Claude Skills by qhjqhj00

Evaluates the quality and diversity of multi-objective optimization algorithms for water distribution system design by comparing generated Pareto fronts against established benchmark fronts. It measures coverage of known solutions, discovery of novel non-dominated designs, and computational efficiency. Use when the user wants to benchmark on HAN, NYT, BLA, and GOY networks, or asks about evaluating this task. Reports N_A^u, N_B^u, N_A^a, N_B^a, N_c, N_{FE}.

Evaluates the visual mathematical reasoning capabilities of Large Multimodal Models (LMMs). It probes their ability to decompose composite problems, apply hierarchical knowledge concepts, and reason through multi-step visual math tasks without relying on rote memorization. Use when the user wants to benchmark on We-Math testmini, or asks about evaluating this task. Reports accuracy.

Evaluates inertial-based activity recognition models trained on weakly-supervised labels generated via vision foundation model clustering, benchmarked against fully-supervised and few-shot baselines. Use when the user wants to benchmark on WEAR, Wetlab, ActionSense, or asks about evaluating this task. Reports Acc.

This evaluation probes a model's ability to perform named entity recognition under weak supervision, where training labels are noisy and derived from multiple distant supervision sources. It measures how well the model can denoise these labels and generalize entity patterns across general, biomedical, and review domains. Use when the user wants to benchmark on CoNLL 2003, LaptopReview, NCBI-Disease, BC5CDR, or asks about evaluating this task. Reports entity-level F1.

This evaluation protocol assesses a multimodal fusion model's ability to classify emotional and stress states from wearable physiological signals. It probes the model's robustness across different data collection settings, subject variability, and varying label granularities (binary vs. multi-class affect). Use when the user wants to benchmark on WESAD, SWELL-KW, CASE, or asks about evaluating this task. Reports accuracy, macro-F1.

Evaluates speech-language models on egocentric, multi-channel wearable audio tasks, probing their ability to handle noisy real-world acoustic conditions, reject side-talk, execute tool calls, answer questions with or without context, and translate speech in conversational settings. Use when the user wants to benchmark on WearVox, or asks about evaluating this task. Reports Turn-basedMicro-avg.

Evaluates the capability of data-driven time-series forecasting models to predict global meteorological variables over short to long horizons, and assesses their robustness in forecasting extreme weather events compared to numerical weather prediction baselines. Use when the user wants to benchmark on WEATHER-5K, or asks about evaluating this task. Reports MAE.

Evaluates object detection robustness to adverse weather by measuring performance degradation when models trained on clear-weather datasets are tested on weather-corrupted images. It specifically quantifies dataset bias by comparing baseline in-distribution performance against out-of-distribution performance on the DAWN dataset. Use when the user wants to benchmark on DAWN, Pascal VOC 2012, Microsoft COCO 2017, or asks about evaluating this task. Reports mAP.

Evaluates the ability of deep learning models to forecast multivariate and univariate meteorological factors (temperature, visibility, humidity) using historical time-series and spatio-temporal data from ground weather stations. Use when the user wants to benchmark on Weather2K, or asks about evaluating this task. Reports MAE.

Evaluates the accuracy and reliability of probabilistic medium-range weather forecasting models against operational ensemble baselines. It probes how well deep learning methods capture uncertainty, calibration, and sharpness for key atmospheric variables. Use when the user wants to benchmark on WeatherBench Probability, or asks about evaluating this task. Reports CRPS.

Evaluates the capability of generative weather forecasting models to predict global atmospheric and surface conditions from mid-range to sub-seasonal horizons (up to 30 days). It probes deterministic accuracy and probabilistic ensemble calibration against established meteorological baselines and climatology. Use when the user wants to benchmark on WeatherBench-2, or asks about evaluating this task. Reports Latitude-weighted RMSE.

Evaluates the capability of diffusion models to perform controllable weather editing in intrinsic space. It probes whether the model can preserve geometric and material consistency while synthesizing realistic weather effects like rain, snow, and fog. Use when the user wants to benchmark on WeatherSynthetic, ACDC, TransWeather, Waymo, or asks about evaluating this task. Reports PickScore.

