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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Cognition Inspired Dual Stream EmotionCognition-Inspired Dual-Stream Semantic Enhancement (DuSE) for Vision-Based Dynamic Emotion Modeling. Implements hierarchical temporal prompt clusters (HTPC) for cognitive priming and latent semantic emotion aggregators (LSEA) for knowledge integration. Models neuro-cognitive mechanisms from Conceptual Act Theory for dynamic facial expression recognition. Use for: emotion recognition, cognitive-inspired computer vision, neuro-cognitive modeling, dynamic facial expression analysis.Votes: 0GitHub stars: 3
- Cognitive Flexibility Task StructureNeural network model of attention to task structure enabling cognitive flexibility. Studies how neural networks learn to attend to relevant task dimensions and switch between task rules. Combines attention mechanisms with structured task representations to model cognitive control. Use when building neural models of cognitive flexibility, studying task switching in neural networks, implementing attention-based rule learning, or analyzing prefrontal cortex-like computation in artificial network...Votes: 0GitHub stars: 3
- Cognitive Load Multiscale AttractorsIntegrating Cognitive Load and Embodied Cognition Theories Through Representations as Multi-Scale Attractors — proposing a formal rapprochement between cognitive load theory and embodied cognition by reconceptualizing psychological representations as dynamic multiscale attractors within a temporal-hierarchical prediction architecture.Votes: 0GitHub stars: 3
- Coherence Law Noisy Equivariant Qnn TrainabilityCoherence law for trainability in noisy equivariant quantum neural networks. U(1)-equivariant QNNs with light-cone gradient confinement, sector coherence rate as Rayleigh quotient, and open-system training law. Use when designing symmetric QNNs for noisy hardware, analyzing gradient survival under decoherence, or building noise-resilient quantum neural architectures.Votes: 0GitHub stars: 3
- Cohort Amortized Personalization Brain Twins提出 **Cohort-Amortized Personalization (CAP)** 框架，解决个性化脑模型临床转化中的两大障碍： 1. **隐私壁垒**：个体神经影像数据难以共享，存在重新识别风险 2. **计算成本**：逐被试拟合需要数小时计算，限制多中心合作Votes: 0GitHub stars: 3
- Cold Atom Medical ImagingMedical imaging classification using cold-atom (neutral-atom) reservoir computing with auto-encoders and surrogate-driven training. Based on arXiv:2605 (2026). Use when building hybrid quantum-classical pipelines for medical image analysis, applying neutral-atom reservoir computing to classification tasks, or dimensionality reduction for medical imaging. Combines guided auto-encoder for feature compression with physical reservoir dynamics for classification. Activation: cold-atom reservoir co...Votes: 0GitHub stars: 3
- Cold Atom Reservoir ComputingHybrid quantum-classical machine learning using neutral-atom (cold-atom) reservoir computing for classification tasks, especially medical imaging. Covers the pipeline of guided auto-encoder dimensionality reduction, surrogate-driven training, and cold-atom reservoir state evolution. Use when: (1) implementing reservoir computing with quantum/neutral-atom systems, (2) building hybrid quantum-classical ML pipelines, (3) medical image classification with reservoir computing, (4) surrogate-gradie...Votes: 0GitHub stars: 3
- Combinatorial Complex Brain FmriThe Human Brain as a Combinatorial Complex - framework for constructing combinatorial complexes from fMRI time series that captures both pairwise and higher-order neural interactions through information-theoretic measures. Bridges topological deep learning and network neuroscience.Votes: 0GitHub stars: 3
- Common Synaptic Input Estimation HdemgPractical methodology for estimating common synaptic input to spinal motor neurons from high-density surface EMG motor unit spike trains using openhdemg. Three complementary approaches: time-domain, frequency-domain, and network-information methods.Votes: 0GitHub stars: 3
- Commuting Pauli ParallelizationMethodology for optimizing parallel execution of commuting Pauli Product Rotations in fault-tolerant quantum computation. Use when: (1) compiling quantum programs to Pauli Product Measurements (PPMs), (2) reducing circuit depth in surface code architectures with lattice surgery, (3) scheduling commuting quantum operations under hardware port constraints, (4) optimizing logical-layer quantum compilation. Keywords: Pauli Product Rotation, commuting groups, lattice surgery, surface code, circuit...Votes: 0GitHub stars: 3
