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Claude Skills by qhjqhj00
github.com/qhjqhj007,572 skills29 installs10,436 views
- Exoplanet Anomaly Detection EvalEvaluates the ability of autoencoder-derived latent representations combined with various anomaly detection algorithms to identify chemically anomalous exoplanet transit spectra under realistic observational noise levels. It benchmarks reconstruction loss, 1-class SVM, K-means, and LOF across raw spectral and latent feature spaces. Use when the user wants to benchmark on Exoplanet transit spectra database, or asks about evaluating this task. Reports AUC.Votes: 0GitHub stars: 3
- Exoplanet Demographics EvalEvaluates a cosmological galaxy simulation framework by comparing its synthetic exoplanet population demographics against real observational catalogs from the NASA Exoplanet Archive and Kepler mission. Use when the user wants to benchmark on NASA Exoplanet Archive, Kepler observations, or asks about evaluating this task. Reports planet type fraction.Votes: 0GitHub stars: 3
- Exoplanet Detection EvalEvaluates high-contrast imaging algorithms' ability to detect injected exoplanet companions in real and simulated ADI sequences. It measures detection sensitivity and specificity across different noise regimes and instrument datasets, comparing performance against standard PCA and deep learning baselines. Use when the user wants to benchmark on EIDC (Exoplanet Imaging Data Challenge) Phase 1, or asks about evaluating this task. Reports F1-score.Votes: 0GitHub stars: 3
- Exoplanet Habitability Anomaly EvalEvaluates an unsupervised anomaly detection model's ability to identify potentially habitable exoplanets using planetary parameter features, deliberately excluding direct habitability labels to simulate real-world discovery scenarios. Use when the user wants to benchmark on PHL-EC (Planetary Habitability Laboratory Exoplanet Catalog), or asks about evaluating this task. Reports PHL-HEC intersection.Votes: 0GitHub stars: 3
- Exoplanet Imaging Challenge Ii EvalThis benchmark evaluates the ability of high-contrast imaging post-processing pipelines to accurately estimate the astrometric position of injected exoplanet signals in multispectral astronomical data. It probes how well algorithms handle varying signal-to-noise ratios, complex residual backgrounds (e.g., diffraction patterns, coronagraphic inner working angles), and different observing conditions. Use when the user wants to benchmark on Exoplanet Imaging Data Challenge Phase II, or asks abou...Votes: 0GitHub stars: 3
- Exoplanet Spectra Model BenchmarkEvaluates the consistency of 1D radiative-convective atmospheric models in predicting exoplanet emission and transmission spectra, specifically probing how differences in opacity linelists, line shape treatments, and chemical equilibrium assumptions affect spectral predictions relative to JWST observational uncertainties. Use when the user wants to benchmark on Exoplanet Atmospheric Test Cases, or asks about evaluating this task. Reports spectral_resolution_and_jwst_error_bar_comparison.Votes: 0GitHub stars: 3
- Exoplanet Vit Temporal EvalEvaluates a Vision Transformer's ability to classify exoplanet transits by processing temporal light curve data transformed into image representations (Recurrence Plots and Gramian Angular Fields). It probes the model's capacity to capture long-range temporal dependencies and handle class imbalance in astronomical time-series data. Use when the user wants to benchmark on Kepler Light Curve Exoplanet Candidates, or asks about evaluating this task. Reports F1-score.Votes: 0GitHub stars: 3
- Exp Bench EvalEvaluates AI agents' end-to-end capability to conduct real AI research experiments, including designing methodologies, implementing code, executing experiments, and drawing conclusions. Use when the user wants to benchmark on EXP-Bench, or asks about evaluating this task. Reports All·E✓.Votes: 0GitHub stars: 3
- Explaincpe EvalThis benchmark evaluates large language models on Chinese medical multiple-choice questions, specifically probing their ability to select correct answers and generate faithful, logically consistent free-text explanations. It measures both factual accuracy and the quality of interpretability in high-stakes healthcare domains. Use when the user wants to benchmark on ExplainCPE, or asks about evaluating this task. Reports Accuracy.Votes: 0GitHub stars: 3
