Claude Skills by qhjqhj00

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Evaluates a model's ability to predict the 3D spatial arrangement (binding pose) of a small molecule ligand when docked to a target protein structure. It probes geometric reasoning, conformational sampling, and the capacity to generate physically plausible protein-ligand complexes. Use when the user wants to benchmark on PDBBind, or asks about evaluating this task. Reports L-RMSD.

Evaluates an LLM's ability to perform zero-shot dialogue segmentation and joint state tracking on real-world, open-domain human-LLM conversations. It probes the model's capacity to identify topic shifts, assign intent and domain labels, and maintain context over long multi-turn interactions without hallucination. Use when the user wants to benchmark on Bing Chat (Internal Human-LLM Dialogue Dataset), or asks about evaluating this task. Reports JGA (I/D).

Evaluates AI agents' ability to execute multi-step bioinformatics pipelines (e.g., RNA-seq, variant calling) under normal and perturbed conditions. It probes step-level reasoning, tool-use robustness, and the capacity to produce correctly formatted final artifacts despite input corruption or prompt bloat. Use when the user wants to benchmark on BioAgent Bench Tasks, or asks about evaluating this task. Reports completion rate (%).

Evaluates a model's ability to identify and classify biomedical entities (diseases, drugs/chemicals, genes/proteins, species) in text using transfer learning from domain-specific pre-training. It tests whether contextualized representations improve entity boundary detection and classification on small biomedical corpora. Use when the user wants to benchmark on NCBI disease, 2010 i2b2/VA, BC5CDR, BC4CHEMD, BC2GM, JNLPBA, LINNAEUS, Species-800, or asks about evaluating this task. Reports entity...

Evaluates factoid question answering performance on small biomedical datasets, testing transfer learning effectiveness from domain-specific pre-training. It measures how well the model retrieves exact or lenient answers to biomedical queries. Use when the user wants to benchmark on BioASQ 4b, BioASQ 5b, BioASQ 6b, or asks about evaluating this task. Reports Mean Reciprocal Rank (MRR).

Tests a model's capability to extract biomedical relations (gene-disease, gene-chemical) from text using minimal task-specific modifications. It probes whether domain-specific pre-training improves relation classification on small-scale biomedical corpora. Use when the user wants to benchmark on GAD, EU-ADR, CHEMPROT, or asks about evaluating this task. Reports entity-level F1.

Evaluates zero-shot species classification and fine-grained text-image retrieval capabilities in biological domains. Probes the model's ability to align visual features with taxonomic labels and descriptive natural language without task-specific fine-tuning. Use when the user wants to benchmark on NABirds, Meta-Album (Plankton, Insects, Insects 2), IDLE-OO Camera Traps, Rare Species, PlantNet, Fungi, PlantVillage, Med. Leaf, INQUIRE-Rerank, Cornell Bird, PlantID, or asks about evaluating this...

Evaluates long-context clinical NLP encoders on biomedical entity recognition, clinical text classification, and demographic information extraction. Probes the model's ability to process full-length clinical notes (up to 8,192 tokens) and retain domain-specific knowledge without truncation. Use when the user wants to benchmark on ChemProt, Phenotype, Social History, DEID, COS, or asks about evaluating this task. Reports F1 score.

Evaluates the ability of audio denoising models to remove background noise from animal vocalization recordings without access to clean reference data during training. It measures how well models generalize across diverse species and environments using synthetic mixtures and a held-out benchmark set. Use when the user wants to benchmark on Biodenoising benchmark set, or asks about evaluating this task. Reports SI-SDR.

Evaluates large language models on biomedical natural language processing tasks, including multiple-choice question answering, natural language inference, clinical information extraction, and text generation. It probes the model's ability to follow domain-specific instructions, extract precise medical entities, and generate coherent clinical notes or answers. Use when the user wants to benchmark on MedQA-USMLE, MedMCQA, PubmedQA, BioASQ MCQA, MedNLI, Medication Status Extraction, Coreference ...

Evaluates the ability of merged multimodal large language models to perform cross-modal biological reasoning tasks, specifically predicting molecular interactions with proteins/cells and predicting enzyme functionality. It probes whether embedding-space-aware merging preserves modality-specific expertise better than parameter-space heuristics or fine-tuning. Use when the user wants to benchmark on Biological MLLM Interaction & Functionality Benchmarks, or asks about evaluating this task. Repo...

