Claude Skills by qhjqhj00

Measures end-to-end robotic manipulation performance and grasp robustness by clearing a bin of soft objects using a learned policy. It evaluates the ability to predict optimal grasping poses from RGB-D inputs and execute them across different hardware platforms. Use when the user wants to benchmark on Soft toy bin-clearing set, or asks about evaluating this task. Reports r_success.

Evaluates the runtime performance and energy efficiency of different programming language implementations. It specifically compares Lua interpreters, LuaJIT JIT compilers, and C on computationally intensive benchmark programs from the Computer Language Benchmarks Game. Use when the user wants to benchmark on CLBG (Computer Language Benchmarks Game), or asks about evaluating this task. Reports Energy Consumption.

Evaluates LLMs' ability to extract structured Causal Loop Diagrams (CLDs) from natural language system dynamics descriptions. It probes structured output generation, schema conformance, and iterative model updating under varying context lengths and prompt strategies. Use when the user wants to benchmark on CLD Leaderboard, or asks about evaluating this task. Reports exact_structured_match.

This evaluation probes a model's ability to remove background noise from speech signals directly in the waveform domain. It measures perceptual quality, speech intelligibility, and computational efficiency using standardized objective metrics and crowdsourced subjective listening tests. Use when the user wants to benchmark on DNS dataset, Valentini dataset, Internal dataset, or asks about evaluating this task. Reports PESQ-MOS (SIG/BAK/OVRL).

Evaluates embodied cleaning agents in physics-accurate indoor simulations, probing their ability to perform sweeping and grasping tasks across diverse cluttered scenes. It measures task completion, spatial coverage efficiency, motion quality, and collision safety under strict time limits. Use when the user wants to benchmark on CleanUpBench, or asks about evaluating this task. Reports TCR.

Evaluates a model's ability to continuously learn from non-stationary data streams without catastrophic forgetting, balancing stability and plasticity in regression tasks. Use when the user wants to benchmark on Artificial periodic dataset, Wind power generation dataset, or asks about evaluating this task. Reports prediction error.

Evaluates perception models' ability to estimate 6 DoF poses and complete depth maps for transparent and translucent objects. It specifically probes robustness to challenging real-world conditions such as heavy occlusion, cluttered backgrounds, varying lighting, and objects filled with liquid. Use when the user wants to benchmark on ClearPose, or asks about evaluating this task. Reports 6 DoF poses.

Evaluates Chinese large language models across six key dimensions: accuracy, robustness, fairness, calibration, bias, and diversity. The platform uses standardized prompts and dynamic test set sampling to mitigate train-test contamination while comparing open-source and limited-access models. Use when the user wants to benchmark on CLEVA benchmark suite, or asks about evaluating this task. Reports Accuracy.

Evaluates end-to-end formally verified code generation by requiring models to produce both a logically equivalent formal specification and a provably correct implementation in Lean 4. It probes the model's ability to reason about non-computable specifications, synthesize machine-checkable proofs, and ensure semantic correctness beyond syntactic compilation. Use when the user wants to benchmark on CLEVER, or asks about evaluating this task. Reports pass@600-seconds.

This benchmark evaluates a model's ability to comprehend referring expressions in synthetic visual scenes. It probes compositional visual reasoning by measuring how well models localize objects based on text descriptions that vary in attribute complexity, spatial relationships, and reasoning topology. Use when the user wants to benchmark on CLEVR-Ref+, or asks about evaluating this task. Reports IoU.

Evaluates 3D visual question answering capabilities on point cloud scenes, probing spatial reasoning, object recognition, and scene graph understanding without relying on common-sense spatial priors. Use when the user wants to benchmark on CLEVR3D, or asks about evaluating this task. Reports Accuracy.

CliBench evaluates large language models on real-world clinical decision-making tasks, including diagnosis, procedure recommendation, lab test ordering, and medication prescribing. It probes the models' ability to process complex patient records, generate structured medical codes, and maintain coherence across multi-step clinical workflows in a zero-shot setting. Use when the user wants to benchmark on CliBench (MIMIC-IV derived), or asks about evaluating this task. Reports micro F1.

Evaluates language models' proficiency in Korean cultural knowledge (e.g., history, law, society, economy) and linguistic competence (e.g., grammar, functional usage) using multiple-choice questions sourced from official Korean exams and textbooks. Use when the user wants to benchmark on CLIcK, or asks about evaluating this task. Reports accuracy.

