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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Heterogeneous Contract ControlHeterogeneous assume-guarantee contract framework for co-design of layered control architectures. Decomposes safety-liveness specifications across discrete-time planning (MPC) and continuous-time safety layers using vertical refinement, timing compatibility, and explicit reference governors. Use when designing hierarchical control systems, layered control architectures (LCAs), assume-guarantee contracts for CPS, safety-liveness decomposition, MPC + low-level controller integration, reference ...Votes: 0GitHub stars: 3
- Heterogeneous Stochastic Momentum Admm分布式非凸复合优化的异构随机动量ADMM方法。通过节点自适应步长策略解耦算法稳定性与全局网络属性，实现任意连接拓扑下的鲁棒收敛。适用于分布式机器学习、联邦学习、网络优化。触发词：分布式优化、ADMM、随机优化、非凸优化、动量加速、异构网络、distributed optimization、stochastic ADMM、heterogeneous network。Votes: 0GitHub stars: 3
- Heterogeneous Synaptic Dynamics异质突触动力学建模方法论。基于现象学建模框架，整合连接性、突触传输、突触可塑性和突触异质性四个关键维度。适用于大规模脑网络模拟、突触模型实现、计算神经科学研究。触发词：突触动力学、异质性建模、突触可塑性、突触传输、计算神经科学、synaptic dynamics, heterogeneous synapses, computational neuroscience。Votes: 0GitHub stars: 3
- Heterogeneous Synaptic Motifs Macroscale Dynamics提出首个将突触分辨率连接组学（synaptic-resolution connectomics）与宏观异质性群体动力学联系起来的数学框架，揭示了微观突触结构如何通过二阶突触基序（second-order synaptic motifs）贡献于宏观计算。Votes: 0GitHub stars: 3
- Heterophily Synergistic InterdependenciesHeterophily as a generative mechanism for self-organized synergistic interdependencies in adaptive networks. Explains how heterophily induces higher-order dependencies while weakening pairwise dependencies, enabling robust collective behavior. Trigger words: heterophily, synergistic interdependencies, adaptive networks, higher-order dependencies, self-organization, network dynamics, collective behavior.Votes: 0GitHub stars: 3
- Hh Model Inference From MeaFramework for rapidly inferring Hodgkin-Huxley (HH) biophysical parameters from extracellular multi-electrode array (MEA) measurements using differentiable biophysical simulation and simulation-based inference. Enables precise neurostimulation prediction without invasive intracellular recordings. Validated on macaque retina with 512-electrode array achieving 90.6% accuracy predicting unseen multi-electrode stimulation responses. Use when: fitting HH models from extracellular data, predicting ...Votes: 0GitHub stars: 3
- Hidden Progress Overtraining Sensory CortexHidden progress during overtraining in sensory cortex networks — discovering that overtrained neural networks continue learning useful representations even after apparent performance plateau, with implications for neuroscience and deep learning theory. Activation: overtraining, sensory cortex, hidden progress, learning plateau, groove, deep learning theory, representational change, network training dynamics.Votes: 0GitHub stars: 3
- Hierarchical Brain CriticalityHierarchical organization of critical brain dynamics. Studies how criticality signatures vary along anatomical hierarchy in brain systems using phenomenological renormalization group approaches on large-scale neuronal spiking data.Votes: 0GitHub stars: 3
- Hierarchical Connectome PhcParallelized Hierarchical Connectome (PHC) framework for spatiotemporal recurrent spiking neural networks. Upgrades State-Space Models (SSMs) into spatiotemporal networks with biological constraints including Dale's Law, short-term plasticity, and reward-modulated STDP. Activation: spiking neural networks, SSM, connectome, spatiotemporal modeling, biological neural networks.Votes: 0GitHub stars: 3
- Hierarchical Connectome SsmParallelized Hierarchical Connectome (PHC) framework that upgrades temporal State-Space Models into spatiotemporal recurrent networks for brain connectivity modeling.. Activation: hierarchical connectome, state-space models, spatiotemporal.Votes: 0GitHub stars: 3
- Hierarchical Control AbstractionHierarchical control design via approximate simulation relations (ε-gAAS). Enables abstraction-based controller synthesis for continuous-time nonlinear systems with formal error bounds and control refinement guarantees. Use when: (1) designing controllers for complex continuous-time systems via model abstraction, (2) needing formal guarantees that abstract controllers transfer to concrete systems, (3) working with simulation relations for control refinement, (4) building hierarchical control ...Votes: 0GitHub stars: 3
- Hierarchical Control GaasHierarchical control synthesis for continuous-time systems using epsilon-general Approximate Alternating Simulation (epsilon-gAAS) relations. Enables formal controller design with coarser abstractions while maintaining correctness guarantees. Use when: (1) designing hierarchical controllers for continuous-time systems, (2) building formal abstractions for complex dynamical systems, (3) synthesizing safety-critical controllers with correctness guarantees, (4) applying simulation relations for ...Votes: 0GitHub stars: 3