Evaluates frozen visual embedding extractors on ecological trait alignment, fine-grained intra-species variation preservation, and zero-shot/few-shot transfer learning across diverse biological domains. Use when the user wants to benchmark on FishNet, NeWT, AwA2, Herb, PlantDoc, Life stage-Diff/Align, Sex-Diff/Align, or asks about evaluating this task. Reports accuracy.

Evaluates the biomedical knowledge, clinical reasoning, and domain-specific comprehension of language models using multiple-choice question-answering benchmarks across anatomy, clinical medicine, genetics, and multilingual medical QA. Use when the user wants to benchmark on MMLU Professional Medicine, MedQA, MedMCQA, PubMedQA, FrenchMedMCQA, or asks about evaluating this task. Reports accuracy.

Evaluates the domain-adaptive post-training of multimodal large language models on biomedical visual question answering tasks, measuring how well models generalize to specialized medical domains using both open and closed evaluation splits. Use when the user wants to benchmark on SLAKE, PathVQA, VQA-RAD, PMC-VQA, or asks about evaluating this task. Reports accuracy.

Probes an LLM's ability to generate syntactically and semantically correct Cypher queries for a biomedical knowledge graph, and execute them to answer domain-specific questions without hallucination. Use when the user wants to benchmark on Custom Biomedical QA Benchmark, or asks about evaluating this task. Reports accuracy.

This benchmark evaluates large language models on four core biomedical natural language processing tasks: event extraction, relation extraction, named entity recognition, and text classification. It probes the models' ability to identify complex biomedical entities, relationships, and events, as well as classify medical texts, highlighting precision-recall trade-offs in domain-specific applications. Use when the user wants to benchmark on PHEE, Genia2013, Genia2011, DDI, GIT, BioRED, BC5CDR, ...

Evaluates the robustness and classification accuracy of deep learning models on biomedical time-series signals (ECG and EEG). It probes the model's ability to handle class imbalance, signal noise, and diverse diagnostic categories without relying on traditional oversampling techniques. Use when the user wants to benchmark on PTB Diagnostic ECG Database, MIT-BIH Arrhythmia Database, UCI Seizure EEG Dataset, or asks about evaluating this task. Reports Accuracy, F1 Score.

Probes an AI agent's ability to answer pharmacology questions by querying federated biomedical knowledge graphs. It evaluates three access methods—direct MCP tools, text-to-Cypher generation, and standalone LLM reasoning—to measure factual accuracy and query efficiency. Use when the user wants to benchmark on BiomedQA, or asks about evaluating this task. Reports Accuracy.

This evaluation probes a model's ability to verify scientific claims against provided or retrieved evidence in a binary classification setting. It measures performance on Supported vs. Refuted labels, testing factual grounding, uncertainty calibration, and the impact of atomic decomposition and web corroboration. Use when the user wants to benchmark on BIONLI-300, or asks about evaluating this task. Reports Balanced Accuracy.

Evaluates a Persian biomedical large language model's ability to generate accurate, domain-specific long-form answers and summaries. It probes subject-specific knowledge acquisition, knowledge synthesis, and evidence-based reasoning by comparing model outputs against human-written biomedical references. Use when the user wants to benchmark on BioPars-BENCH, or asks about evaluating this task. Reports BERTScore.

This benchmark evaluates large language models on biomedical question-answering, specifically probing their factuality, robustness to linguistic variations (paraphrasing and typos), and susceptibility to demographic bias (age and gender). It distinguishes between extractive and abstractive reasoning capabilities using expert-verified QA pairs from clinical documents. Use when the user wants to benchmark on BioPulse-QA, or asks about evaluating this task. Reports F1.

Evaluates a compound AI architecture's ability to retrieve, synthesize, and reason across cross-disciplinary scientific knowledge. It probes performance on established single-domain science benchmarks and a novel benchmark specifically designed for bio-AI cross-domain synthesis and reasoning. Use when the user wants to benchmark on LitQA2, GPQA, WMDP, HLE-Bio, BioSage Cross-Disciplinary Benchmark, or asks about evaluating this task. Reports accuracy.

