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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Bio Mystery BenchBioMysteryBench methodology for evaluating AI bioinformatics research capabilities. Tasks models with analyzing real-world biological datasets using method-agnostic, ground-truth-verified questions that allow superhuman evaluation.Votes: 0GitHub stars: 3
- Bio Neuron Snn Learning生物神经元参数学习SNN方法论。联合优化突触权重和神经元内在参数，结合Lempel-Ziv复杂度实现可解释的时空脉冲数据分类。适用于脉冲神经网络、神经形态计算、可解释AI。触发词：生物神经元、SNN参数学习、Lempel-Ziv复杂度、神经形态计算、biological neuron、LZC、spiking neural network。Votes: 0GitHub stars: 3
- Bio Quantum Pso OptimizationHybrid optimization combining quantum-behaved particle swarm optimization with bio-inspired swarm intelligence mechanisms. Integrates quantum-probabilistic position updates with ant colony and bee foraging behavior for multimodal and biologically structured search landscapes. Use when solving complex optimization problems requiring global search with local refinement, multimodal optimization, or hybrid quantum-bio approaches.Votes: 0GitHub stars: 3
- Biological Plausibility Assessment SnnAutomated framework for assessing biological plausibility of spiking neuron models using Izhikevich firing pattern classification. Use when evaluating spiking neuron models, comparing neuromorphic designs, or quantifying how well artificial neurons replicate biological behavior.Votes: 0GitHub stars: 3
- Biomysterybench EvaluationMethodology from Anthropic research (Apr 29, 2026) for benchmarking LLM bioinformatics research capabilities.Votes: 0GitHub stars: 3
- BiomysterybenchBioMysteryBench methodology for benchmarking LLM bioinformatics research capabilities on real-world datasets with consensus-based grading and path-independent evaluationVotes: 0GitHub stars: 3
- Bispikclm Binary Spiking LlmBiSpikCLM methodology — the first fully binary spiking MatMul-free causal language model. Integrates Softmax-Free Spiking Attention (SFSA) and Spike-Aware Alignment Distillation (SpAD) to train energy-efficient spiking LLMs. Use when building spiking language models, energy-efficient NLP, binary spiking networks, spiking attention mechanisms, or knowledge distillation for SNNs. Trigger words: spiking language model, binary spiking, softmax-free attention, spike-aware distillation, BiSpikCLM, ...Votes: 0GitHub stars: 3
- Bleg Llm Brain Graph EnhancerBLEG (LLM-Enhanced Brain Graph Analysis) methodology. Integrates LLMs with brain graph neural networks for improved neurological disease classification via knowledge-enhanced connectivity representation.Votes: 0GitHub stars: 3
- Bleg Llm Functions As Powerful FmriBLEG (Brain LLM Enhanced Graph) - using Large Language Models as fMRI graph enhancers for brain network analysis. LLM-augmented GNNs for sparse neurograph learning. Keywords: LLM, fMRI, brain network, GNN, graph enhancement, multimodal fusion.Votes: 0GitHub stars: 3
- Blend Behavior Guided Neural行为指导的神经种群动力学建模方法论（BLEND）。通过特权知识蒸馏， 在训练时利用行为信号，推理时仅需神经活动。 触发词：神经种群动力学、行为建模、知识蒸馏、特权信息、BLEND、 neural population dynamics, behavior-guided, privileged distillation。Votes: 0GitHub stars: 3
- Blockrank Scalable Incontext RankingBlockRank methodology for scalable In-context Ranking (ICR) with LLMs. Enforces structured sparse attention (linear complexity) + auxiliary contrastive loss for efficient document ranking. Activation triggers: in-context ranking, blockrank, listwise reranking, LLM retrieval, attention sparsity, efficient ranking, ICR, document ranking with LLMsVotes: 0GitHub stars: 3
- Boltzmann Attention IsingBoltzmann Attention methodology — energy-based attention mechanism using learnable Ising model pairwise couplings for cooperative attention. Augments data-dependent local fields with learnable inter-position correlations, enabling diabatic quantum annealing as a practical training strategy. Improves over softmax attention on character-level LM and bracket matching, with advantage scaling with sequence length. Activation: Boltzmann attention, Ising attention, energy-based attention, cooperativ...Votes: 0GitHub stars: 3