Evaluates multimodal reasoning capabilities in the meteorological domain, specifically testing a model's ability to interpret weather maps and answer domain-specific multiple-choice questions. It also measures cross-task generalization and the logical consistency of the model's reasoning chains versus final answers. Use when the user wants to benchmark on WeatherQA, ScienceQA, or asks about evaluating this task. Reports Multiple-choice accuracy.

Evaluates multi-turn, context-aware image comprehension and generation in an interleaved setting. It probes a model's ability to maintain visual consistency, follow iterative editing instructions, and integrate historical context across multiple turns. Use when the user wants to benchmark on WEAVEBench, or asks about evaluating this task. Reports WEAVEBench.

Evaluates long-horizon web navigation and information-seeking capabilities. Probes the agent's ability to formulate search queries, browse multiple web pages, synthesize information from diverse sources, and answer complex multi-step questions. Use when the user wants to benchmark on BrowseComp-en, BrowseComp-zh, GAIA, WebWalkerQA, FRAMES, XBench-DeepSearch, HLE, or asks about evaluating this task. Reports Avg@4 Accuracy.

Evaluates the planning and execution quality of LLM-based web agents by comparing their action trajectories against human-demonstrated gold standards. It measures recovery from deviations, action repetitiveness, step fulfillment, partial task completion, and alignment between planned and executed actions. Use when the user wants to benchmark on WebArena Human Trajectory Dataset, or asks about evaluating this task. Reports Recovery Rate.

This benchmark evaluates multimodal models' ability to translate webpage design screenshots into functional HTML/CSS code. It probes visual fidelity, structural hierarchy recall, and the capacity to generate long, complex, real-world front-end code from visual inputs. Use when the user wants to benchmark on WebCode2M, or asks about evaluating this task. Reports TreeBLEU.

Evaluates LLMs' ability to generate complete web applications from real-world user requirements. It probes multi-modal understanding, code generation quality, and strict adherence to ground-truth checklists across functionality, visual design, and content dimensions. Use when the user wants to benchmark on WebCoderBench, or asks about evaluating this task. Reports checklist-based evaluation.

This benchmark evaluates code language models on multimodal web coding tasks, including generating, editing, and repairing web applications from text, image, or video inputs. It probes functional correctness, UI consistency, and interactive behavior using an automated agent-based pipeline and checklist-guided LLM judges. Use when the user wants to benchmark on WebCompass, or asks about evaluating this task. Reports Overall Score.

Evaluates multilingual dense retrieval models on natural Q&A pairs by measuring ranking quality against gold answers. It tests the model's ability to retrieve relevant FAQ documents across multiple languages and assesses zero-shot generalization to other Wikipedia-based benchmarks. Use when the user wants to benchmark on WebFAQ, Mr. TyDi, MIRACL (Hard Negatives), or asks about evaluating this task. Reports NDCG@10.

Evaluates fine-grained image classification performance on large-scale webly supervised datasets characterized by label noise and extreme class imbalance. It probes a model's ability to learn robust visual features and correct noisy labels through mutual peer learning or standard fine-tuning. Use when the user wants to benchmark on WebFG-496, WebiNat-5089, or asks about evaluating this task. Reports classification accuracy (%).

Evaluates browser agents' ability to complete interactive web tasks across varying difficulty levels and domains. It probes navigation, visual understanding, multi-step reasoning, and interaction capabilities in realistic, self-contained web environments. Use when the user wants to benchmark on WebForge-Bench, or asks about evaluating this task. Reports accuracy (%).

Evaluates a model's ability to generate functional and visually accurate website codebases from natural language instructions. It measures both functional correctness via automated GUI-agent testing and visual fidelity via VLM-based appearance scoring. Use when the user wants to benchmark on WebGen-Bench, or asks about evaluating this task. Reports Accuracy.

This benchmark evaluates visual web agents on their ability to navigate complex, multi-step tasks across diverse websites and domains. It specifically probes long-horizon interaction, information extraction, and out-of-distribution generalization to unseen websites. Use when the user wants to benchmark on WebGym, or asks about evaluating this task. Reports accuracy.