- Compactionrl Context Compaction AgentsCompactionRL methodology for training long-horizon agentic LLMs with context compaction. Jointly optimizes task execution and summary generation with token-level loss normalization and cross-trajectory GAE.Votes: 0GitHub stars: 3
- Competition Stability Ei CircuitsGame-theoretic energetic framework for excitatory-inhibitory neural circuits - competition, stability, and functionality in asymmetric networksVotes: 0GitHub stars: 3
- Competitive Complementary ToolsMethodology for modeling the co-evolution of human competence and AI tool reliance as a bistable dynamical system. Analyzes competence collapse thresholds, transparency effects, and agency transfer in human-AI collaboration. Use when designing AI tools, studying human-AI interaction dynamics, or developing tool-resistant education strategies.Votes: 0GitHub stars: 3
- Compiler World Model Tensor OptimizationWorld-model-inspired evaluator for tensor program optimization. Models schedule evaluation as action-conditioned latent dynamics over program states.Votes: 0GitHub stars: 3
- Complex Berry Phase Quantum ControlComplex Berry phase measurement and control methodology for non-Hermitian quantum systems. Experimental measurement of real and imaginary Berry phase components using superconducting transmon circuits with engineered dissipation. Path-dependent effects enable non-unitary quantum control protocols.Votes: 0GitHub stars: 3
- Complex Brain HypothesisComplex Brain Hypothesis (CBH) methodology for resolving entropy-content conundrum in consciousness research. Extends Entropic Brain Hypothesis by introducing brain complexity as index of phenomenal richness, modulated by inference grain. Use when: studying consciousness, minimal phenomenal experiences, psychedelic states, entropy vs complexity in brain activity, Karl Friston free energy framework, computational theories of consciousness. Activation: complex brain hypothesis, entropy content ...Votes: 0GitHub stars: 3
- Complex Kuramoto ControlUnified control framework for synchronization in coupled oscillator networks using complex-valued Kuramoto extensions. Use when designing synchronization controllers, phase-locking mechanisms, oscillator network control, or when real-valued Kuramoto model fails. Keywords: Kuramoto, synchronization, complex-valued control, oscillator networks, phase locking, sliding-mode control.Votes: 0GitHub stars: 3
- Complex System Robustness CollapseComplex system robustness and collapse analysis - temporal structure, percolation methods, phase transitions, bistability, catastrophic collapse. Activation: system robustness, system collapse, complex network, phase transition, resilience analysis.Votes: 0GitHub stars: 3
- Complex Valued Gnn ControlComplex-Valued GNN Control - 复数值图神经网络用于分布式基不变控制系统。核心技术：复数域几何表示、相位等变激活、全局基不变性。适用于GPS拒绝环境分布式控制。激活词：complex GNN, 复数值 GNN, basis invariant, 基不变控制.Votes: 0GitHub stars: 3
- Complex Valued Kuramoto ControlComplex-valued Kuramoto network synchronization control using switched control and sliding-mode methods. Embeds phase dynamics into linear state space for tractable control design. Use for: coupled oscillator networks, phase synchronization, Kuramoto model control, complex systems synchronization.Votes: 0GitHub stars: 3
- Complex Valued Neuromorphic Magnitude PhaseComplex-valued neural network with magnitude-phase decomposition for event-driven neuromorphic learning, enabling efficient spiking computation with rich representational capacityVotes: 0GitHub stars: 3
- Complexity Stability Neural Activity Aging DiseaseDistribution-level framework for understanding neural stability across cognition, aging, and neurodegenerative disease using Wasserstein distance for temporal stability and intrinsic dimensionality for representational complexity. Analyzes EEG as distributions of windowed activity patterns to quantify condition-specific stability and complexity collapse in Alzheimer's disease. Use when analyzing neural representational stability, tracking cognitive aging, or developing biomarkers for neurodeg...Votes: 0GitHub stars: 3
- Composite Quantum Gates Error CancellationComposite quantum pulse gates that simultaneously compensate multiple systematic errors via derivative-based error cancellation. Covers derivative removal by adiabatic gate (DRAG)-extended pulse shaping, multi-error compensating composite sequences, Wimperis/Trotter-Suzuki derived composite pulses, and numerical optimization of gate fidelity under simultaneous amplitude, frequency, and timing errors. Use when: designing robust quantum gates, compensating pulse errors, building composite seque...Votes: 0GitHub stars: 3