Evaluates models on insect biodiversity monitoring by testing closed-world species identification, open-world genus-level grouping for novel species, and zero-shot clustering of multimodal embeddings against taxonomic ground truth. Use when the user wants to benchmark on BIOSCAN-5M, or asks about evaluating this task. Reports Fine-tuned accuracy.

Evaluates the ability of models to perform clinical named entity recognition in Urdu, specifically identifying and classifying biomedical entities like diseases, genes, and proteins within clinical text sequences. It probes sequence labeling capabilities in a low-resource, domain-specific language setting. Use when the user wants to benchmark on BioUNER, or asks about evaluating this task. Reports F1 score.

This benchmark evaluates LLM-based information retrieval systems on complex, multi-faceted query objectives that go beyond simple lexical or semantic similarity. It probes whether models can correctly rank documents based on structured tasks like refuting claims, measuring drug effects, or identifying specific book details, often requiring explicit task understanding rather than just passage matching. Use when the user wants to benchmark on DORIS-MAE, ArguAna, WhatsThatBook, Clinical-Trial, R...

Evaluates the capability of large language models to generate correct SQL queries from natural language questions. It specifically probes how annotation noise and errors in benchmark datasets affect model performance and reliability. Use when the user wants to benchmark on BIRD-Bench, or asks about evaluating this task. Reports accuracy.

This benchmark evaluates an LLM's ability to debug and fix real-world, user-reported SQL issues. It probes the model's capacity to identify logical, syntactic, and semantic flaws in existing queries and generate correct, functional SQL replacements across different database dialects and complexity levels. Use when the user wants to benchmark on BIRD-CRITIC, or asks about evaluating this task. Reports Success Rate (SR).

Evaluates an LLM's ability to generate syntactically correct and semantically accurate SQL queries from natural language questions over large, real-world databases. It probes database schema understanding, value matching, external knowledge incorporation, and query execution efficiency. Use when the user wants to benchmark on BIRD, or asks about evaluating this task. Reports Execution Accuracy (EX).

Evaluates an LLM's ability to generate executable Python code for file-based data retrieval tasks from natural language questions. It probes the model's capacity to handle explicit procedural logic, resolve ambiguous user intent, and correctly apply domain knowledge without relying on implicit database semantics. Use when the user wants to benchmark on BIRD-Python, or asks about evaluating this task. Reports LLM-based Execution Accuracy (EX).

This benchmark evaluates a model's ability to detect fraudulent user accounts in e-commerce and app review platforms by analyzing their rating patterns and temporal behavior. It probes whether the model can identify users who exhibit extreme rating biases or bursty posting times indicative of coordinated spam or defamation campaigns. Use when the user wants to benchmark on Flipkart, SWM, or asks about evaluating this task. Reports precision@k.

Evaluates deep learning models on multi-label audio classification for avian bioacoustics, specifically probing robustness to covariate shift, class imbalance, and noisy labels in passive acoustic monitoring scenarios. Use when the user wants to benchmark on BirdSet, or asks about evaluating this task. Reports cmAP.

Evaluates the trade-off between safety and efficiency of energy-function-based safe control algorithms in human-robot interaction and robot co-working scenarios. It tests how well different controllers navigate toward goals while avoiding collisions with human or robot agents across varying dynamic models. Use when the user wants to benchmark on BIS (Benchmark of Interactive Safety), or asks about evaluating this task. Reports efficiency_score.

Evaluates the performance of the BiSHop model on tabular classification and regression tasks, probing its ability to handle mixed feature types, bi-directional cellular learning, and generalized sparse modern Hopfield layers. Use when the user wants to benchmark on Tabular Benchmarks (Adult, Bank, Blastchar, Income, SeismicBump, Shrutime, Spambase, Qsar, Jannis, CR), or asks about evaluating this task. Reports AUC (%).

Evaluates the robustness of neural network architectures (MLPs, CNNs, and Differentiable Weightless Networks) to parameter bit-flips under varying corruption rates. It measures how task accuracy degrades as a function of bit error rate (BER) and isolates the impact of architectural hyperparameters like precision, width, depth, activation functions, and sparsity. Use when the user wants to benchmark on MLPerf Tiny, or asks about evaluating this task. Reports accuracy.