Claude Skills by qhjqhj00

Evaluates the model's ability to reconstruct high-quality dynamic HDR radiance fields and synthesize novel views and time steps from alternating-exposure monocular videos. It probes exposure-invariant geometric reconstruction, temporal coherence, and radiometric accuracy under extreme exposure variations. Use when the user wants to benchmark on HDR-GoPro, or asks about evaluating this task. Reports PSNR.

Compute He-Xingwei/sari_metric via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of He-Xingwei/sari_metric.

This benchmark evaluates a model's ability to perform multi-label classification on radiological text reports, specifically identifying the presence of 13 clinical findings in head CT scans. It probes the model's capacity to handle significant class imbalance and generalize from general-domain pretraining to specialized medical NLP tasks. Use when the user wants to benchmark on Head CT Reports, or asks about evaluating this task. Reports Sample-weighted F1-score.

Evaluates complex reasoning and domain-specific knowledge integration in healthcare by testing models on multi-choice questions derived from real Spanish medical specialization exams. It probes the ability to handle long, context-rich questions requiring cross-domain inference and precise medical knowledge. Use when the user wants to benchmark on HEAD-QA, or asks about evaluating this task. Reports accuracy.

This benchmark evaluates large language models on complex medical reasoning using real Spanish medical licensing exam questions. It probes domain-specific knowledge retention, cross-lingual generalization, and the effectiveness of various inference strategies like prompting, retrieval-augmented generation, and log-probability selection. Use when the user wants to benchmark on HEAD-QA v2, or asks about evaluating this task. Reports accuracy.

Evaluates a model's ability to detect implicit causal relationships between two news headlines without relying on explicit causal linking words. It probes commonsense reasoning and world knowledge to distinguish between causal, refutational, same-event, and unrelated headline pairs. Use when the user wants to benchmark on HeadlineCause, or asks about evaluating this task. Reports causality ROC AUC.

Evaluates the ability of unsupervised contrastive learning models to extract robust, degradation-sensitive health indicators from sensor data. It probes how well the learned features correlate with actual wear or track operational degradation over time, while remaining invariant to noise and operating condition shifts. Use when the user wants to benchmark on Milling Machine Wear Dataset, Railway Wheel Dataset, or asks about evaluating this task. Reports correlation value to the wear.

This benchmark evaluates large language models' tendency to over-refuse benign health-related queries and their ability to provide safe, helpful completions in medical contexts. It specifically probes the trade-off between safety alignment and utility by measuring refusal rates on carefully curated boundary prompts across varying difficulty levels. Use when the user wants to benchmark on Health-ORSC-Bench, or asks about evaluating this task. Reports Over-Refusal Rate (ORR).

Evaluates LLM responses to realistic clinical queries using a fine-grained, rubric-based scoring system. It measures medical accuracy, instruction following, completeness, context awareness, and safety by assigning positive or negative points to specific behavioral criteria, then normalizing the total to a [0, 1] scale. Use when the user wants to benchmark on HealthBench, or asks about evaluating this task. Reports HealthBench Score.

Evaluates a medical vision-language model's ability to perform visual question answering (comprehension) and medical image synthesis (generation) on heterogeneous datasets. It probes the model's capacity to unify multiple downstream tasks using parameter-efficient fine-tuning without task interference. Use when the user wants to benchmark on VL-Health, VQA-RAD, SLAKE, PathVQA, IXI, SynthRAD2023, or asks about evaluating this task. Reports accuracy.

Evaluates small language models on health prediction tasks using wearable sensor data. It probes the models' ability to infer physiological and mental health states (e.g., stress, fatigue, depression) from temporal behavioral and physiological features under zero-shot, few-shot, and instruction-tuned settings. Use when the user wants to benchmark on PMData, GLOBEM, AW-FB, or asks about evaluating this task. Reports accuracy.

Evaluates the zero-shot generalization and transferability of pre-trained audio representations across 19 diverse downstream tasks spanning speech, environmental sounds, and music. The benchmark requires models to perform without fine-tuning, emphasizing robustness and cross-domain adaptability. Use when the user wants to benchmark on FSD50K, ESC-50, GTZAN, Vocal Imitations, LibriCount, CREMA-D, VoxLingua107, Speech Commands, DCASE 2016 Task 2, Gunshot Triangulation, Beijing Opera, Mridingham...