This evaluation probes a recommender system's ability to mitigate clickbait by measuring performance exclusively on user interactions that result in positive post-click feedback (likes), rather than raw click-through rates. Use when the user wants to benchmark on Unspecified in provided section, or asks about evaluating this task. Reports post-click satisfaction (likes).

Evaluates the ability to detect fraudulent ad clicks (clickspam) by analyzing temporal reuse patterns in organic clickstreams. It tests both passive traffic analysis and active bait-click injection strategies to distinguish legitimate user behavior from automated or malware-driven fraud. Use when the user wants to benchmark on University Network Click Traffic Dataset, or asks about evaluating this task. Reports FPR.

This benchmark evaluates zero-shot detection of AI-generated images across general and domain-specific settings. It probes a model's ability to distinguish real from synthetic images without task-specific fine-tuning, measuring robustness to domain shifts (e.g., artistic styles, damaged cars, invoices) and resistance to 'flipped classification' where detectors misrank generated content as real. Use when the user wants to benchmark on General Image Benchmark (LAION + MS-COCO), ImaginET, CarDD,...

Evaluates a model's ability to accumulate knowledge across a sequence of NLP tasks (continual learning) while maintaining performance on previously seen tasks and generalizing to new few-shot tasks. Use when the user wants to benchmark on CLIF-26, CLIF-55, or asks about evaluating this task. Reports Final Accuracy.

This benchmark evaluates LLM-based agents on autonomous, open-ended climate science problem-solving. It probes the model's ability to perform data-driven modeling, apply physics-aware constraints, and generate scientifically rigorous analysis reports without human intervention. Use when the user wants to benchmark on ClimaBench, or asks about evaluating this task. Reports Overall.

Evaluates LLMs on climate science question-answering across multiple formats (multiple-choice, freeform, cloze) and complexity levels (base, reasoning, hypothetical). It probes factual recall, scientific reasoning, and the impact of adaptation techniques like RAG, few-shot prompting, and fine-tuning. Use when the user wants to benchmark on ClimaQA, or asks about evaluating this task. Reports accuracy.

Evaluates the ability of deep learning models to perform super-resolution (downscaling) on meteorological surface variables across different spatial resolutions and climate datasets. It probes spatial reconstruction accuracy, structural fidelity, and zero-shot generalization capability in Earth system modeling. Use when the user wants to benchmark on ERA5, BARRA-SY, or asks about evaluating this task. Reports RMSE.

This benchmark evaluates open-source large language models on their ability to understand, classify, and reason about climate-related discourse. It probes capabilities across text classification, stance detection, claim verification, misinformation detection, and named entity recognition using real-world news, corporate reports, social media, and scientific abstracts. Use when the user wants to benchmark on Guardian Climate News Corpus, Climate-Stance, Climate-FEVER, Climate-Change NER, Net-Z...

This evaluation probes a model's ability to verify scientific claims in a binary classification setting, specifically testing out-of-domain generalization. It measures performance on Supported vs. Refuted labels, emphasizing robustness when applied to climate-related claims outside the training distribution. Use when the user wants to benchmark on CLIMATE-FEVER, or asks about evaluating this task. Reports Balanced Accuracy.

Evaluates Retrieval-Augmented Generation (RAG) systems on climate-finance question answering using expert-validated Q&A pairs from corporate sustainability reports. It measures answer correctness across different retrieval strategies and LLMs, while also quantifying the environmental footprint (GHG emissions) of each configuration. Use when the user wants to benchmark on Climate Finance Bench, or asks about evaluating this task. Reports accuracy.

Evaluates the out-of-distribution robustness of climate emulators under temporal extrapolation and cross-scenario forcing shifts. It probes whether models trained on historical climate data can accurately generalize to novel future regimes and extreme emission pathways without seeing them during training. Use when the user wants to benchmark on ClimateSet / CMIP6 GCM outputs, or asks about evaluating this task. Reports LL-RMSE.

This evaluation probes pixel-level weather pattern segmentation (atmospheric rivers and tropical cyclones) from multi-channel climate data. It measures both segmentation accuracy and exascale training throughput/scaling efficiency across different network architectures and hardware configurations. Use when the user wants to benchmark on Climate weather pattern dataset, or asks about evaluating this task. Reports IoU.

Evaluates the out-of-distribution robustness of machine learning climate emulation models under time-domain shifts (training on historical data, testing on recent years) and source-domain shifts (training on one SSP scenario, testing on others). This protocol assesses how well models generalize to changing climate dynamics and divergent emission pathways. It specifically measures performance degradation when distribution shifts occur in temporal or scenario domains. Use when the user wants to...