- Compositional Quantum HeuristicsCompositional quantum heuristics for mitigating barren plateaus in quantum machine learning. Assembles larger quantum models from smaller subcomponents with group-invariant loss functions introducing symmetry-induced inductive bias for improved gradient behavior. Use when: barren plateau mitigation, quantum graph neural networks, permutation-equivariant quantum models, recursive quantum-classical hybrid optimization, QIRO-inspired quantum heuristics, max-clique quantum detection, group-invari...Votes: 0GitHub stars: 3
- Compress Distill Reasoning Trace CompressionPost-hoc compression of reasoning chain-of-thought traces before knowledge distillation for efficient training and inferenceVotes: 0GitHub stars: 3
- Compressed Minimum Purity Time EvolutionCoMPuTE method for late-time quantum dynamics simulation using compressed minimum-purity time evolution. Tracks reduced local density matrices via minimum-purity principle for efficient long-time quantum many-body simulations.Votes: 0GitHub stars: 3
- Computation Aware Kalman Neural DynamicsComputation-Aware Kalman Filtering with Model Selection for Neural Dynamics - solving scale-imbalanced neural data analysisVotes: 0GitHub stars: 3
- Computational Affordance Landscape Brain NetworksComputational Affordance Landscape framework for quantifying neural network computation costs using control theory. Use when analyzing brain network structure-function relationships, neural circuit computations, or applying control theory to quantify computational costs in biological and artificial neural networks.Votes: 0GitHub stars: 3
- Computational Linguistics Brain PerspectiveComputational neuroscience perspective on linguistics and human brain relationship. Bridging theoretical linguistics with empirical neural data using formal computational models. Triggers: linguistics, brain, computational neuroscience, language, cognitive science, neural modeling.Votes: 0GitHub stars: 3
- Computational Neuroscience In Llm EraComputational neuroscience methodology enhanced by large language models. Covers LLM-based neural data analysis, brain signal interpretation, computational modeling with AI, and neuro-symbolic approaches. Use when working with LLMs in neuroscience, neural data analysis with language models, brain-computer interface with LLMs, or AI-enhanced neuroscience research.Votes: 0GitHub stars: 3
- Confidence Dynamics Early Stop早停策略技能 - 利用中间答案的置信度动态来决定何时终止推理，适用于大推理模型的长链式思维生成。基于论文 Early Stopping for Large Reasoning Models via Confidence Dynamics (arXiv 2604.04930)。激活关键词: 早停, early stop, confidence dynamics, reasoning stop, 推理终止, overthinking prevention, 防止过度思考。Votes: 0GitHub stars: 3
- Confirmation Bias Quantum ProbabilityQuantum probability framework modeling confirmation bias as optimal evidence selection in sequential hypothesis testing. Use when analyzing confirmation bias, sequential evidence sampling, active inference, quantum probability models of cognition, binary hypothesis testing, or rational decision-making under uncertainty.Votes: 0GitHub stars: 3
- Congestion Aware Axonal Delay SnnCongestion-Aware Dynamic Axonal Delay for Spiking Neural Networks. Replaces static per-synapse delays with input-dependent dynamic delays that adapt to network activity patterns, reducing delay parameters while improving temporal task performance. Activation: congestion-aware delay, dynamic axonal delay SNN, input-dependent delay, SNN temporal processing, adaptive delay learning.Votes: 0GitHub stars: 3
- Congestion Aware Delay SnnCongestion-Aware Dynamic Axonal Delay mechanism for Spiking Neural Networks. Decomposes delay into channel-wise static base delay + global activity-conditioned shift. Reduces delay parameters by ~50% while improving accuracy on temporal tasks. Source: arXiv:2605.01291 (Bai et al., May 2026).Votes: 0GitHub stars: 3
- Conjugacy Based Similarity AnalysisConjugacy-based Similarity Analysis (CSA) methodology for comparing dynamical systems in neuroscience and ML. Addresses limitations of Dynamical Similarity Analysis (DSA) by restricting alignments to state-space bijections rather than arbitrary orthogonal matrices.Votes: 0GitHub stars: 3
- Connectivity Distributions Neural PopulationsIdentifying neural connectivity distributions from population recordings using low-rank recurrent neural networks (lrRNNs). Addresses the degeneracy problem where multiple connectivity structures generate identical dynamics. Provides mechanistic interpretation of neural dynamics through inferred connectivity. Applicable to neural population analysis, connectivity inference, circuit architecture mapping. Trigger: connectivity inference neural populations, lrRNN connectivity, neural dynamics de...Votes: 0GitHub stars: 3