- Boosting Brain To Image Tribe V2TRIBE v2 data augmentation methodology for brain-to-image decoding. Uses pretrained encoding model on 1000+ hours of video/audio/language fMRI to generate synthetic data, achieving 68% improvement in Top-10 retrieval accuracy. Supports zero-shot decoding when trained exclusively on synthetic fMRI.Votes: 0GitHub stars: 3
- Bosonic Gkp Parity EncodingLoss-tolerant quantum communication using Bosonic Gottesman-Kitaev-Preskill (GKP) parity encoding. Implements quantum repeaters with concatenated Bell state measurement for long-distance quantum communication. Activation: bosonic GKP, quantum repeater, loss-tolerant communication, quantum error correction.Votes: 0GitHub stars: 3
- Bosonic Grid States QecBosonic quantum error correction using Gottesman-Kitaev-Preskill (GKP) grid states and programmable nonlinear bosonic circuits. Covers grid state preparation, logical qubit encoding in continuous-variable oscillators, Wigner function characterization, and fault-tolerant bosonic QEC architectures. Use when working on: bosonic codes, GKP states, cat/qubit encodings, continuous-variable QEC, nonlinear bosonic gates, or oscillator-based quantum computing. Triggers: bosonic QEC, GKP grid states, c...Votes: 0GitHub stars: 3
- Bounded Degree Max Linsat DqiApproximability limits for bounded-degree max-LINSAT and implications for decoded quantum interferometryVotes: 0GitHub stars: 3
- Brain Alignment Learning Rules ComparisonComparative methodology for brain alignment across learning rules (BP, FA, PC, STDP). Key finding: single training epoch reduces V1 alignment by 25-90%. BP most destructive, PC and STDP preserve brain-like structure. Use when: brain alignment, representational similarity analysis, biologically plausible learning, visual cortex modeling, learning rule comparison. arXiv: 2605.30556Votes: 0GitHub stars: 3
- Brain Alignment Vlm Lam GameplayBrain alignment of vision-language models (VLMs) and large-action models (LAMs) with fMRI during naturalistic gameplay. Use when: studying brain-AI alignment during interactive tasks, comparing VLMs vs LAMs neural encoding, analyzing action vs reasoning representations in frontal-parietal cortex, or designing fMRI encoding studies with foundation models.Votes: 0GitHub stars: 3
- Brain Brainstorming Generative ModelsGenerative models for brain "brainstorming" — studying the brain's spontaneous idea generation using free energy principle, critical dynamics, and default mode network analysis. Based on Smith et al. (2026) linking neural criticality, predictive coding, and spontaneous thought patterns. Triggers: brain brainstorming, spontaneous thought, neural idea generation, predictive coding creativity, free energy imagination, default mode network generative, 脑风暴生成模型, 自发思维生成Votes: 0GitHub stars: 3
- Brain Cause Causal Visual RepresentationCausal visual representation discovery framework for neuroscience. Use when analyzing brain region representations through causal testing rather than mere activation maximization. Covers counterfactual stimulus generation, image-to-fMRI encoding models, automated functional localization validation, and follow-up experiment design. Triggers: causal neuroscience, brain representation, counterfactory fMRI, visual concept localization, activation causality, functional localization validation, Bra...Votes: 0GitHub stars: 3