- Explained Variance ScoreCompute the explained_variance_score metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute explained_variance_score, or asks how to score with explained_variance_score.Votes: 0GitHub stars: 3
- ExplainedvarianceCompute the ExplainedVariance metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute ExplainedVariance, or asks how to score with ExplainedVariance.Votes: 0GitHub stars: 3
- Explainseg Segmentation EvalEvaluates the ability of an XAI-driven classification model to generate clinically meaningful segmentation masks without pixel-level annotations. It probes spatial coherence, boundary precision, and generalization across diverse medical imaging modalities (mammography, histopathology, endoscopy). Use when the user wants to benchmark on CBIS-DDSM, NuInsSeg, Kvasir-SEG, or asks about evaluating this task. Reports Dice.Votes: 0GitHub stars: 3
- Explanation Disagreement EvalEvaluates the consistency of post-hoc explanation methods by quantifying how much feature attributions differ across algorithms for identical model predictions. It probes whether local explanations are reliable and whether practitioners have principled ways to resolve conflicts when different methods yield conflicting importance scores. Use when the user wants to benchmark on COMPAS, German Credit, News text dataset, PASCAL VOC 2012, or asks about evaluating this task. Reports L2 distance of ...Votes: 0GitHub stars: 3
- Express Bench EvalEvaluates an agent's ability to actively explore 3D environments to gather visual evidence and answer questions accurately, while measuring exploration efficiency and navigation performance. It specifically probes whether the agent's final answer is grounded in the actual visual observations collected during its exploration path, detecting hallucinations and ungrounded reasoning. Use when the user wants to benchmark on EXPRESS-Bench, or asks about evaluating this task. Reports C.Votes: 0GitHub stars: 3
- Express Emotion Recognition EvalEvaluates language models' ability to recognize and decompose fine-grained human emotions from self-disclosed narratives. It probes whether models can align with human emotional expressions across 10 Plutchik-based dimensions (8 basic emotions + 2 sentiments) rather than just predicting surface-level emotion words. Use when the user wants to benchmark on EXPRESS, or asks about evaluating this task. Reports F1_V.Votes: 0GitHub stars: 3
- Expressive Voice Cloning EvalEvaluates zero-shot and adapted voice cloning models on their ability to preserve speaker identity, transfer expressive style (pitch and rhythm), and generate natural-sounding speech. It measures speaker similarity, style fidelity, and perceptual quality across text-to-speech, imitation, and style transfer tasks. Use when the user wants to benchmark on VCTK, Libri-TTS, or asks about evaluating this task. Reports Speaker Classification Accuracy.Votes: 0GitHub stars: 3
- ExtendededitdistanceCompute the ExtendedEditDistance metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute ExtendedEditDistance, or asks how to score with ExtendedEditDistance.Votes: 0GitHub stars: 3
- Extra Cot EvalEvaluates the ability of large language models to generate mathematically reasoned chain-of-thought outputs that are compressed to a target token budget while preserving logical fidelity and answer accuracy. Use when the user wants to benchmark on GSM8K, MATH-500, AMC2023, or asks about evaluating this task. Reports Acc@all.Votes: 0GitHub stars: 3
- Extractbench EvalEvaluates LLMs on complex, schema-driven PDF-to-JSON structured extraction, testing their ability to handle nested objects, arrays, heterogeneous field types, and strict correctness criteria across enterprise-scale documents. Use when the user wants to benchmark on ExtractBench, or asks about evaluating this task. Reports Pass Rate.Votes: 0GitHub stars: 3
- Extremenerf EvalEvaluates few-shot novel view synthesis and depth estimation under unconstrained, varying illumination. It probes a model's ability to maintain geometric consistency and produce photorealistic images when trained on only a few sparse views with different lighting conditions. Use when the user wants to benchmark on Phototourism F^3, NeRF Extreme, LLFF, or asks about evaluating this task. Reports SSIM.Votes: 0GitHub stars: 3