Evaluates AI models on multi-modal climate data across three tasks: tensor time-series forecasting, extreme weather anomaly detection, and crop classification from satellite imagery. It probes spatial-temporal alignment, generative data synthesis, and robustness to highly imbalanced rare events. Use when the user wants to benchmark on ClimateBench-M, or asks about evaluating this task. Reports Mean Absolute Error (MAE).

Evaluates large language models' ability to infer correlation directions between event pairs and identify causal chain structures (membership and node position) from climate science text, including implicit and nested causal relations. Use when the user wants to benchmark on ClimateCause, or asks about evaluating this task. Reports F1.

Tests the ability to retrieve relevant scholarly abstracts for climate change claims from social media and classify the relationship between claims and abstracts as supporting, refuting, or inconclusive. Use when the user wants to benchmark on ClimateCheck, or asks about evaluating this task. Reports Recall@10.

Evaluates large language models on climate-specific knowledge, reasoning, and fact-verification, alongside general domain benchmarks for commonsense reasoning and world knowledge. It also tests multilingual capability via cascaded machine translation on an Arabic exam dataset. Use when the user wants to benchmark on ClimaBench, Pira 2.0 MCQ, Exeter Misinformation, HellaSwag, PIQA, OpenBookQA, WinoGrande, MMLU, EXAMS (Arabic), or asks about evaluating this task. Reports Acc.

This benchmark evaluates vision-language models on meteorological heatmap analysis, probing their ability to perform spatial localization, color semantics understanding, and anomaly detection through visual question answering. It tests four distinct capabilities: verifying statements about anomalies, enumerating affected regions, geo-indexing precise coordinates, and generating descriptive analyses. Use when the user wants to benchmark on ClimateIQA, or asks about evaluating this task. Report...

This benchmark evaluates multimodal models' ability to perform statistical reasoning and fact verification on scientific charts. It tests whether models can correctly classify claims as supporting, refuting, or not enough information (NEI) based on visual data, and assesses the quality of their generated structured explanatory triplets. Use when the user wants to benchmark on ClimateViz, or asks about evaluating this task. Reports accuracy.

Evaluates the stability and error covariance representation of an AI-based weather prediction model (ClimaX) when integrated into an ensemble data assimilation system (LETKF). It probes the model's ability to generate physically consistent ensemble forecasts, capture flow-dependent error growth, and propagate observation information to unobserved variables without filter divergence. Use when the user wants to benchmark on WeatherBench, or asks about evaluating this task. Reports RMSE.

Evaluates clinical foundation models across diverse medical modalities (imaging, time series, graphs, text) using multitask pretraining, few-shot transfer, and multimodal fusion. It probes model robustness on understudied tasks, adaptation to limited labeled data, and integration of heterogeneous clinical signals for prognosis. Use when the user wants to benchmark on CLIMB, or asks about evaluating this task. Reports balanced AUC.

This benchmark evaluates machine learning models' ability to detect and attribute human-induced climate change signals from daily spatial climate fields. It specifically probes how well models can predict Annual Global Mean Temperature (AGMT) using surface temperature, humidity, and precipitation data, and assesses their sensitivity in identifying the year when climate change signals robustly emerge from natural variability. Use when the user wants to benchmark on ClimDetect, or asks about ev...

Evaluates foundational models on climate intelligence by testing their ability to generate long-form, evidence-grounded answers with accurate citations and relevant multimodal content. It probes knowledge synthesis, hallucination rates in references and images, and alignment with expert-curated quality rubrics. Use when the user wants to benchmark on CLINB, or asks about evaluating this task. Reports ELO score.

Evaluates Chinese medical LLMs on their ability to generate clinically usable, consistent, and safe responses across diverse specialties and difficulty levels. It probes reasoning depth, evidence integration, and longitudinal follow-up rather than raw factual accuracy. Use when the user wants to benchmark on ClinConsensus, or asks about evaluating this task. Reports CACS@7.

Evaluates a model's ability to classify the assertion status of medical entities in clinical text across six categories: present, absent, possible, hypothetical, conditional, and associated with someone else. It benchmarks fine-tuned LLMs, transformer classifiers, rule-based systems, and commercial APIs to measure domain-specific clinical NLP performance. Use when the user wants to benchmark on i2b2 2010, or asks about evaluating this task. Reports weighted avg performance.

