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Testing & QA
Unit tests, integration tests, E2E, QA, validation, and test automation
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- Synlogic EvalEvaluates the logical reasoning and cross-domain generalization capabilities of models trained with verifiable synthetic data. It measures accuracy on mathematical, coding, and logical reasoning benchmarks using a multi-sample generation and verification protocol. Use when the user wants to benchmark on MATH 500, AIME 2024, AMC 2023, LiveCodeBench, SynLogic coding validation split, or asks about evaluating this task. Reports avg@8.Votes: 0GitHub stars: 3
- Scholarqa Bench EvalEvaluates LLMs' ability to synthesize scientific literature by answering open-ended, multi-domain questions using retrieved papers. It probes long-form generation, factual correctness, citation accuracy, and content quality/organization across single- and multi-paper retrieval setups. Use when the user wants to benchmark on ScholarQABench, or asks about evaluating this task. Reports Citation F1.Votes: 0GitHub stars: 3
- Quan Temp Plus EvalEvaluates open-domain numerical fact-checking by testing how well models can verify claims using decomposed queries and retrieved evidence. It probes the impact of claim decomposition quality on evidence retrieval and downstream NLI-based verification accuracy. Use when the user wants to benchmark on QuanTemp++, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Pearson CorrelationEvaluates the validity of automatic machine translation metrics by measuring their linear correlation with human Direct Assessment (DA) scores at both segment and system levels. It emphasizes rigorous validation protocols, including adaptive sample size determination for human judgments and statistical significance testing to compare metric performance. Use when the user has predictions and gold and needs to compute pearson-correlation.Votes: 0GitHub stars: 3
- News Claim Verification EvalEvaluates a model's ability to verify the veracity of real-world news claims by retrieving relevant evidence documents and predicting a multi-class label. It probes the system's capacity for evidence selection, claim decomposition, and fact-checking under black-box LLM constraints. Use when the user wants to benchmark on RAWFC, LIAR-RAW, or asks about evaluating this task. Reports macro-average F1.Votes: 0GitHub stars: 3
- Navsim Pdm EvalEvaluates end-to-end autonomous driving planners on trajectory prediction and safety-critical behaviors. It measures compliance with traffic rules, drivable area boundaries, collision avoidance, and driving comfort over short-horizon scenarios. Use when the user wants to benchmark on NAVSIM, or asks about evaluating this task. Reports PDM Score (PDMS).Votes: 0GitHub stars: 3
- Moleculenet Property Prediction EvalEvaluates the ability of multimodal molecular representation models to predict diverse physicochemical and biological properties from graph, text, and fingerprint inputs. It probes both classification (binary/multi-label activity prediction) and regression (continuous property estimation) capabilities across standardized chemical benchmarks. Use when the user wants to benchmark on MoleculeNet, or asks about evaluating this task. Reports ROC-AUC, RMSE.Votes: 0GitHub stars: 3
- Mimic Iv Tu Dataset EvalEvaluates self-supervised graph representation learning on electronic health records and general graph classification tasks. It probes the model's ability to learn temporal and structural patient representations without task-specific fine-tuning, and its robustness across clinical and non-clinical domains. Use when the user wants to benchmark on MIMIC-IV, TUDataset, or asks about evaluating this task. Reports binary classification accuracy.Votes: 0GitHub stars: 3
- Meta Omnium EvalEvaluates few-shot meta-learners and transfer learning baselines on their ability to generalize across heterogeneous vision tasks including classification, semantic segmentation, keypoint localization, and regression. It specifically probes cross-task knowledge transfer, in-distribution versus out-of-distribution robustness, and the comparative effectiveness of single-task versus multi-task meta-training protocols. Use when the user wants to benchmark on Meta Omnium, or asks about evaluating ...Votes: 0GitHub stars: 3
- Medmnist V2 EvalEvaluates the ability of machine learning models to classify biomedical images across diverse modalities, tasks, and scales. It probes generalization capabilities by testing on standardized 2D and 3D images resized to 28×28 or 28×28×28, covering binary, multi-class, multi-label, and ordinal regression tasks. Use when the user wants to benchmark on PathMNIST, ChestMNIST, DermaMNIST, OCTMNIST, PneumoniaMNIST, RetinaMNIST, BreastMNIST, BloodMNIST, TissueMNIST, OrganAMNIST, OrganCMNIST, OrganSMNI...Votes: 0GitHub stars: 3
- Ilsep Regression EvalProbes a model's ability to predict gene expression levels from promoter sequences across different cellular contexts, testing its capacity to capture long-range regulatory dependencies and quantitative biological signals. Use when the user wants to benchmark on ILSEP, or asks about evaluating this task. Reports Pearson r.Votes: 0GitHub stars: 3
