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Claude Skills by qhjqhj00
github.com/qhjqhj007,572 skills29 installs10,436 views
- Mc Bench EvalThis benchmark evaluates multi-context visual grounding, requiring models to localize target objects across multiple images using open-ended, context-rich text prompts. It probes cross-image reasoning, fine-grained instance localization, and the ability to correctly group and reject irrelevant instances. Use when the user wants to benchmark on MC-Bench, or asks about evaluating this task. Reports AP50.Votes: 0GitHub stars: 3
- Mcds Scheduling EvalEvaluates AI-augmented workflow scheduling in mobile edge-cloud environments by comparing execution time, energy consumption, SLA violations, and fairness against state-of-the-art baselines under dynamic workloads and host mobility. Use when the user wants to benchmark on WFCommons (Pegasus workflows: BLAST, Cycles, Montage), or asks about evaluating this task. Reports SLA Violation Rate.Votes: 0GitHub stars: 3
- Mceval EvalEvaluates the multilingual code generation, explanation, and completion capabilities of LLMs across 40 programming languages. It measures how well models can produce correct code, explain code logic, and complete code snippets in diverse syntaxes. The benchmark highlights performance disparities between closed-source and open-source models, particularly in non-Python languages. Use when the user wants to benchmark on MCEVAL, or asks about evaluating this task. Reports Pass@1 (%).Votes: 0GitHub stars: 3
- Mcformer Piv EvalEvaluates optical flow models on synthetic Particle Image Velocimetry (PIV) datasets with varying particle densities, flow velocities, and turbulence levels. Probes spatio-temporal flow modeling and robustness to sparse particle imagery and high-speed turbulent regimes. Use when the user wants to benchmark on PIV Benchmark (MHD, Isotropic, Mixing, Channel, Boundary Layer), or asks about evaluating this task. Reports NEPE.Votes: 0GitHub stars: 3
- Mcif EvalEvaluates multimodal models' ability to follow crosslingual instructions on scientific talks, testing speech recognition, translation, question answering, and summarization across short and long contexts in English, German, Italian, and Chinese. Use when the user wants to benchmark on MCIF, or asks about evaluating this task. Reports BERTScore.Votes: 0GitHub stars: 3
- Mcitlib EvalEvaluates multimodal continual instruction tuning (MCIT) methods on MLLMs, measuring their ability to learn new multimodal tasks sequentially while mitigating catastrophic forgetting and cross-modal conflict. Use when the user wants to benchmark on MLLM-DCL, UCIT, or asks about evaluating this task. Reports MAA, MFN.Votes: 0GitHub stars: 3
- Mcity Data Engine EvalEvaluates an open-vocabulary data selection pipeline for iteratively improving object detection models on rare classes. Specifically, it tests whether adding newly selected and labeled frames of vulnerable road users (pedestrians, cyclists) to a seed dataset improves detection performance on fisheye traffic camera data. Use when the user wants to benchmark on SIP VRU Detection Dataset, or asks about evaluating this task. Reports mAP@0.5.Votes: 0GitHub stars: 3
- Mcl EvalEvaluates the downstream performance and sample efficiency of the MCL pre-trained language model on general language understanding and reading comprehension benchmarks. It measures how well the model captures multi-perspective semantics and self-corrects during pre-training when fine-tuned on standard NLP tasks. Use when the user wants to benchmark on GLUE benchmark, SQuAD 2.0, or asks about evaluating this task. Reports GLUE Average.Votes: 0GitHub stars: 3
- Mcmc Eclipse Fitting Benchmark EvalThis benchmark evaluates the accuracy and reliability of Markov Chain Monte Carlo (MCMC) light curve fitting routines for detecting and measuring exoplanet secondary eclipses. It probes how well analysis pipelines recover known eclipse depths and phase centers under varying noise conditions, including synthetic white/red noise and real observational systematics. Use when the user wants to benchmark on MCMC Eclipse Benchmark Suite, or asks about evaluating this task. Reports eclipse depth.Votes: 0GitHub stars: 3
- Mcp Rest Latency EvalEvaluates the end-to-end and component-level latency of retrieving and searching AI/ML model cards across three server architectures (REST, Native MCP, Layered MCP) under local and wide-area network conditions. It measures how protocol overhead, payload size, and network distance impact system responsiveness in edge computing environments. Use when the user wants to benchmark on Patra Model Cards (Pseudo-Synthetic), or asks about evaluating this task. Reports end-to-end latency.Votes: 0GitHub stars: 3