Evaluates LLMs on real-world clinical decision-making across three dimensions: effectiveness (accuracy on closed-ended questions), efficiency (proportion of correct, non-redundant reasoning steps), and explainability (quality of clinical reasoning/answers via human or LLM judges). Use when the user has predictions and gold and needs to compute Accuracy.

Evaluates sequential question-selection strategies for recovering target clinical fields from synthetic patient responses under a fixed interaction budget. It probes how well adaptive versus fixed questioning policies handle varying patient communication styles to maximize information coverage within conversational constraints. Use when the user wants to benchmark on Clinical Psychiatric Intake Vignette Benchmark, or asks about evaluating this task. Reports accuracy.

Evaluates a biomedical language model on short- and long-context clinical NLP tasks including classification, named entity recognition, and retrieval. It also measures pre-training masked language modeling accuracy and measures inference efficiency under varying computational loads. Use when the user wants to benchmark on EHR-Prediction (MIMIC-IV ED), MedNER, Pubmed-NCT, PMC-Retrieval, i2b2 2006, i2b2 2010, i2b2 2012, i2b2 2014, or asks about evaluating this task. Reports top-k accuracy.

Evaluates language models' ability to identify and classify standardized medical entities (e.g., diseases, drugs, procedures, genes) in unstructured clinical text. It probes sequence labeling performance under strict terminology standardization (OMOP CDM) to ensure interoperability across diverse healthcare datasets. Use when the user wants to benchmark on NCBI Disease corpus, CHIA, BC5CDR, BIORED, or asks about evaluating this task. Reports Macro Average F1-score (token-based).

This evaluation probes the clinical reasoning, safety, and instruction-following capabilities of large language models on real-world medical datasets. It measures how well models generate accurate and appropriate responses to clinical prompts compared to baseline systems and GPT-3.5-turbo. Use when the user wants to benchmark on MIMIC-III, MIMIC-IV, i2b2, MTSamples, CASI (AE), CASI (CR), DisCQ, or asks about evaluating this task. Reports scores.

Evaluates the capability of transformer-based models to automatically assign numerical scores to clinical patient notes. It probes how masked language modeling pretraining and pseudo-labeling strategies improve scoring performance across different model architectures. Use when the user wants to benchmark on Unspecified clinical patient notes dataset, or asks about evaluating this task. Reports CV Score.

Evaluates NLP models on hierarchical clinical reasoning tasks, including SOAP section segmentation, diagnostic inference via assessment-plan relation labeling, and clinical summarization through problem/action plan extraction. Use when the user wants to benchmark on Clinical Progress Notes (MIMIC-III), or asks about evaluating this task. Reports Cohen's Kappa.

Evaluates a model's ability to predict clinical outcomes (mortality and length of stay) by fusing structured ICU time-series data with medical entities extracted from clinical notes. Use when the user wants to benchmark on Clinical ICU dataset (unspecified), or asks about evaluating this task. Reports AUROC.

Evaluates the safety, accuracy, and interaction quality of a clinical AI voice agent using real-world production call data and clinician-validated simulations. It probes system-level reliability across clinical tasks, conversational dynamics, and operational performance to determine if the model handles noisy, multi-turn healthcare conversations safely. Use when the user wants to benchmark on Live Patient Calls, Clinician-Validated Simulations, HEART, or asks about evaluating this task. Repor...

Evaluates multimodal clinical reasoning and medical knowledge by testing models on standardized medical exams, text-based QA benchmarks, and medical imaging visual question-answering tasks. Use when the user wants to benchmark on USMLE, MedQA, MMLU, MedXpertQA, VQA-RAD, BraTS, PathVQA, Blood Cell VQA, BreaKHis, EMBED, InBreast, CMMD, CBIS-DDS, or asks about evaluating this task. Reports percentage of correct answers.

Evaluates transformer-based models on clinical relation extraction tasks, measuring their ability to identify and classify relationships between medical entities in text. It compares general vs. clinical-pretrained architectures and binary vs. multi-class classification strategies. Use when the user wants to benchmark on MADE1.0, n2c2, or asks about evaluating this task. Reports strict micro-averaged F1-score.

Evaluates the robustness of clinical NLP models against real-world input noise across four standard tasks. It probes whether models maintain performance when text contains character- or word-level perturbations (e.g., misspellings, deletions, negations) that remain human-readable. Use when the user wants to benchmark on i2b2, MedSTS, MedNLI, or asks about evaluating this task. Reports evaluation scores (accuracy/F1).