- Brain Cause Causal Visual RepresentationsBrainCause methodology for discovering and causally validating visual representations in the human brain using generative models, counterfactual stimulus synthesis, and fMRI encoding models. Use when: (1) studying causal vs correlational brain representations, (2) designing controlled fMRI experiments with counterfactual stimuli, (3) validating whether brain regions truly represent specific visual concepts beyond activation-based localization, or (4) applying generative AI to neuroscience bra...Votes: 0GitHub stars: 3
- Brain Cliplm Semantic Compression EegBrain-CLIPLM semantic compression framework for EEG-to-text decoding. Two-stage methodology: semantic anchor recovery via contrastive learning + anchor-guided sentence reconstruction with retrieval-grounded LLM. Key principle: granularity matching - aligns decoding complexity with recoverable neural information scale. Use when: (1) EEG language decoding tasks, (2) brain-to-text translation, (3) neural signal semantic extraction, (4) cognitive state reconstruction from EEG, (5) sentence-level ...Votes: 0GitHub stars: 3
- Brain Connectivity AnalysisBrain network connectivity analysis using knowledge graph tools. Analyze brain connectivity patterns, neural networks, and graph-based brain models. Use when working with brain graphs, connectivity matrices, neural network analysis, or integrating neuroscience papers into knowledge graphs. Supports PageRank for important nodes, Louvain community detection, and similarity search for related research.Votes: 0GitHub stars: 3
- Brain Critical Dynamics HierarchicalHierarchical organization of critical brain dynamics. Analyze how brain networks exhibit critical behavior across multiple scales, including neuronal avalanches, power-law distributions, and long-range temporal correlations. Use when studying brain criticality, neural avalanches, scale-free dynamics, phase transitions in neural systems, or multi-scale brain network analysis. Combines renormalization group theory, statistical physics, and network science approaches.Votes: 0GitHub stars: 3
- Brain Criticality AssessmentCritical assessment methodology for evaluating the brain criticality hypothesis using rigorous statistical and computational approaches. Use for analyzing criticality in neural systems, avalanche dynamics, and evaluating claims of critical brain states. Keywords: criticality, neural avalanches, brain networks, critical states, statistical mechanics, power laws.Votes: 0GitHub stars: 3
- Brain Criticality Hypothesis AssessmentCritical assessment methodology for evaluating the brain criticality hypothesis. Proposes Memory-Induced Long-Range Order (MILRO) as an alternative explanation for scale-invariant correlations in neural activity. Use for analyzing neural avalanches, criticality claims, and brain dynamics theory. Keywords: brain criticality, MILRO, neural avalanches, scale-invariant correlations, memory-induced long-range order, critical point.Votes: 0GitHub stars: 3
- Brain Criticality Milro AssessmentMemory-Induced Long-Range Order (MILRO) assessment framework challenging the brain criticality hypothesis. Analyzes scale-invariant correlations in neural activity as stable phase rather than critical point. Keywords: brain criticality, MILRO, scale-free, neural correlations, memory-induced orderVotes: 0GitHub stars: 3
- Brain Data Value Scaling LawsMathematical framework for quantifying the value of brain data for machine learning. Derives scaling laws, exchange rates between brain and task samples, and conditions for robustness gains via neural regularization. Activation: brain data value, neural data worth, brain-regularized learning, neuroai scaling laws, brain sample exchange rate.Votes: 0GitHub stars: 3
- Brain Digital Twins Execution Semantics V3Framework for brain digital twins centered on execution semantics, bridging computational brain models to executable systems. Unifies fragmented data pipelines, model classes, and temporal scales. Activation: brain-digital-twins, execution-semantics, neuromorphic, modeling, neuroscience, brain, neuralVotes: 0GitHub stars: 3