- Mcqa Accuracy EvalEvaluates the ability of hyperbolic and Euclidean LLMs to answer multiple-choice questions across STEM, general knowledge, and commonsense reasoning domains. It probes how well the models capture semantic hierarchies and perform complex reasoning under few-shot and zero-shot settings. Use when the user wants to benchmark on MMLU, ARC-Challenging, CommonsenseQA, HellaSwag, OpenbookQA, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Mcr EvalEvaluates large language models' ability to perform compositional relation reasoning across multiple languages. It tests whether models can infer a transitive relationship (R∘S) from two given relations (R and S) using multiple-choice questions covering positional, comparative, personal, mathematical, identity, and other logical relations. Use when the user wants to benchmark on Multilingual Compositional Relation (MCR), or asks about evaluating this task. Reports Accuracy (%).Votes: 0GitHub stars: 3
- Mctaco EvalEvaluates a model's ability to reason about temporal commonsense, including event duration, ordering, typical time, frequency, and stationarity. It tests whether systems can correctly classify candidate answers as 'likely' or 'unlikely' given a context sentence and a question. Use when the user wants to benchmark on MCTACO, or asks about evaluating this task. Reports F1.Votes: 0GitHub stars: 3
- Mcts Vcb EvalEvaluates multimodal large language models on fine-grained video captioning by measuring how well generated descriptions capture verified, detailed key points from videos. It probes the model's ability to reason about and describe specific visual details like color, quantity, and position. Use when the user wants to benchmark on MCTS-VCB, or asks about evaluating this task. Reports F1.Votes: 0GitHub stars: 3
- Mcu Minecraft EvalEvaluates a model's ability to follow natural language instructions to perform multi-step, spatially grounded tasks in an open-world 3D environment (Minecraft). It specifically probes capabilities in mining, combat, crafting, and smelting under human-like visibility constraints. Use when the user wants to benchmark on MCU Benchmark, or asks about evaluating this task. Reports success rate.Votes: 0GitHub stars: 3
- Mcwq EvalEvaluates a model's ability to generalize compositionally to unseen syntactic structures and cross-lingual settings in semantic parsing. It measures how well models translate natural language questions into correct SPARQL queries across monolingual and zero-shot cross-lingual scenarios. Use when the user wants to benchmark on MCWQ, or asks about evaluating this task. Reports Exact Match (%).Votes: 0GitHub stars: 3
- Md Evalbench EvalEvaluates large language models on molecular dynamics domain knowledge, LAMMPS scripting syntax comprehension, and automatic generation of executable LAMMPS simulation scripts from natural language instructions. Use when the user wants to benchmark on MD-EvalBench, or asks about evaluating this task. Reports Exec-Success@$k$.Votes: 0GitHub stars: 3
- MdadEvaluates the reliability of micro-benchmarks in preserving pairwise model rankings compared to full benchmark performance. It quantifies the minimum accuracy gap required between two models for a small sampled subset to consistently rank them correctly. Use when the user has predictions and gold and needs to compute MDAD.Votes: 0GitHub stars: 3
- Mdec EvalEvaluates self-supervised monocular depth estimation models by measuring image-based accuracy and 3D pointcloud reconstruction quality. It probes the models' ability to generalize across diverse environments (urban, natural, agricultural, indoor) and highlights the impact of scale ambiguity and oversmoothing on relative object positioning. Use when the user wants to benchmark on SYNS-Patches, or asks about evaluating this task. Reports F-Score (Edges).Votes: 0GitHub stars: 3
- Mdec Syns Patches EvalEvaluates monocular depth estimation models across diverse real-world environments (natural, agricultural, urban, indoor) using high-quality LiDAR ground truth. Probes zero-shot generalization, boundary interpolation accuracy, and robustness to scene diversity and image artifacts. Use when the user wants to benchmark on SYNS-Patches, or asks about evaluating this task. Reports F-Score.Votes: 0GitHub stars: 3
- Mdec Synspatches EvalEvaluates monocular depth estimation models on their ability to predict accurate depth maps from single images. It specifically probes zero-shot generalization across diverse natural and indoor scenes using LiDAR-ground truth. Use when the user wants to benchmark on SYNS-Patches, or asks about evaluating this task. Reports F-Score.Votes: 0GitHub stars: 3