- Hierarchical Critical Brain DynamicsHierarchical organization of critical brain dynamics. Analysis of how brain structure hierarchies interact with criticality hypothesis. Activation: hierarchical brain, critical dynamics, connectome hierarchy, brain criticality.Votes: 0GitHub stars: 3
- High Spin Cat Codes Fault ToleranceMethodology for implementing fault-tolerant quantum computation using high-spin cat codes with universal phase-error-transparent gates. Constructs error-transparent logical gate sets that preserve correctability of phase errors during operations, addressing key challenges like logical X gate implementation and multi-tone microwave driving for CZ gates.Votes: 0GitHub stars: 3
- Higher Order Brain NetworksHigher-order brain network analysis using topological signal processing. Captures circulatory and multi-node interactions beyond pairwise graph models.. Activation: higher-order networks, topological signal processing, brain connectomics.Votes: 0GitHub stars: 3
- Higher Order Functional Brain Networks Global ConstraintsMethodology for extracting high-order functional brain network structures beyond pairwise connections under global constraints. Addresses theoretical limitations of pairwise FBN modeling. Activation: higher-order brain networks, beyond pairwise, global constraints, FBN limitations.Votes: 0GitHub stars: 3
- Higher Order Topological Ad AlzheimerExtracting interpretable higher-order topological features across multiple scales for Alzheimer's Disease classification using persistent homology. Captures connected components, cycles, and cavities from fMRI brain networks. Activation: higher-order topology, Alzheimer classification, persistent homology, brain network topology, topological features.Votes: 0GitHub stars: 3
- Hippo Multi Attractor MemoryBiologically detailed extension of Hopfield/Marr auto-associative memory model for CA3 hippocampus. Implements ten populations (two asymmetric pyramidal subtypes, eight GABAergic interneurons) to study multi-attractor dynamics and stability effects in memory circuits.Votes: 0GitHub stars: 3
- Hippocampal Data Augmentation GeneralizationData augmentation framework for modeling hippocampal contributions to generalization across offline and online timescales. Use when implementing hippocampal-inspired AI systems or studying neural mechanisms of flexible repurposing of prior experiences.Votes: 0GitHub stars: 3
- Hippocampal Entorhinal World ModelBrain-inspired hierarchical world model using hippocampal-entorhinal (HPC-MEC) circuit for structure abstraction and generalization. Inverse model for structural extraction, HPC-MEC coupling for dissociating relational structures from episodic scenes. Based on Zhang et al. (arXiv: 2605.15733). Use when designing brain-inspired world models, building self-supervised learning systems with structural generalization, modeling hippocampal-entorhinal computation, or developing abstraction mechanism...Votes: 0GitHub stars: 3
- Holobrain Holograph Oscillatory GnnHoloBrain and HoloGraph framework: modeling brain rhythms through coupled oscillatory synchronization and applying this principle to graph neural networks. Addresses GNN over-smoothing and enables reasoning on graphs through oscillatory dynamics.Votes: 0GitHub stars: 3
- Holonic Digital Twins Physical Ai NetworksHDT-Nets framework for Physical AI over Networks.Votes: 0GitHub stars: 3
- Homology Brain AtrophyHomology-based morphometry methods for analyzing brain atrophy using topological data analysis. Activation triggers: homology morphometry, brain atrophy, topological neuroimaging, persistent homology brain, TDA neuroimagingVotes: 0GitHub stars: 3
- Homology Morphometry Brain AtrophyHomology-based Morphometry (HBM) methodology for analyzing brain atrophy using persistent homology. Two complementary pipelines for quantifying multiscale geometric features of structural T1-weighted MRI scans: Pipeline 1 for regional thinning via Euclidean distance transform, Pipeline 2 for structural similarity via α-filtrations. Use for Alzheimer's disease detection, longitudinal brain change tracking, and topological biomarker extraction. Keywords: brain atrophy, persistent homology, TDA,...Votes: 0GitHub stars: 3
- Hopfield Networks Dreaming TheoryStatistical-mechanical theory of dreaming in multidirectional associative memories using DLAM architecture. Use when: (1) implementing energy-based models with dreaming capabilities; (2) designing multi-layer Hebbian architectures; (3) analyzing pattern disentanglement in neural networks; (4) studying statistical mechanics of neural memory; (5) developing heteroassociative memory systems. Trigger words: Hopfield dreaming, DLAM, associative memory, energy-based models, pattern disentanglement.Votes: 0GitHub stars: 3