This evaluation probes how socially manipulated content encountered during an AI agent's background 'heartbeat' execution influences its downstream behavior. It measures both immediate same-session carry-over and cross-session long-term memory pollution under varying social credibility cues, agent personas, and realistic content dilution. Use when the user wants to benchmark on MissClaw (Custom Testbed), or asks about evaluating this task. Reports ASR.

Evaluates multimodal ECG understanding and clinical reasoning across closed/open question answering, report generation, and signal prediction. It probes a model's ability to align temporal signal patterns with diagnostic language and generate clinically faithful outputs. Use when the user wants to benchmark on Heartcare-BenchS, Heartcare-BenchI, or asks about evaluating this task. Reports accuracy.

This benchmark evaluates a model's ability to convert unvocalized Hebrew text into fully-specified IPA transcriptions, including accurate stress placement and shva realization. It also measures downstream text-to-speech quality and inference latency to assess real-time applicability. Use when the user wants to benchmark on ILSpeech, SASPEECH, or asks about evaluating this task. Reports WER.

Evaluates the quality of a diacritic-free Hebrew text-to-speech system by measuring content accuracy, speech naturalness, and speaker similarity against baseline models and different text tokenization strategies. Use when the user wants to benchmark on Hebrew TTS Test Set, or asks about evaluating this task. Reports Content Preservation.

This benchmark evaluates whether large language models and automated essay scoring systems can correctly distinguish between harmful essays (containing toxic or discriminatory content) and argumentative essays (which present controversial but non-harmful viewpoints). It also measures safety alignment by tracking refusal rates and the tendency to redirect harmful prompts into ethical, argumentative responses. Use when the user wants to benchmark on HED benchmark, or asks about evaluating this ...

Evaluates language model generalization and overfitting by measuring cross-entropy loss on a held-out test set. It probes how well the model retains predictive performance when trained on repeated or constrained data subsets. Use when the user has predictions and gold and needs to compute held-out test loss.

Evaluates deep learning architectures for pilot-based channel estimation in 5G-NR OFDM systems. It probes the model's ability to reconstruct full Channel State Information (CSI) from sparse pilot measurements across varying SNR levels, Doppler shifts, and 3GPP TDL propagation profiles. Use when the user wants to benchmark on 5G Deep Learning Data Synthesis (MATLAB), or asks about evaluating this task. Reports accuracy.

Compute helena-balabin/youden_index via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of helena-balabin/youden_index.

Evaluates an end-to-end Large Audio Language Model's capabilities in audio understanding (ASR, QA, translation, reasoning, emotion/event recognition, instruction following) and text-to-speech synthesis (naturalness, intelligibility, speaker similarity). Use when the user wants to benchmark on AIShell, WeNet, LibriSpeech, AlpacaEval, LLaMA Questions, Web Questions, Synthetic multilingual (Claude-generated), MMAU-Mini, EmoBox, AudioSet, CochlScene, Seed-TTS-Eval (Chinese), or asks about evaluat...

Evaluates long-horizon vision-language-action (VLA) manipulation capabilities, specifically testing a model's ability to maintain cross-phase context, predict action failures before execution, and recover from perturbations via rollback or replanning. Use when the user wants to benchmark on LIBERO-LONG, CALVIN ABC→D, LIBERO-Recovery, or asks about evaluating this task. Reports TSR.

This evaluation probes the robustness of open benchmarks against test-set memorization and data leakage. It measures whether small language models can artificially inflate leaderboard scores by overfitting directly to public test sets, revealing flaws in current benchmarking practices. Use when the user wants to benchmark on HELM-lite, or asks about evaluating this task. Reports Exact Match.

Evaluates LLM-powered recommender systems across five human-centered dimensions: intent alignment, explanation quality, interaction naturalness, trust & transparency, and fairness & diversity. It combines expert human ratings with automated accuracy and fairness metrics across multiple domains and interaction scenarios. Use when the user wants to benchmark on MovieLens-1M, Amazon Books, Yelp, or asks about evaluating this task. Reports NDCG@10.

Evaluates a model's ability to retain, process, and reason over extended contexts (8K to 128K tokens) across retrieval-augmented generation (RAG) and long-range question answering (LongQA) tasks. It probes robustness to noise, multi-hop reasoning, and memorization in long-context settings. Use when the user wants to benchmark on HELMET, or asks about evaluating this task. Reports accuracy.