- Brain Digital Twins Execution Semantics V4Brain digital twins execution semantics survey bridging computational neuroscience to neuromorphic systems - arXiv:2604.13574 (April 2026). Covers physically constrained executability taxonomy, execution regimes, hybrid-time correctness, and neuro-neuromorphic physical systems. Supersedes v3 with systems/runtime perspective.Votes: 0GitHub stars: 3
- Brain Digital Twins Execution SemanticsBrain digital twins execution semantics framework bridging computational modeling and neurobiological dynamics. Covers physically constrained executability, end-to-end workflow preservation, and neuromorphic implementation. Activation: brain digital twins, execution semantics, neuro-neuromorphic, computational neuroscience.Votes: 0GitHub stars: 3
- Brain Dit Fmri Foundation Model V4Brain-DiT universal multi-state fMRI foundation model methodology. Metadata-conditioned diffusion pretraining with DiT on 349,898 sessions across 24 datasets spanning resting, task, naturalistic, disease, and sleep states. Activation: brain-dit, fmri foundation model, diffusion transformer brain, metadata-conditioned pretraining, multi-state fmri, brain diffusionVotes: 0GitHub stars: 3
- Brain Dit Fmri Foundation Model V5Brain-DiT v5 universal multi-state fMRI foundation model with pre-training and fine-tuning for zero-shot and few-shot brain decoding across multiple states. Supports cross-task, cross-subject, and cross-dataset fMRI analysis using diffusion transformer architecture. Use when: fMRI foundation models, brain decoding, diffusion transformers for neuroimaging, cross-subject fMRI analysis, zero-shot brain state prediction, multi-task fMRI modeling, neural state decoding, fMRI pre-training. Activati...Votes: 0GitHub stars: 3
- Brain Dit Fmri Foundation Model V6Brain-DiT v6 universal multi-state fMRI foundation model with metadata-conditioned pretraining across 24 datasets covering resting, task, naturalistic, disease, and sleep states. Use when working with fMRI foundation models, brain state decoding, or multi-state neuroimaging.Votes: 0GitHub stars: 3
- Brain Dit Fmri Foundation ModelBrain-DiT universal multi-state fMRI foundation model with metadata-conditioned diffusion pretraining. Trigger words: Brain-DiT, fMRI foundation model, diffusion transformer, multi-state, metadata-conditionedVotes: 0GitHub stars: 3
- Brain Dit Universal Multi StateBrain-DiT universal multi-state fMRI foundation model methodology. Integrates diffusion transformer architecture with fMRI data for generative modeling and brain state analysis. Covers multi-state fMRI generation, brain state transition modeling, and foundation model fine-tuning for neuroscience applications. Use when working with fMRI foundation models, brain state generation, diffusion models for neuroimaging, or multi-state neural dynamics simulation.Votes: 0GitHub stars: 3
- Brain Dnn Transformation AlignmentNaturality Violation Score (NVS) for brain-DNN alignment beyond object-level comparison. Uses category theory to test whether brains and DNNs preserve the same transformations among stimuli. Covers approximate naturality, axis-resolved alignment analysis, and hierarchy crossover detection. Use when: (1) evaluating brain-DNN alignment at the transformation level, (2) comparing neural representations across model architectures, (3) analyzing semantic vs low-level visual alignment, or (4) design...Votes: 0GitHub stars: 3
- Brain Foundation Biomarker ValidationRE-CONFIRM framework for validating robustness of biomarkers discovered by brain foundation models from dynamic functional connectivity. Systematic evaluation of internal reliability, external reliability, and validity for clinical biomarkers. Activation: RE-CONFIRM, biomarker validation, brain foundation model, robust biomarkers, dynamic functional connectivity.Votes: 0GitHub stars: 3