- Mdgender EvalEvaluates models' ability to classify pragmatic gender bias in text across three conversational dimensions: ABOUT (topic-related), AS (speaker role/attitude), and TO (addressee-directed). It probes fine-grained detection of gendered language and contextual bias cues. Use when the user wants to benchmark on MDGENDER, or asks about evaluating this task. Reports percentage accuracy.Votes: 0GitHub stars: 3
- Mdia EvalThis benchmark evaluates a model's ability to generate coherent, contextually appropriate, and lexically diverse dialogue responses across 46 languages. It specifically probes cross-lingual transfer capabilities and measures the performance gap between high-resource and low-resource languages in open-domain conversation. Use when the user wants to benchmark on MDIA, or asks about evaluating this task. Reports sacreBLEU.Votes: 0GitHub stars: 3
- Mdlbench EvalEvaluates the inference performance and overhead of six major mobile deep learning libraries across diverse model architectures, tasks, and mobile hardware configurations. It measures how software-level optimizations and library choices impact on-device latency compared to hardware capabilities and algorithmic optimizations like quantization. Use when the user wants to benchmark on MDLBench, or asks about evaluating this task. Reports inference time.Votes: 0GitHub stars: 3
- Mdocekal Multi Label Precision Recall Accuracy FscoreCompute mdocekal/multi_label_precision_recall_accuracy_fscore via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of mdocekal/multi_label_precision_recall_accuracy_fscore.Votes: 0GitHub stars: 3
- Mdocekal Precision Recall Fscore AccuracyCompute mdocekal/precision_recall_fscore_accuracy via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of mdocekal/precision_recall_fscore_accuracy.Votes: 0GitHub stars: 3
- Mdpbench EvalMDPBench evaluates the capability of document parsing models to accurately extract text, formulas, tables, and layout structures from multilingual document images under real-world conditions. It specifically probes robustness to photographic degradation, non-Latin scripts, right-to-left reading orders, and cross-lingual generalization without prior language or image-type knowledge. Use when the user wants to benchmark on MDPBench, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Mdpe EvalEvaluates multimodal deception detection across video, audio, and text modalities, while also probing how individual differences—specifically personality traits and emotional expressivity—influence deceptive behavior and detection accuracy. Use when the user wants to benchmark on MDPE, or asks about evaluating this task. Reports Accuracy.Votes: 0GitHub stars: 3
- Mds Icu EvalThis benchmark evaluates a multimodal deep learning model's ability to predict 33 distinct ICU clinical outcomes by fusing 10-second 12-lead ECG waveforms with structured tabular clinical data. It probes the model's discriminative capacity and probabilistic calibration across mortality, medication administration, clinical deterioration, and organ dysfunction tasks. Use when the user wants to benchmark on MDS-ICU, or asks about evaluating this task. Reports macro-averaged AUROC.Votes: 0GitHub stars: 3
- Mean Absolute ErrorCompute the mean_absolute_error metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute mean_absolute_error, or asks how to score with mean_absolute_error.Votes: 0GitHub stars: 3
- Mean Absolute Percentage ErrorCompute the mean_absolute_percentage_error metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute mean_absolute_percentage_error, or asks how to score with mean_absolute_percentage_error.Votes: 0GitHub stars: 3
- Mean Gamma DevianceCompute the mean_gamma_deviance metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute mean_gamma_deviance, or asks how to score with mean_gamma_deviance.Votes: 0GitHub stars: 3
- Mean Pinball LossCompute the mean_pinball_loss metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute mean_pinball_loss, or asks how to score with mean_pinball_loss.Votes: 0GitHub stars: 3
- Mean Poisson DevianceCompute the mean_poisson_deviance metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute mean_poisson_deviance, or asks how to score with mean_poisson_deviance.Votes: 0GitHub stars: 3
- Mean Squared ErrorCompute the mean_squared_error metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute mean_squared_error, or asks how to score with mean_squared_error.Votes: 0GitHub stars: 3
- Mean Squared Log ErrorCompute the mean_squared_log_error metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute mean_squared_log_error, or asks how to score with mean_squared_log_error.Votes: 0GitHub stars: 3
- Mean Tweedie DevianceCompute the mean_tweedie_deviance metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute mean_tweedie_deviance, or asks how to score with mean_tweedie_deviance.Votes: 0GitHub stars: 3