- Fraud Detection EvalThis evaluation probes a machine learning model's ability to accurately detect fraudulent financial transactions in highly imbalanced tabular data, while also measuring the system-level overhead and economic viability of integrating blockchain-based audit trails. It tests both detection accuracy on real-world and synthetic datasets and the practical throughput/latency constraints of on-chain verification workflows. Use when the user wants to benchmark on Kaggle Credit Card Fraud, Enterprise P...Votes: 0GitHub stars: 3
- Factlens EvalEvaluates the quality of fine-grained claim decomposition and the downstream impact of sub-claim quality on fact verification performance. It probes an LLM's ability to break complex claims into atomic, sufficient, and non-fabricated sub-claims, and measures how these sub-claim properties correlate with verification accuracy. Use when the user wants to benchmark on CoverBench, or asks about evaluating this task. Reports verification F1.Votes: 0GitHub stars: 3
- Cde Regression EvalEvaluates tabular foundation models and traditional baselines on conditional density estimation for regression tasks. It probes density accuracy, probabilistic calibration, prediction sharpness, and computational efficiency across varying training sample sizes and diverse real-world domains. Use when the user wants to benchmark on OpenML & SDSS DR18 regression datasets, or asks about evaluating this task. Reports CDE loss.Votes: 0GitHub stars: 3
- Asr Coprocessor EvalEvaluates the training efficiency and final accuracy of end-to-end acoustic models for Automatic Speech Recognition across different CPU-GPU co-processor hardware configurations. It measures how quickly models reach specific accuracy targets (Time-to-Accuracy) and compares final error rates against baseline architectures. Use when the user wants to benchmark on ASR_Dataset, or asks about evaluating this task. Reports TTA.Votes: 0GitHub stars: 3
- Sindy IdentificationSparse Identification of Nonlinear Dynamics (SINDy) — discover governing equations from time-series data. Builds sparse dynamical system models dx/dt = f(x) from measurements using PySINDy. Use when you have trajectory data and want to find the underlying ODE.Votes: 0GitHub stars: 4
- Ode SolverSolve ordinary differential equations (initial and boundary value problems). Supports stiff/non-stiff systems, event detection, Hamiltonian/symplectic integration, parameter sweeps, and phase space analysis. Use for any ODE system in physics, engineering, or applied math.Votes: 0GitHub stars: 4
- Dimensional AnalysisAutomated dimensional analysis — Buckingham Pi theorem, non-dimensionalization, unit validation with pint, and characteristic scale estimation. Use before any physics computation to verify consistency and reduce parameter space.Votes: 0GitHub stars: 4
- Ml Benchmark EvaluationRigorous methodology for evaluating ML models on established benchmarks. Covers proper train/val/test splits, baseline verification from original papers, exact metric formula discrepancies, data-leak detection checklist, multi-seed robustness, and honest reporting templates. Use when claiming to beat published baselines, writing methods papers, or auditing existing results.Votes: 0GitHub stars: 4
- Bio Workflows Scrnaseq PipelineEnd-to-end single-cell RNA-seq workflow from 10X Genomics data to annotated cell types. Covers QC, normalization, clustering, marker detection, and cell type annotation. Use when analyzing single-cell RNA-seq data.Votes: 0GitHub stars: 4
- Bio Workflows Neoantigen PipelineEnd-to-end neoantigen discovery from somatic variants to ranked vaccine candidates. Integrates HLA typing, MHC binding prediction, pVACtools neoantigen calling, and immunogenicity scoring. Use when identifying tumor neoantigens for personalized vaccine design or checkpoint biomarkers.Votes: 0GitHub stars: 4
- Bio Workflows Multiome PipelineEnd-to-end multiome workflow for joint scRNA-seq + scATAC-seq analysis. Covers data loading, separate modality processing, and WNN integration with Seurat/Signac. Use when analyzing joint scRNA+scATAC data.Votes: 0GitHub stars: 4
- Bio Workflows Metabolomics PipelineOrchestrates the untargeted LC-MS metabolomics pipeline end-to-end (xcms 4.x feature extraction, QC/drift/normalization, confidence-stratified annotation, permutation-validated statistics, background-aware pathway mapping), naming what each stage decides and where it silently fails. Use when running a full LC-MS metabolomics study from raw mzML to enriched pathways and needing the honest handoffs between stages. Each stage defers to its component skill for parameters and traps; for stable-iso...Votes: 0GitHub stars: 4
- Bio Workflows Metabolic Modeling PipelineEnd-to-end genome-scale metabolic modeling from genome sequence to flux predictions. Covers automated reconstruction with CarveMe, model validation with memote, FBA/FVA analysis, and gene essentiality prediction. Use when building metabolic models or predicting metabolic phenotypes from genomic data.Votes: 0GitHub stars: 4