Evaluates a model's ability to extract main content from HTML web pages by classifying semantic blocks and generating clean text or Markdown. It probes robustness across varying difficulty levels and rich content types such as tables, code, and equations. Use when the user wants to benchmark on WebMainBench, WCEB, or asks about evaluating this task. Reports ROUGE-N F1.

Evaluates the robustness and accuracy of deep visual tracking algorithms on large-scale, real-world UAV video sequences. It probes how well trackers handle diverse environmental conditions, motion dynamics, and target appearance changes without parameter tuning. Use when the user wants to benchmark on WebUAV-3M, or asks about evaluating this task. Reports AUC.

Evaluates the robustness and accuracy of deep object trackers in challenging underwater environments. It probes cross-domain adaptation from open-air to underwater domains, as well as within-domain fine-tuning capabilities, while also assessing performance under varying frame rates and complex visual conditions like occlusion and low visibility. Use when the user wants to benchmark on WebUOT-1M, or asks about evaluating this task. Reports AUC.

Measures an autonomous web agent's end-to-end task completion capability across dynamic, real-world websites. It evaluates multi-step navigation, form filling, and robustness in open-web environments. Use when the user wants to benchmark on WebVoyager, or asks about evaluating this task. Reports Success Rate.

Evaluates LLM-based agents' ability to systematically navigate multi-layered, real-world websites to extract buried information. It probes long-range reasoning, memory management, and step-by-step click-based navigation under strict action limits. Use when the user wants to benchmark on WebWalkerQA, or asks about evaluating this task. Reports accuracy.

Evaluates a model's ability to jointly perform fine-grained weed species segmentation, continuous plant height regression, and discrete temporal growth stage classification from single RGB images. Use when the user wants to benchmark on WeedSense, or asks about evaluating this task. Reports mIoU, MAE, Accuracy.

Compute the WeightedMeanAbsolutePercentageError metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute WeightedMeanAbsolutePercentageError, or asks how to score with WeightedMeanAbsolutePercentageError.

Compute the weightedtau metric — provided by scipy.stats. Use when the user has predictions and ground-truth and needs to compute weightedtau, or asks how to score with weightedtau.

Evaluates Mandarin speech recognition systems across diverse, real-world domains (internet, meetings) and mixed Mandarin-English content. It benchmarks the robustness of ASR models against noisy, production-level audio and varying data scales. Use when the user wants to benchmark on WenetSpeech, or asks about evaluating this task. Reports MER%.

Evaluates speech processing capabilities for the Chinese Wu dialect, including automatic speech recognition (ASR), automatic speech translation (AST), speaker attribute prediction (gender, age), emotion recognition, text-to-speech (TTS), and instruction-following TTS. Use when the user wants to benchmark on WenetSpeech-Wu-Bench, or asks about evaluating this task. Reports CER (%).

Evaluates neural language models' ability to understand Words of Estimative Probability (WEP) by testing their capacity to distinguish valid from invalid probabilistic verbalizations and perform logical consistency checks in probabilistic reasoning. Use when the user wants to benchmark on WEP Reasoning 1 hop, WEP Reasoning 2 hops, WEP-UNLI, or asks about evaluating this task. Reports accuracy.

Evaluates single-channel speech separation and enhancement (denoising/dereverberation) capabilities under realistic noisy and reverberant conditions using synthetically generated reverberant mixtures. Use when the user wants to benchmark on WHAMR!, or asks about evaluating this task. Reports SI-SDR.

Evaluates the zero-shot generalization capability of a speech recognition model across diverse English and multilingual domains. It measures robustness to out-of-distribution audio, varying noise levels, and translation tasks without any dataset-specific fine-tuning. Use when the user wants to benchmark on LibriSpeech, Common Voice, Fleurs, CoVoST2, Multilingual LibriSpeech (MLS), VoxPopuli, or asks about evaluating this task. Reports WER.