- Ezsql Sql To Text EvalEvaluates a model's capability to generate fluent natural language descriptions from SQL queries (SQL-to-text) and measures how well the generated text can augment training data for Text-to-SQL parsers. Use when the user wants to benchmark on WikiSQL, Spider, or asks about evaluating this task. Reports BLEU-4.Votes: 0GitHub stars: 3
- F Siol 310 EvalEvaluates few-shot incremental learning (FSIL) capabilities in robotic vision, specifically testing a model's ability to learn new object classes sequentially with very limited examples (5 or 10 per class) while resisting catastrophic forgetting of previously learned classes. Use when the user wants to benchmark on F-SIOL-310, or asks about evaluating this task. Reports classification accuracy (%).Votes: 0GitHub stars: 3
- F1 ScoreCompute the f1_score metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute f1_score, or asks how to score with f1_score.Votes: 0GitHub stars: 3
- F1scoreCompute the F1Score metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute F1Score, or asks how to score with F1Score.Votes: 0GitHub stars: 3
- Fabl EvalEvaluates a joint learning framework (FABL) for real-time human behavior recognition using 3D skeletal data from depth sensors. It tests the method's ability to simultaneously select discriminative body parts and features for action classification across public benchmarks and a custom robot-interaction task. Use when the user wants to benchmark on MSR Action3D Dataset, Cornell Activity Dataset 60 (CAD-60), Baxter Robot Serving Drinks Task, or asks about evaluating this task. Reports average r...Votes: 0GitHub stars: 3
- Face Mtl EvalEvaluates a multi-task learning framework for face analysis across heterogeneous tasks including valence-arousal estimation, action unit detection, expression classification, and face recognition. It probes the model's ability to jointly learn from diverse, in-the-wild and lab-controlled facial datasets while mitigating negative transfer through distribution matching. Use when the user wants to benchmark on Aff-Wild, AffectNet, RAF-DB, DISFA, GFT, BP4D, CelebA, or asks about evaluating this t...Votes: 0GitHub stars: 3
- Facebehaviornet EvalEvaluates a multi-task facial analysis model's ability to jointly predict continuous affect (valence/arousal), discrete facial expressions, and facial action units from in-the-wild and lab-controlled face images. It probes the model's capacity for task-coupled learning and generalization across heterogeneous annotation schemes and domains. Use when the user wants to benchmark on Aff-Wild, AffectNet, AFEW, RAF-DB, EmotioNet, DISFA, BP4D, BP4D+, or asks about evaluating this task. Reports CCC.Votes: 0GitHub stars: 3
- Facebook Hateful Memes EvalThis benchmark evaluates multimodal hate speech detection by classifying image-caption pairs (memes) as hateful or non-hateful. It probes a model's ability to align visual and textual cues while resisting spurious correlations, particularly under different prompt structures and data augmentation strategies. Use when the user wants to benchmark on Facebook Hateful Memes, or asks about evaluating this task. Reports weighted-F1 score.Votes: 0GitHub stars: 3
- Faceid 6m EvalThis evaluation protocol assesses the effectiveness of a large-scale dataset for training conditional diffusion models in FaceID customization. It probes the model's ability to preserve facial identity from a reference image while adhering to textual prompts and maintaining overall image quality. Use when the user wants to benchmark on COCO2017, Unsplash-50, or asks about evaluating this task. Reports Face Sim.Votes: 0GitHub stars: 3
- Facescape EvalEvaluates single-view 3D face reconstruction models on their ability to predict high-fidelity, expression-specific dynamic details (displacement maps) and generate riggable 3D face meshes from a single image. It probes geometric accuracy, detail synthesis, and generalization across multiple expressions and real-world sequences. Use when the user wants to benchmark on FaceScape, Volker Sequence, or asks about evaluating this task. Reports mean absolute point-to-surface distance.Votes: 0GitHub stars: 3
- Facet Fairness EvalThis benchmark probes the intersectional fairness of computer vision models by evaluating their performance across diverse demographic attributes (e.g., skin tone, gender presentation, hair type) and person-related categories (e.g., occupations, hobbies). It measures whether models exhibit systematic performance disparities when detecting, classifying, or segmenting individuals with different attribute combinations. Use when the user wants to benchmark on FACET, or asks about evaluating this ...Votes: 0GitHub stars: 3