- MeanabsoluteerrorCompute the MeanAbsoluteError metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute MeanAbsoluteError, or asks how to score with MeanAbsoluteError.Votes: 0GitHub stars: 3
- MeanabsolutepercentageerrorCompute the MeanAbsolutePercentageError metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute MeanAbsolutePercentageError, or asks how to score with MeanAbsolutePercentageError.Votes: 0GitHub stars: 3
- MeanmetricCompute the MeanMetric metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute MeanMetric, or asks how to score with MeanMetric.Votes: 0GitHub stars: 3
- MeansquarederrorCompute the MeanSquaredError metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute MeanSquaredError, or asks how to score with MeanSquaredError.Votes: 0GitHub stars: 3
- MeansquaredlogerrorCompute the MeanSquaredLogError metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute MeanSquaredLogError, or asks how to score with MeanSquaredLogError.Votes: 0GitHub stars: 3
- Measurebench EvalEvaluates vision-language models on fine-grained visual measurement reading, specifically testing their ability to accurately localize pointers and ticks on instrument scales, map visual cues to numerical values, and recognize measurement units from real-world and synthetic images. Use when the user wants to benchmark on MeasureBench, or asks about evaluating this task. Reports Overall accuracy.Votes: 0GitHub stars: 3
- Mebench EvalEvaluates vision-language models' ability to ground objects and exhibit mutual exclusivity bias when mapping novel pseudo-labels to unknown items in cluttered scenes. It also measures spatial reasoning capabilities and the model's ability to resolve ambiguity among multiple novel objects. Use when the user wants to benchmark on MEBench, or asks about evaluating this task. Reports ME score.Votes: 0GitHub stars: 3
- Mec Offloading EvalEvaluates the stability, convergence, and resource efficiency of online computation offloading algorithms in dynamic mobile-edge networks under stochastic task arrivals and time-varying channel conditions. It probes whether an algorithm can maintain queue stability and power constraints while maximizing computation throughput. Use when the user wants to benchmark on Simulated MEC Offloading Environment, or asks about evaluating this task. Reports weighted sum computation rate.Votes: 0GitHub stars: 3
- Mecat EvalThis benchmark evaluates fine-grained audio understanding by testing models on generating detailed, multi-perspective captions and answering probing questions across diverse acoustic domains. It specifically probes a model's ability to distinguish between speech, music, and sound events, reason about acoustic scenes, and assess technical audio quality without relying on generic descriptions. Use when the user wants to benchmark on MECAT, or asks about evaluating this task. Reports DATE.Votes: 0GitHub stars: 3
- Meccano EvalEvaluates multimodal egocentric video understanding in an industrial-like setting. It probes action recognition, active object detection, human-object interaction, and future action anticipation using synchronized RGB, depth, and gaze signals. Use when the user wants to benchmark on MECCANO, or asks about evaluating this task. Reports Top-1 Accuracy.Votes: 0GitHub stars: 3
- Med Anomaly Detection EvalThis evaluation probes a model's ability to detect pathological anomalies in medical images using a one-class learning setting. It measures how well the model distinguishes between normal and abnormal samples across diverse imaging modalities and anatomical regions without seeing abnormal examples during training. Use when the user wants to benchmark on RSNA Pneumonia, VinDr-CXR, Brain Tumor, LAG, ISIC 2018, Camelyon16, BraTS2021, or asks about evaluating this task. Reports AUC-ROC.Votes: 0GitHub stars: 3
- Med Critics EvalThis benchmark evaluates a model's ability to verify factual accuracy in long-form medical texts by recursively decomposing claims into a verification tree. It probes fine-grained fact-checking across six medical domains, requiring the model to distinguish between factual and deliberately falsified claims while accounting for contextual dependencies. Use when the user wants to benchmark on Med-Critics, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Med Mim EvalEvaluates medical vision-language models on multi-image reasoning tasks, including temporal understanding, cross-modal comparison, multi-view diagnosis, and co-reference resolution across longitudinal and multi-modality medical imaging data. Use when the user wants to benchmark on Med-MIM Benchmark, or asks about evaluating this task. Reports closed-type accuracy.Votes: 0GitHub stars: 3