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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Domain Informed Moeeg Channel Selection BciMulti-objective optimization framework combining spatial relevance and functional discriminability for EEG channel selection in motor imagery BCIsVotes: 0GitHub stars: 3
- Domain Saturation ValidationComplete validation workflow for automated research when domain saturation is detected (>80% paper coverage). Verifies synchronization across local skills, repository, INDEX.md, knowledge graph, and Obsidian notes. Activation: domain saturation, research validation, sync verification, complete validationVotes: 0GitHub stars: 3
- Doob Barrier Noise ConsolidationDoob-barrier-conditioned diffusion methodology that turns analog device noise into a continual-learning resource for neuromorphic hardware. Uses Doob h-transform to condition synaptic weight dynamics on never crossing memory-critical barriers, creating a noise-amplified restoring force. Based on arXiv:2607.06924v1 (Howe, 2026).Votes: 0GitHub stars: 3
- Dopsd Diffusion Llm Self DistillationdOPSD methodology for on-policy self-distillation in diffusion language models. Derives teacher privilege from student's own denoising trajectory rather than external labels, enabling reasoning improvement without tractable sequence likelihoods.Votes: 0GitHub stars: 3
- Dose Efficient Quantum InterferometryDose-efficient quantum phase estimation methodology using sequential strategies in lossy optical interferometry for biological and medical imaging. Control-enhanced sequential strategies achieve superior quantum Fisher information per dose, approaching the quantum limit in dose-limited regimes.Votes: 0GitHub stars: 3
- Dre Dynamic Rollout EditingDynamic Rollout Editing (DRE) for reducing overthinking in RL-trained reasoning models. Training-time intervention that preserves verified prefixes and edits unnecessary continuation. Use when: (1) GRPO/RLVR training shows overthinking, (2) credit assignment fails for successful trajectories, (3) models continue reasoning after correct answer emergence. Activation: overthinking, GRPO, rollout editing, credit assignment, RL post-training.Votes: 0GitHub stars: 3
- Dream2learn Structured Generative DreamingDream2Learn (D2L) framework for continual learning using structured generative dreaming to create novel synthetic experiences from internal representations. Use when: (1) implementing continual learning systems; (2) addressing catastrophic forgetting; (3) generating synthetic training data; (4) expanding representation space through internal simulation; (5) achieving positive forward transfer in sequential tasks. Trigger words: Dream2Learn, D2L, structured dreaming, generative dreaming, conti...Votes: 0GitHub stars: 3
- Dreaming World Action Models梦境推理与世界行动模型：将梦境的认知重组机制应用于多模态推理，实现适应性推理策略切换。核心：Dreaming-when-Necessary机制、多模态推理适应性、长程任务规划。触发词：梦境推理、世界模型、行动规划、dreaming、多模态推理、adaptive reasoning、长程任务。Votes: 0GitHub stars: 3
- Driada Cross Scale Neural AnalysisDRIADA: Open-source Python toolkit for cross-scale analysis of single-neuron selectivity and population dynamics. Unifies neural signals and behavior in shared data model for selectivity testing, dimensionality reduction, and network analysis. Activation: DRIADA toolkit, cross-scale neural analysis, single-neuron selectivity, population dynamics, hippocampal calcium imaging, neural coding toolkit, information-theoretic selectivity.Votes: 0GitHub stars: 3
- Drl Gnn Brain Network深度强化学习引导图神经网络脑网络分析方法论。结合DRL和GNN进行脑网络分析。适用于脑网络分类、疾病诊断。触发词：深度强化学习、图神经网络、脑网络、DRL-GNN。Votes: 0GitHub stars: 3
- Drl Quantum Optimal ControlDeep reinforcement learning for quantum optimal control. Combines DRL with quantum gate synthesis to achieve high-fidelity, high-speed quantum operations without prior heuristic ansatz. Use when: (1) Designing quantum optimal control protocols, (2) Applying DRL to quantum gate synthesis, (3) Implementing incremental-update learning policies, (4) Optimizing Rydberg gate operations in neutral-atom quantum computers, (5) Multi-parameter pulse modulation for quantum control. Trigger: DRL quantum ...Votes: 0GitHub stars: 3
- Drl Sc3 Closed Loop Control[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
- Drpo Llm RlDivergence Regularized Policy Optimization (DRPO) — smooth advantage-weighted quadratic regularizer replacing hard trust-region masks in LLM reinforcement learning. Use when optimizing LLM post-training with RL, improving upon GRPO/PPO/DPPO stability.Votes: 0GitHub stars: 3
- Dual Axis Zebrafish Circuits斑马鱼被盖微电路双轴归因方法论。基于斑马鱼视网膜被盖微电路的双轴功能归因（能量高效信息处理和鲁棒稳定）方法，将生物学电路组织转化为类脑神经网络架构设计。适用于脉冲神经网络、类脑架构设计、电路归因。触发词：dual-axis, 双轴归因, zebrafish tectal, microcircuit attribution, energy efficiency, robustness, ns_TIN, superficial_TINVotes: 0GitHub stars: 3