- Facial Attribute Prediction EvalTests the model's capability to predict multiple facial attributes (e.g., gender, hairstyle) from a single facial image as a multilabel classification task. It probes fine-grained visual feature extraction and attribute-level alignment. Use when the user wants to benchmark on CelebA, LFWA, or asks about evaluating this task. Reports Average Precision (AP).Votes: 0GitHub stars: 3
- Facial Emotion Analysis EvalEvaluates vision-language models on facial emotion analysis tasks, including fine-grained action unit detection, categorical emotion recognition, and grounded natural language reasoning over facial expressions. The protocol tests both recognition accuracy and the model's ability to generate interpretable, AU-grounded explanations. Use when the user wants to benchmark on DISFA, BP4D, RAF-AU, FER2013, AffectNet, RAF-DB, FABA-Instruct, FEA-20K, or asks about evaluating this task. Reports F1 scor...Votes: 0GitHub stars: 3
- Facies Classification EvalThis benchmark evaluates machine learning models on 3D seismic facies classification, a task critical for geological interpretation. It probes a model's ability to accurately segment and label distinct geological strata from 3D seismic data using both local patch-based and global section-based contextual information. Use when the user wants to benchmark on F3 Block, or asks about evaluating this task. Reports MCA.Votes: 0GitHub stars: 3
- Fact Based Oie EvalEvaluates Open Information Extraction systems on their ability to correctly extract complete facts from sentences, moving beyond token-level overlap to fact-level exact matching against exhaustive gold synsets. It measures whether a system can identify all surface realizations of a fact and penalizes extractions that contain correct tokens but express incorrect or incomplete facts. Use when the user wants to benchmark on CaRB, or asks about evaluating this task. Reports Precision, Recall, F1 ...Votes: 0GitHub stars: 3
- Fact Checking Arena EvalEvaluates LLMs on multi-hop fact-checking by measuring claim extraction, evidence retrieval, and justification quality. Uses an arena-style pairwise comparison framework with LLM judges to rank models across multiple reasoning dimensions. Use when the user wants to benchmark on HOVER, FEVERIOUS, or asks about evaluating this task. Reports Accuracy (%).Votes: 0GitHub stars: 3
- Factcheck Kg Validation EvalEvaluates large language models' ability to validate factual claims in knowledge graphs by classifying triples as true or false. It probes internal knowledge retrieval, retrieval-augmented generation (RAG) with external search results, and multi-model consensus strategies for fact-checking reliability. Use when the user wants to benchmark on FactBench, YAGO, DBpedia, or asks about evaluating this task. Reports Class-wise F1 Score.Votes: 0GitHub stars: 3
- Factir EvalEvaluates open-domain retrieval and re-ranking systems on real-world fact-checking claims. It probes the ability to retrieve indirect, multifaceted evidence from unstructured web sources to support or refute complex queries involving health, politics, and economics. Use when the user wants to benchmark on FactIR, or asks about evaluating this task. Reports nDCG@k.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
- Factual Scene Graph Parsing EvalThis benchmark evaluates a model's ability to parse natural language captions into structured scene graphs that faithfully represent described visual elements. It probes compositional generalization and output consistency by testing parsers on both standard and length-constrained splits, measuring how well generated graph structures align with human-annotated ground truth. Use when the user wants to benchmark on FACTUAL, or asks about evaluating this task. Reports SPICE.Votes: 0GitHub stars: 3
- Factuality ScoreEvaluates the quality of synthetically generated natural language reports derived from tabular data. It probes factual grounding, narrative coherence, hallucination, and the precise preservation of numerical and temporal information from the source table. Use when the user has predictions and gold and needs to compute factuality_score.Votes: 0GitHub stars: 3