- Hormone T5 Emotion LayerHormone-inspired Emotion Layer for Transformer language models (HELT / HormoneT5). Biologically-inspired architecture augmenting transformers with a Hormone Emotion Block simulating the endocrine system's role in emotional processing. Six continuous hormone-like values computed via specialized per-hormone attention heads with orthogonally initialized learnable queries, temperature-scaled attention, and deep output projections. Emotional embedding modulates encoder hidden states for emotionall...Votes: 0GitHub stars: 3
- Hot Start Quantum Portfolio OptimizationHot-starting methodology for quantum portfolio optimization using continuous relaxation to construct compact Hilbert space, reducing qubit requirements for QUBO formulations. Based on arXiv:2510.11153v1.Votes: 0GitHub stars: 3
- Hotstart Quantum Portfolio OptimizationHot-starting methodology for quantum portfolio optimization — restricting search space to discrete solutions near the continuous optimum by constructing a compact Hilbert space, reducing qubit requirements.Votes: 0GitHub stars: 3
- How Canada Uses Claude Findings From The Anthropic Economic IndexHow Canada uses Claude: Findings from the Anthropic Economic IndexVotes: 0GitHub stars: 3
- How We Monitor Internal Coding Agents MisalignmentChain-of-thought monitoring methodology for detecting misalignment in coding agents. GPT-5.4 Thinking monitors 99.9% of internal coding agent traffic. Defense-in-depth approach with layered controls. Use when implementing agent safety systems or building production monitoring.Votes: 0GitHub stars: 3
- Hqnn Blood Cell ClassificationHybrid Quantum-Classical Neural Network (HQNN) methodology for medical image classification, specifically blood cell classification. Combines pre-trained classical backbone (ResNet-50) with variational quantum circuit for enhanced feature representation. Use when: (1) medical image classification with limited data, (2) hybrid quantum-classical ML pipeline design, (3) comparing quantum vs classical feature transformations, (4) NISQ-era quantum advantage in medical imaging. Activation: HQNN, hy...Votes: 0GitHub stars: 3
- Hqnn Breast Cancer ThermographicHybrid Quantum Neural Network (HQNN) architecture for breast cancer thermographic classification. Integrates parameterized quantum circuits with multi-head attention and classical CNN layers for enhanced medical thermal pattern recognition.Votes: 0GitHub stars: 3
- Hqnn Expressibility TrainabilityExpressibility-trainability trade-off analysis and multi-objective NAS framework for Hybrid Quantum Neural Networks (HQNNs) — reveals how classical components reshape quantum optimization landscapes and decouple trainability from PQC expressibility.Votes: 0GitHub stars: 3
- Hubo Quantum OptimizationHigher-Order Unconstrained Binary Optimization (HUBO) methodology for quantum optimization workflows. Compact binary encoding reduces qubit requirements vs QUBO but increases circuit depth via higher-order interaction terms. Use when formulating industrial logistics, scheduling, routing, or portfolio optimization problems for quantum/hybrid quantum-classical solvers, or when analyzing qubit-vs-depth trade-offs in HUBO vs QUBO encodings. (arXiv: 2605.30252)Votes: 0GitHub stars: 3
- Human Like Object GroupingBehavioral benchmark and object-centricity analysis for self-supervised vision transformers. Uses two-dot same/different judgment task with 1020+ trials to measure human object grouping, and proposes Gram matrix alignment as a mechanism for improving behavioral alignment. Use when: vision transformer evaluation, object segmentation, self-supervised learning, DINO models, behavioral neuroscience benchmarks, Gram matrix distillation, human-AI visual alignment, psychophysics.Votes: 0GitHub stars: 3
- Hybrid Ann Snn Local PlasticityHybrid ANN-SNN pipeline with local plasticity: couples pretrained ANN encoders with spiking classifiers using biologically-inspired local learning rules, bypassing end-to-backprop. Use when building energy-efficient spiking neural networks from pretrained models, implementing local Hebbian/plasticity rules, or converting ANN features to spike trains.Votes: 0GitHub stars: 3
- Hybrid Ann Snn Pipeline Local PlasticityNovel hybrid architecture combining pretrained ANN encoder (EfficientNet) with CoLaNET spiking classifier, achieving **99.09% accuracy** on 64-class ImageNet using biologically inspired **local learning rules** without end-to-end gradient propagation. Demonstrates first successful adaptation of powerful pretrained encoders to downstream SNN tasks.Votes: 0GitHub stars: 3
