating this task. Reports AUC.Votes: 0GitHub stars: 3
- Open Wikitable EvalEvaluates open-domain table retrieval and end-to-end question answering over complex table reasoning tasks. It probes a model's ability to retrieve relevant table segments from a corpus and then answer questions using either direct reading or SQL generation. Use when the user wants to benchmark on Open-WikiTable, or asks about evaluating this task. Reports Top-k table retrieval accuracy.Votes: 0GitHub stars: 3
- Openad EvalEvaluates open-world 3D object detection in autonomous driving by measuring detection accuracy and localization quality across seen and unseen object categories and domains. Use when the user wants to benchmark on OpenAD, or asks about evaluating this task. Reports AP.Votes: 0GitHub stars: 3
- Openapi Completion EvalEvaluates an LLM's ability to perform code infilling for OpenAPI specifications by predicting masked sections of API definitions. It probes semantic understanding of API structure, syntax correctness, and the model's robustness to varying context sizes and prompt formats. Use when the user wants to benchmark on masked OpenAPI definitions, or asks about evaluating this task. Reports correctness.Votes: 0GitHub stars: 3
- Openassistant Human Eval EvalEvaluates the quality of aligned language models on multi-turn conversational prompts by measuring how often their generated responses are preferred over supervised fine-tuning targets by human annotators. Use when the user wants to benchmark on OpenAssistant test set, or asks about evaluating this task. Reports winrate.Votes: 0GitHub stars: 3
- Opencodeinstruct EvalEvaluates the code generation, algorithmic problem-solving, and complex function-calling capabilities of instruction-tuned LLMs across multiple standardized coding benchmarks. Use when the user wants to benchmark on HumanEval, MBPP, LiveCodeBench, BigIntCodeBench-Instruct, or asks about evaluating this task. Reports pass@1.Votes: 0GitHub stars: 3
- Opencompass Downstream EvalEvaluates language model performance across five diverse downstream benchmarks spanning commonsense reasoning, science QA, and complex reasoning. It specifically probes how dynamic expert routing mechanisms adapt to input difficulty compared to fixed Top-K routing. Use when the user wants to benchmark on PIQA, Hellaswag, ARC-e, CommonsenseQA, BBH, or asks about evaluating this task. Reports accuracy (score).Votes: 0GitHub stars: 3
- Openevents V1 EvalProbes a model's ability to perform multimodal event grounding by integrating visual content with textual context. It evaluates three interdependent capabilities: generating event-aware image captions, retrieving relevant news articles from images, and retrieving images from narrative event descriptions. Use when the user wants to benchmark on OpenEvents V1, or asks about evaluating this task. Reports CLIPScore, mAP.Votes: 0GitHub stars: 3
- Openexempt EvalProbes legal reasoning capabilities in U.S. bankruptcy exemption law, specifically testing multi-step inference, robustness to distractors and obfuscation, and scalability across asset counts and temporal complexity. Use when the user wants to benchmark on OpenExempt, or asks about evaluating this task. Reports macro-averaged F1.Votes: 0GitHub stars: 3
- Openfake EvalBinary classification capability for detecting AI-generated images versus real photographs. It probes a model's ability to generalize across diverse generative models (diffusion, transformer-based) and real-world social media distributions. Use when the user wants to benchmark on OpenFake, or asks about evaluating this task. Reports F1 Score.Votes: 0GitHub stars: 3
- Openfgl EvalEvaluates federated graph learning (FGL) algorithms across effectiveness, robustness, and efficiency dimensions. It probes how well distributed GNN training handles data heterogeneity, local noise/sparsity, low client participation, and privacy constraints across diverse graph topologies and simulation scenarios. Use when the user wants to benchmark on MUTAG, BZR, COX2, ENZYMES, DD, PROTEINS, COLLAB, BINARY, MULTI, Cora, CiteSeer, PubMed, Photo, Computers, Products, Chameleon, Actor, Ratings,...Votes: 0GitHub stars: 3
- Openforesight EvalEvaluates language models' ability to make probabilistic forecasts on open-ended, future-uncertain questions derived from global news. It probes both prediction accuracy and calibration, testing whether models can generalize forecasting skills across diverse sources and time horizons without leaking future information. Use when the user wants to benchmark on OpenForesight Test Set, FutureX, SimpleQA, MMLU-Pro, GPQA-Diamond, or asks about evaluating this task. Reports Brier Score.Votes: 0GitHub stars: 3
- Openfungraph EvalEvaluates a model's ability to predict open-vocabulary functional 3D scene graphs from posed RGB-D images. It specifically probes the detection of objects and interactive elements, as well as the inference of their functional relationships (e.g., switch controls light) in real-world indoor spaces. Use when the user wants to benchmark on SceneFun3D, FunGraph3D, or asks about evaluating this task. Reports Recall@K.Votes: 0GitHub stars: 3
- Openfwi EvalEvaluates the predictive accuracy and computational efficiency of a pruned deep learning model for seismic full waveform inversion. It measures how well the model reconstructs subsurface velocity maps and quantifies inference latency and resource consumption on edge hardware. Use when the user wants to benchmark on OpenFWI, or asks about evaluating this task. Reports MAE.Votes: 0GitHub stars: 3
- Openfwi Fwi EvalEvaluates deep learning models for seismic full-waveform inversion (FWI) by predicting subsurface velocity models from seismic wavefield data. It probes the model's ability to generalize across varying geological complexities and out-of-distribution scenarios using parameter-efficient fine-tuning. Use when the user wants to benchmark on OpenFWI, or asks about evaluating this task. Reports SSIM.Votes: 0GitHub stars: 3
- Openie EvalThis evaluation probes a model's ability to perform Open Information Extraction (OpenIE), which involves identifying and extracting relational triples (subject, predicate, object) from unstructured text without relying on a predefined ontology or schema. It measures how well systems capture complete, correct, and minimal information spans across diverse domains like news and encyclopedias. Use when the user wants to benchmark on OIE2016, CaRB, or asks about evaluating this task. Reports F1.Votes: 0GitHub stars: 3
- Openie Systems EvalEvaluates the performance and efficiency of state-of-the-art neural OpenIE models and training datasets across multiple standard benchmarks. It probes how model properties like N-ary relation support and inferred relation extraction capability align with benchmark characteristics and downstream task requirements. Use when the user wants to benchmark on OIE2016, WiRE57, ReOIE2016, CaRB, LSOIE, or asks about evaluating this task. Reports F1 score.Votes: 0GitHub stars: 3