- Faetar Benchmark EvalEvaluates automatic speech recognition (ASR) models on a highly under-resourced language (Faetar/Franco-Provençal) characterized by noisy field recordings, lack of standard orthography, and inconsistent phonetic transcriptions. Use when the user wants to benchmark on Faetar Benchmark, or asks about evaluating this task. Reports PER.Votes: 0GitHub stars: 3
- Fair Credit Scoring EvalThis benchmark evaluates the trade-off between algorithmic fairness and financial profitability in credit scoring models. It probes how well various fairness-aware preprocessing, in-processing, and post-processing techniques maintain predictive accuracy and demographic parity while minimizing economic loss for lenders. Use when the user wants to benchmark on german, bene, taiwan, uk, pakdd, gmsc, homecredit, or asks about evaluating this task. Reports Profitability (Profit per EUR).Votes: 0GitHub stars: 3
- Fair Income Distribution EvalAssesses the fairness of a country's income distribution by comparing its Gini index and quintile income shares against a theoretical benchmark derived from professional sports salary allocations. The benchmark assumes that sports salaries, determined by performance and transparent rules, represent a procedurally and distributively fair standard. Use when the user wants to benchmark on World Bank Income Data, or asks about evaluating this task. Reports percentage deviation.Votes: 0GitHub stars: 3
- Fair Inference Causal Mediation EvalEvaluates whether a predictive model can satisfy fairness constraints defined by causal mediation analysis (NDE/PSE) while maintaining out-of-sample accuracy. It probes the model's ability to isolate and eliminate discriminatory pathways from sensitive attributes to outcomes without relying on fully specified outcome models. Use when the user wants to benchmark on COMPAS, Adult (UCI), or asks about evaluating this task. Reports NDE (odds ratio).Votes: 0GitHub stars: 3
- Fair Summm EvalEvaluates the fairness of abstractive summarization by measuring distributional alignment across social attributes (e.g., sentiment, gender, party). It quantifies how well generated summaries preserve the proportion of diverse perspectives present in the source text, penalizing underrepresentation of minority viewpoints. Use when the user wants to benchmark on PERSPECTIVESUMM, or asks about evaluating this task. Reports Binary Unfair Rate (BUR).Votes: 0GitHub stars: 3
- Fair Weak Supervision EvalEvaluates a weak supervision pipeline's ability to mitigate labeling function bias and improve fairness across demographic groups. It measures how well a source bias mitigation method recovers accurate pseudolabels while reducing disparities in prediction rates between privileged and underrepresented groups. Use when the user wants to benchmark on Adult, Bank Marketing, CivilComments, HateXplain, CelebA, UTKFace, WRENCH, or asks about evaluating this task. Reports demographic parity gap ($\De...Votes: 0GitHub stars: 3
- Fairco Dynamic Ltr EvalThis evaluation probes a dynamic learning-to-rank algorithm's ability to balance ranking quality with group-level fairness under position bias. It tests whether the model can maintain high relevance-based ranking performance while actively controlling exposure and impact disparities between predefined item groups over a sequence of user interactions. Use when the user wants to benchmark on Ad Fontes Media Bias (semi-synthetic news), MovieLens-20M, or asks about evaluating this task. Reports a...Votes: 0GitHub stars: 3
- Faircontrast Tabular EvalThis evaluation probes a model's ability to learn fair representations from tabular data by balancing predictive accuracy with demographic parity. It measures how effectively the model mitigates bias across privileged and unprivileged groups defined by sensitive attributes such as gender or age. Use when the user wants to benchmark on Adult, German Credit, Heritage Health, or asks about evaluating this task. Reports Demographic Parity (DP).Votes: 0GitHub stars: 3
- Fairdomain EvalEvaluates cross-domain medical image segmentation and classification performance while measuring demographic fairness across gender, race, and ethnicity. It probes whether models maintain equitable accuracy across demographic groups when domain shifts occur between imaging modalities (En face vs. SLO fundus images). Use when the user wants to benchmark on FairDomain-Segmentation, or asks about evaluating this task. Reports ESP (Equity-Scaled Performance).Votes: 0GitHub stars: 3