Evaluates a hybrid recommender model's ability to balance memorization of frequent user-item interactions with generalization to unseen combinations for ranking candidate apps. The protocol measures predictive accuracy on a static holdout set and business impact via live A/B testing on user acquisition rates. Use when the user wants to benchmark on Google Play App Store (internal), or asks about evaluating this task. Reports Online Acquisition Gain.

Evaluates a model's ability to perform broad information seeking by decomposing complex queries into parallel subtasks and producing structured tabular outputs. It also measures robustness on standard single-hop and multi-hop open-domain QA tasks. Use when the user wants to benchmark on WideSearch, or asks about evaluating this task. Reports item F1 score.

Evaluates face detection algorithms on real-world images with extreme variations in scale, pose, occlusion, and event context. It probes the ability of detectors to handle small faces, heavy occlusion, and atypical poses under standard bounding box matching criteria. Use when the user wants to benchmark on WIDER FACE, or asks about evaluating this task. Reports Average Precision (AP).

This benchmark evaluates a model's ability to generate natural language descriptions for individual mobile UI elements using multimodal inputs. It probes the capability to fuse visual appearance and structural hierarchy data to produce accurate, context-aware captions for accessibility and UI understanding tasks. Use when the user wants to benchmark on Widget Captioning Dataset, or asks about evaluating this task. Reports CIDEr.

Evaluates end-to-end speech-to-speech (S2S) language models on real-world conversational tasks, probing their ability to handle diverse query types, paralinguistic features (prosody, disfluencies), and robustness to background noise. Use when the user wants to benchmark on WildSpeech-Bench, or asks about evaluating this task. Reports Score.

Evaluates knowledge graph models on node and entity classification under edge incompleteness, and link prediction with varying feature/class configurations. It probes how well models leverage relational structure, node features, and joint link prediction capacity to handle missing edges and predict missing links. Use when the user wants to benchmark on WikiAlumni, or asks about evaluating this task. Reports validation accuracy.

Measures how well automated RAG scoring metrics align with human preferences in pairwise comparison tasks. It probes the ability of reference-free faithfulness, answer relevance, and context relevance estimators to replicate human judgment on answer and context quality. Use when the user wants to benchmark on WikiEval, or asks about evaluating this task. Reports accuracy.

Evaluates multi-domain aspect-based summarization, requiring models to first discover relevant aspects (Wikipedia section titles) from cited references and then generate domain-specific summaries. It probes content selection, cross-document pronoun resolution, and temporal ordering in multi-source generation. Use when the user wants to benchmark on WikiAsp, or asks about evaluating this task. Reports R-2.

Evaluates abstractive multi-document summarization models on their ability to generate coherent, content-adequate summaries across three domains (Company, Film, Animal). The protocol measures lexical and sentence-level overlap between generated summaries and reference summaries using ROUGE metrics, while also contextualizing scores against a baseline overlap between input documents and summaries. Use when the user wants to benchmark on WIKICATSUM, or asks about evaluating this task. Reports R...

Evaluates the factual accuracy, conversational quality, and latency of knowledge-grounded chatbots in simulated multi-turn dialogues across head, tail, and recent knowledge domains. Use when the user wants to benchmark on Simulated Dialogues (WikiChat), or asks about evaluating this task. Reports factual_accuracy.

Evaluates a model's ability to disambiguate named entities in text by matching them to correct Wikidata entries using graph-based representations. It probes how well different neural architectures leverage graph triplet information versus full graph topology for entity resolution. Use when the user wants to benchmark on Wikidata-Disamb, or asks about evaluating this task. Reports F1.

Evaluates text summarization systems on procedural, step-by-step articles written by non-journalists. It probes the model's ability to handle long sequences, non-inverted-pyramid structures, and high-abstraction content compared to standard news datasets. Use when the user wants to benchmark on WikiHow, or asks about evaluating this task. Reports ROUGE-L.

This benchmark evaluates cross-lingual abstractive summarization, specifically the ability of models to generate coherent English summaries from articles written in other languages. It probes how well systems can handle translation and summarization jointly, either through direct cross-lingual fine-tuning or two-step pipeline approaches. Use when the user wants to benchmark on WikiLingua, or asks about evaluating this task. Reports ROUGE-L F1.