- Connectome Constrained Neural NetworkConnectome-Constrained Neural Network (CCNN) methodology for brain-inspired AI. Integrates biological structural connectivity (connectome) into artificial neural network architectures to improve generalization and biological plausibility. Activation: connectome constraint, structural connectivity, brain-inspired architecture, connectome-based AI, wiring cost, brain network prior, diffusion MRI connectivity.Votes: 0GitHub stars: 3
- Connectome Genetic Environmental ArchitectureMethodology for decomposing functional connectome variance into genetic and environmental components using extended ACE/ADE twin models with explicit measurement error modeling. Reveals hierarchical community structure in genetic and environmental influences.Votes: 0GitHub stars: 3
- Connectome Wiring Statistical Dynamics SeparationSeparating wiring-specific from statistical control of dynamics in a complete connectome. Analysis of larval Drosophila brain showing coarse statistics set dynamical regime while specific wiring determines activity routing.Votes: 0GitHub stars: 3
- Connectome Wiring Statistics Control DynamicsSeparating wiring-specific from statistical control of dynamics in complete connectomes - clarifying which connectome-based claims rest on wiring aloneVotes: 0GitHub stars: 3
- Consciousness Intrinsic Structure Chemistry ExperienceIIT framework for consciousness as intrinsic structure.Votes: 0GitHub stars: 3
- Consciousness Usk Framework意识作为罕见自我知识（USK）框架。基于部分信息分解（PID）的意识理论框架，将意识定义为系统对自身携带的协同信息，仅在子系统联合中存在且被分解破坏。适用于意识研究、信息论、脑网络分析、LLM对齐评估。触发词：consciousness, USK, synergistic information, Partial Information Decomposition, IIT, GWT, HOT, 意识, PIRDVotes: 0GitHub stars: 3
- Conservative Adaptive Rank Quantum KineticsConservative adaptive rank methodology for quantum kinetic simulations — ACA SVD with Fermi-Dirac reconstruction preserving discrete macroscopic invariants near machine precision.Votes: 0GitHub stars: 3
- Conservative Discrete Abstractions CpsCPS discrete abstractions with sound verification.Votes: 0GitHub stars: 3
- Conserved Kinematic Bci Zeroshot零样本手写脑机接口解码方法。研究运动皮层是否通过共享运动学基元的组合来表示手写动作，提出基于运动学预测和模板匹配的两阶段零手写字母解码框架。适用于BCI解码、运动皮层表征、零样本学习、iBCI。触发词：零手写BCI、运动学表征、手写解码、运动原语、运动皮层组合编码、BCI recalibration、kinematics prediction, zero-shot BCI, handwriting BCI, motor cortex representationVotes: 0GitHub stars: 3
- Conserved Kinematic Zero Shot BciConserved Kinematic Representations for Zero-Shot Decoding in Handwriting BCIs. Use when: researching brain-computer interfaces for handwriting decoding, zero-shot neural decoding, conserved kinematic primitives, motor cortex representation, intracortical BCI for logographic languages (Chinese, Japanese), compositional motor control, or cross-character generalization in neural decoding. Keywords: kinematic BCI, zero-shot neural decoding, handwriting iBCI, conserved motor representations, comp...Votes: 0GitHub stars: 3
- Constraint Preserving Quantum MixersConstraint-preserving quantum mixer patterns for combinatorial optimization on NISQ hardware. Covers XY-mixer vs Pauli-X mixer selection criteria, Trotterized Adiabatic Evolution (TAE), compressed AQC-QAOA initialization, and QUBO formulation strategies. Use when designing quantum optimization circuits for constrained problems (VRP, TSP, portfolio optimization, network security), choosing mixer Hamiltonians, or encoding combinatorial problems for quantum solvers (QAOA, adiabatic evolution, va...Votes: 0GitHub stars: 3
- Constraint Relax Then Tighten约束先放后紧的方法论。核心思想：先放宽约束条件证明解的存在性，然后逐步收紧约束，逼近原始问题。适用于复杂约束系统的控制和规划问题。触发词：约束放松、约束收紧、feasibility、存在性证明、约束控制、constraint relaxation、progressive tightening、约束规划。Votes: 0GitHub stars: 3
- Context Reconfiguration Sparse TemporalMechanistic analysis of joint sparse coding and temporal dynamics as the neural basis for context reconfiguration. Combines mouse mPFC recordings with computational network analysis to show how sparsity reduces cross-context interference while temporal dynamics enhance context separability. Establishes SNNs as naturally endowed with both properties, enabling lifelong learning retention without auxiliary heuristics. Energy-efficient architectural principle for stable adaptation. Activation tri...Votes: 0GitHub stars: 3
- Context Selective Multimodal MemoryHuman-inspired context-selective multimodal memory architecture for social robots. Combines hippocampal-inspired memory consolidation with context-dependent retrieval across visual, auditory, and textual modalities. Use when building embodied AI agents, social robots, or any system needing human-like context-aware multimodal memory. Activation: context-selective memory, multimodal memory, social robot memory, hippocampal-inspired memory, embodied AI memory, context-aware retrieval.Votes: 0GitHub stars: 3