- Hybrid Biophysical Neuron Models Neural OdesLearning Hybrid Biophysical Neuron Models with Neural ODEs — combining mechanistic biophysical models with machine learning for accurate and efficient neuron dynamics modelingVotes: 0GitHub stars: 3
- Hybrid Biophysical Neuron Neural OdeHybrid biophysical neuron modeling methodology combining conductance-based models with neural ODEs. Captures unknown ion channel kinetics while preserving mechanistic interpretability. Enables single-compartment reduction of multi-compartment models.Votes: 0GitHub stars: 3
- Hybrid Intelligent Mental Health AssessmentMulti-dimensional mental health assessment using hybrid intelligent frameworks combining clinically validated screening tools, cognitive evaluation, and personality profiling with AI-driven decision support.Votes: 0GitHub stars: 3
- Hybrid Pqc Pseudonym Vehicular SecurityHybrid certificate methodology combining ECC with Post-Quantum Cryptography (PQC) for vehicular communication security. Covers SCMS pseudonym schemes, BKE compatibility, NIST-standardized PQC algorithms, and performance evaluation of RSA/ECC/PQC for vehicular credential management. Activation: vehicular security PQC, SCMS hybrid certificate, pseudonym scheme quantum-safe, BKE post-quantum, vehicular communication security, NIST PQC vehicular, 车载通信后量子密码, 混合证书车联安全Votes: 0GitHub stars: 3
- Hybrid Qcnn Medical DiagnosticsHybrid classical-quantum diagnostic framework using QCNNs for multi-class medical image classificationVotes: 0GitHub stars: 3
- Hybrid Qml Pipeline DesignDesign and evaluate hybrid quantum-classical machine learning pipelines. Covers NISQ-era variational quantum algorithms (VQAs), noise-aware pipeline design, correlation-guided quantum circuit construction, and classical-quantum benchmarking frameworks. Use when: designing QML systems, evaluating quantum vs classical ML tradeoffs, building noise-robust quantum pipelines, optimizing variational quantum circuits, implementing quantum feature maps, or comparing hybrid vs pure classical approaches...Votes: 0GitHub stars: 3
- Hybrid Quantum Classical ArchitectureDesign and optimization of hybrid quantum-classical computing system architectures. Includes dataflow frameworks, fault-tolerant design, resource efficiency optimization, automated architecture search, and quantum ML application patterns. Use when: (1) designing quantum computing systems, (2) optimizing hybrid quantum-classical architectures, (3) implementing fault-tolerant quantum systems, (4) searching for optimal quantum architectures, (5) building hybrid quantum ML pipelines for medical/f...Votes: 0GitHub stars: 3
- Hybrid Quantum Classical FrameworkDesign dataflow-based hybrid quantum-classical computing architectures. Combine remote quantum computers with cloud/distributed systems. Activation: hybrid quantum classical, quantum classical hybrid, 混合量子经典, dataflow quantum, quantum cloud computing.Votes: 0GitHub stars: 3
- Hybrid Quantum Classical PinnHybrid quantum-classical physics-informed neural network (HQPINN) methodology for solving nonlinear PDEs. Integrates classical NN backbone with parameterized quantum circuits (PQC) to enrich solution representation for problems with sharp gradients, stiff dynamics, and multiscale structure.Votes: 0GitHub stars: 3
- Hybrid Quantum Classical ReservoirsHybrid quantum-classical reservoir computing (HRC) combining qubit quantum reservoir with classical echo state network for nonlinear functional approximation and temporal processing of quantum states. Outperforms standalone components in both linear and nonlinear tasks. Use when: hybrid quantum-classical ML, quantum state temporal processing, echo state networks, ESN, near-term qubit reservoir computing, measurement back-action, purity estimation.Votes: 0GitHub stars: 3
- Hybrid Quantum Classical System Design[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
- Hybrid Quantum Classical SystemsHybrid quantum-classical systems engineering skill for designing distributed quantum computing architectures, quantum error correction, and optimization workflows. Activates when discussing quantum-classical hybrid algorithms, distributed quantum systems, quantum error correction, or quantum system optimization.Votes: 0GitHub stars: 3
- Hybrid Quantum FbpinnHybrid quantum-classical FBPINN methodology for wave-based inverse problems. Uses parameterized quantum circuits (PQCs) as differentiable JAX statevector simulators in domain-decomposed physics-informed neural networks. Achieves 8x faster convergence with 33% fewer parameters. Activation: hybrid quantum-classical neural networks, physics-informed neural networks, full waveform inversion, quantum machine learning for PDEs, differentiable quantum circuits, JAX quantum simulation, wave-based inv...Votes: 0GitHub stars: 3