Evaluates the accuracy and computational efficiency of numerical PDE solvers (BEM vs. PINNs) for 2D acoustic wave scattering. Probes generalization capability beyond the training domain and physical fidelity in far-field regions. Use when the user wants to benchmark on 2D Helmholtz Scattering Benchmark, or asks about evaluating this task. Reports relative error.

Evaluates the exploration efficiency and adaptability of a robot planner in unknown environments. It measures how well the system covers free space while minimizing travel distance and adapting to natural language preferences. Use when the user wants to benchmark on Simulated dungeon environments, Indoor office environment (130m x 100m), or asks about evaluating this task. Reports Travel Distance.

Evaluates a cross-lingual program repair framework on low-resource programming languages (Ruby and Rust) by measuring functional correctness against unit tests and syntactic validity via compilation or parsing rates. Use when the user wants to benchmark on xCodeEval (Compact Set for Ruby and Rust), Defects4Ruby, or asks about evaluating this task. Reports Pass@k.

This dataset evaluates language model responses across five key dimensions: helpfulness, correctness, coherence, complexity, and verbosity. It probes an LLM's ability to follow instructions, maintain factual accuracy, produce logically consistent text, and adapt to varying levels of detail and difficulty. Use when the user wants to benchmark on HelpSteer2, or asks about evaluating this task. Reports helpfulness.

Evaluates pathology foundation models on fine-grained tissue classification of liver cancer patches and whole-slide tumor/non-tumor segmentation. It measures how well models can quantify tissue composition (e.g., fibrosis, necrosis, inflammation) within clinically defined regions to support reproducible digital pathology analysis. Use when the user wants to benchmark on HepatoBench, or asks about evaluating this task. Reports F1-score, Dice coefficient.

Evaluates models on detecting volcanic ground deformation using multi-modal InSAR data. It probes the ability to classify deformation presence and segment deformation areas from spatiotemporal interferometric time-series, while handling atmospheric noise and class imbalance. Use when the user wants to benchmark on Hephaestus Minicubes, or asks about evaluating this task. Reports F1-score, IoU.

This benchmark evaluates extractive reading comprehension in Hebrew, a morphologically rich language. It probes a model's ability to accurately identify answer spans within a given context passage, handling challenges like affixation, spelling variations, and domain-specific vocabulary across news and encyclopedic text. Use when the user wants to benchmark on HeQ, or asks about evaluating this task. Reports TLNLS.

Evaluates a training-free KV cache management framework for real-time streaming and offline video understanding. It probes the model's ability to maintain temporal coherence and answer questions accurately under strict token/memory budgets, while measuring inference efficiency. Use when the user wants to benchmark on StreamingBench, OVO-Bench, RVS (Ego & Movie), MVBench, VideoMME, Egoschema, or asks about evaluating this task. Reports accuracy.

Evaluates long-horizon planning and structured reasoning in a grid-based RPG virtual environment. Agents must generate multi-step plans involving resource gathering, crafting, and combat, requiring integration of numerical calculations with action sequencing. Use when the user wants to benchmark on HeroBench, or asks about evaluating this task. Reports Success %.

Evaluates cross-modal alignment between histology images and spatial gene expression profiles, and tests downstream capabilities in gene mutation classification and direct gene expression prediction from whole-slide images. Use when the user wants to benchmark on 5K, Multi, ImmOnc, Colon, Breast, Lung, or asks about evaluating this task. Reports Recall@5.

Evaluates the ability of histopathology foundation models to predict gene expression levels from H&E-stained whole-slide image patches. It probes the alignment between morphological features and transcriptomic profiles across diverse cancer types and organs. Use when the user wants to benchmark on HEST-Benchmark, or asks about evaluating this task. Reports Pearson correlation.

Evaluates the long-term phenotypic and genotypic dynamics of a heterogeneous cellular automaton with age constraints and local evolution, testing its ability to sustain open-ended innovation without stagnation. Use when the user wants to benchmark on Heterogeneous Life-Like CA Simulation, or asks about evaluating this task. Reports quantitative metrics.

Evaluates an LLM agent's ability to execute profitable high-frequency trading decisions under strict latency constraints, balancing response speed with financial accuracy. The benchmark measures how well the model recognizes market patterns and executes trades within a fixed time window without degrading portfolio performance. Use when the user wants to benchmark on HFTBench, or asks about evaluating this task. Reports Daily Yield (%).