- Brain Foundation Model Batch EffectsAnalysis and mitigation of batch effects in fMRI foundation model embeddings. Activation: batch effects, fMRI foundation models, embedding quality.Votes: 0GitHub stars: 3
- Brain Foundation Model InversionBrain foundation model inversion methodology using Simulation-Based Inference (SBI) for stimulus reconstruction from synthetic neural activity. Enables reverse application of brain emulators like TRIBEv2 to recover stimuli or their properties from neural responses. Keywords: brain foundation model inversion, SBI, simulation-based inference, TRIBEv2, stimulus reconstruction, neural decoding, inverse problem neuroscience.Votes: 0GitHub stars: 3
- Brain Graph Augmentation Template基于群体模板的脑图数据增强方法，用于改进单样本学习分类。使用连接脑模板（CBT）和图生成对抗网络（gGAN）从单一群体模板生成增强数据，提升阿尔茨海默病等疾病分类性能。触发词：脑图增强、数据增强、单样本学习、连接脑模板、CBT、图GAN、Alzheimer分类、brain graph augmentation、one-shot learning、CBT、gGAN。Votes: 0GitHub stars: 3
- Brain Graph NeuralGraph Neural Network methods for brain connectivity analysis. Use when analyzing fMRI/EEG brain network data, modeling brain structure-function relationships, predicting cognitive outcomes from connectome data, or applying GNN to neuroscience problems. Keywords: brain graph, connectome GNN, neural network brain, fMRI GNN, brain connectivity analysis, 脑网络图神经网络, 脑连接性分析, 认知预测.Votes: 0GitHub stars: 3
- Brain Guided Llm Reasoning AlignmentBrain-guided language model framework for robust reasoning - using task-fMRI signals from reasoning regions to enhance LLM performance across 10 models with up to 13% accuracy gainVotes: 0GitHub stars: 3
- Brain Higher Order Structures脑网络高阶结构分析方法论。使用单纯复形和持续同调研究功能脑网络中的高阶交互（四节点以上），解释为何常规分析难以检测复杂高阶结构。触发词：高阶结构、持续同调、单纯复形、脑网络拓扑、higher-order interactions、persistent homology、simplicial complex。Votes: 0GitHub stars: 3
- Brain Inspired Attention MechanismsBrain-inspired attention mechanisms for neural networks - incorporating biological attention systems including thalamocortical circuits, pulvinar-mediated attention, basal forebrain modulation, and predictive processing. Implements biologically plausible attention for computer vision, NLP, and multi-modal AI. Activation: brain attention, thalamic attention, pulvinar, predictive attention, biological attention, neuromorphic attention, cortico-thalamic, saliency-based attention.Votes: 0GitHub stars: 3
- Brain Inspired Capture Evidence Driven Neuromimetic PerceptualBrain-Inspired Capture (BI-Cap) methodology for evidence-driven neuromimetic perceptual simulation. Models human perceptual processes for robust visual understanding. Activation: brain-inspired capture, neuromimetic perceptual, BI-Cap, evidence-driven perception.Votes: 0GitHub stars: 3
- Brain Inspired Capture Evidence DrivenBrain-Inspired Capture (BI-Cap) methodology for evidence-driven neuromimetic perceptual simulation in visual decoding. Trigger words: BI-Cap, neuromimetic, perceptual simulation, visual decoding, HVSVotes: 0GitHub stars: 3
- Brain Inspired Cellular Automata[TODO: Complete and informative explanation of what the skill does and when to use it. Include WHEN to use this skill - specific scenarios, file types, or tasks that trigger it.]Votes: 0GitHub stars: 3
- Brain Inspired Gating SnnBrain-inspired gating mechanism for Spiking Neural Networks that unlocks robust computation by incorporating dynamic conductance mechanisms. Addresses limitations of conventional LIF neurons that omit conductance dynamics inherent in biological neurons. Based on arXiv:2509.03281.Votes: 0GitHub stars: 3
- Brain Inspired Intelligence Paradigm类脑智能范式方法论。从神经科学视角重新思考智能形成与演化，提出Brain-like Neural Network (BNN)新范式，涵盖结构组织、学习机制、演化路径三大维度。触发词：类脑智能、BNN、神经科学智能、脑启发范式、brain-like neural network、intelligence paradigm、neuroscience AI。Votes: 0GitHub stars: 3