Evaluates a model's ability to rank pairs of dialogue responses according to human preferences for helpfulness and harmlessness. It probes the model's alignment capabilities by measuring how well it captures human judgments on multi-turn conversations. Use when the user wants to benchmark on HH (Helpful and Harmless) Preference, or asks about evaluating this task. Reports accuracy.

This benchmark evaluates language model alignment across four dimensions: helpfulness, honesty, harmlessness, and other. It measures how well a model's outputs adhere to human values and safety guidelines through pairwise comparison. Use when the user wants to benchmark on HHH_Alignment, or asks about evaluating this task. Reports Net Win Rate.

Evaluates the generalization and classification capabilities of foundational vision transformers on histopathology data across patch-level tissue classification, slide-level cancer subtyping, and nuclei segmentation tasks. Use when the user wants to benchmark on CRC-100K, MHIST, PCam, MSI-CRC, MSI-STAD, TIL-DET, BRCA, NSCLC, RCC, PanNuke, or asks about evaluating this task. Reports top-1 accuracy, AUC.

Evaluates human-object interaction recognition by decomposing activities into atomic body part states and reasoning hierarchically. Probes the model's ability to handle long-tail data and few-shot learning scenarios through compositional part-state representations. Use when the user wants to benchmark on HICO, or asks about evaluating this task. Reports mAP.

Evaluates a model's ability to perform hierarchical multi-label image classification on remote sensing scenes. It probes how well the model captures label dependencies and hierarchy structures while predicting multiple overlapping categories per image. Use when the user wants to benchmark on UCM, AID, DFC-15, MLRSNet, or asks about evaluating this task. Reports AUPRC.

Evaluates the ability of spatiotemporal graph neural networks to perform multistep-ahead forecasting on correlated time series while simultaneously learning hierarchical cluster structures end-to-end. It probes the model's capacity to leverage relational inductive biases and self-supervised aggregation for improved prediction accuracy. Use when the user wants to benchmark on METR-LA, PEMS-BAY, AQI, CER-E, or asks about evaluating this task. Reports MAE.

Evaluates a model's ability to perform fine-grained, taxonomy-aware visual recognition by predicting hierarchical biological labels (order, family, genus, species) from images. It specifically probes whether the model maintains logical consistency across taxonomic levels while accurately identifying leaf-level species, including generalization to unseen/novel categories. Use when the user wants to benchmark on iNaturalist-2021, TerraIncognita, or asks about evaluating this task. Reports Hiera...

Evaluates the accuracy and stability of transformer head pruning methods (specifically HIES vs. baselines) across NLP, vision, and multimodal benchmarks at fixed sparsity ratios (10%, 30%, 50%). Use when the user wants to benchmark on GLUE (SST-2, CoLA, MRPC, QQP, STS-B, QNLI, MNLI, RTE), HellaSwag, Winogrande, ARC-e / ARC-c, OBQA, ImageNet1k, CIFAR-100, Food-101, Fashion MNIST, VizWiz-VQA, MM-Vet, or asks about evaluating this task. Reports Accuracy.

Evaluates the inference throughput speedup of hierarchical speculative decoding against vanilla auto-regressive decoding and other acceleration baselines. It probes the method's ability to accelerate token generation across dialogue, summarization, code generation, and mathematical reasoning tasks without relying on auxiliary draft models. Use when the user wants to benchmark on ShareGPT, CNN/DM, XSum, HumanEval, GSM8K, or asks about evaluating this task. Reports Speedup (vs. Vanilla).

Evaluates models on hierarchical key performance indicator (KPI) extraction from SEC earnings filings, testing their ability to classify paragraph-level labels, perform token-level sequence labeling, and extract structured financial entities (tags, dates, currency, values) at varying granularities. Use when the user wants to benchmark on HiFi-KPI, HiFi-KPI Lite, or asks about evaluating this task. Reports aggregated macro F1.

Evaluates a graph neural network's ability to predict particle velocities in particulate suspensions by learning many-body hydrodynamic interactions. It probes transferability across particle counts, external forcing types, and domain boundaries, alongside computational efficiency. Use when the user wants to benchmark on HIGNN-Training-Data, or asks about evaluating this task. Reports loss.

Evaluates the adversarial robustness of tree ensemble models (RF, XGB, LGBM, EBM) on enterprise network intrusion detection using the more recent HIKARI dataset. It measures how well models maintain detection performance on benign and malicious traffic when subjected to constrained adversarial perturbations of time-series traffic features. Use when the user wants to benchmark on HIKARI, or asks about evaluating this task. Reports F1S.