- Dual Envelope Mpc Drifting ControlDual-envelope constrained nonlinear MPC for autonomous drifting in distributed drive electric vehicles. Covers saddle point modeling, phase plane envelope construction, NMPC with envelope constraints, and extreme vehicle maneuver control. Use when working with: (1) vehicle dynamics control, (2) nonlinear MPC, (3) autonomous drifting, (4) distributed drive systems, (5) envelope-based safety constraints, or (6) yaw moment control for extreme maneuvers.Votes: 0GitHub stars: 3
- Dual Envelope Mpc Vehicle DriftDual-envelope constrained nonlinear MPC for autonomous vehicle drifting control. Methods for constructing stability envelopes in phase plane, model predictive control with envelope constraints, and handling bounded steering/yaw-moment control for distributed drive EVs. Triggers: vehicle drifting control, MPC envelope constraint, autonomous drifting, distributed drive EV, yaw moment control, saddle point stability, phase plane envelope.Votes: 0GitHub stars: 3
- Dual Envelope MpcNonlinear Model Predictive Control with dual-envelope constraints for distributed drive systems. Use when: vehicle dynamics control, MPC implementation, envelope-based stability analysis, autonomous drifting, distributed drive electric vehicles, phase plane analysis, saddle point modeling, or constrained nonlinear control systems.Votes: 0GitHub stars: 3
- Dual Memory Pathway SnnDual Memory Pathway (DMP) neuromorphic network co-design methodology. Inspired by cortical fast-slow organization, combines explicit slow memory with fast spiking activity for long-timescale context. Applies to: neuromorphic computing, event-driven sensing, energy-efficient SNN deployment. Activation: dual memory pathway, neuromorphic co-design, fast-slow SNN, near-memory compute, cortical memory.Votes: 0GitHub stars: 3
- Dual Node Dgx Spark Distributed Llm TrainingDual-node DGX Spark LLM training over Tailscale.Votes: 0GitHub stars: 3
- Dual Pathway Robust Learning ControlDual-pathway architecture for provably robust learning-based control combining neural network feedforward estimation with conventional feedback correction. Use when designing learning-enabled control systems requiring both performance and robustness guarantees, particularly for robotics, aerospace, quantum control, or any system with out-of-distribution deployment concerns.Votes: 0GitHub stars: 3
- Dual Timescale Memory AstrocyteDual-timescale memory mechanism in spiking neuron-astrocyte networks. Astrocytes provide slow-timescale modulation complementing fast spiking dynamics, enabling energy-efficient learning of environmental patterns and persistent memory traces.Votes: 0GitHub stars: 3
- Dual Timescale Memory Spiking Neuron Astrocyte Network EfficientSpiking Neuron-Astrocyte Network (SNAN) combining STDP-based long-term memory with astrocytic calcium-mediated short-term suppression for efficient navigation. Introduces Topological-Context Memory as a dual-timescale working memory mechanism that reduces median path length by up to 6x and dramatically improves goal completion rates.Votes: 0GitHub stars: 3
- Dual Timescale Memory Spiking Neuron AstrocyteDual-timescale memory mechanism in spiking neuron-astrocyte networks (SNAN). Combines STDP-based long-term memory with astrocytic short-term suppression for efficient navigation and working memory. Activation: dual timescale memory, SNAN, spiking neuron astrocyte network, astrocyte working memory, topological context memory, neuromorphic navigation.Votes: 0GitHub stars: 3
- Dual Timescale Neuron Astrocyte MemoryDual-timescale memory mechanism in spiking neuron-astrocyte networks for efficient navigation. Models complementary learning systems: long-term memory via astrocytic regulation + short-term suppression via spike-frequency adaptation. Use for neuromorphic navigation, spatial memory SNNs, bio-inspired RL, and partial observability tasks. Triggers: dual-timescale, neuron-astrocyte, spatial memory, navigation SNN, astrocyte memory, complementary learning, 双时程记忆, 星形胶质细胞Votes: 0GitHub stars: 3
- Dynamic Gated Neuron SnnDynamic Gated Neuron (DGN) - Biologically plausible gating mechanism for Spiking Neural Networks via dynamic membrane conductance modulation. Enables selective input filtering and adaptive noise suppression. Activation triggers: dynamic gated neuron, DGN, SNN gating, conductance-based SNN, robust spiking neural network, biological gating.Votes: 0GitHub stars: 3
- Dynamic Mean Field Nonlinear NoiseGaussian-equivalent process methodology for analyzing nonlinear noise in recurrent neural circuits using Ornstein-Uhlenbeck noise matching and lognormal moment closure. Activation: mean field, nonlinear noise, recurrent networks, OU process.Votes: 0GitHub stars: 3
- Dynamic Neural Manifold Snn ControlDynamic neural manifolds methodology for flexible closed-loop control on neuromorphic hardware (SpiNNaker 2). Sensory-modulated heterogeneous inhibition drives subspace rotations for behavior switching.Votes: 0GitHub stars: 3
- Dynamic Neural Manifolds ControlDynamic neural manifolds methodology for flexible closed-loop control on neuromorphic hardware. From arXiv:2607.07373 (von Seeler et al., Jul 2026).Votes: 0GitHub stars: 3
- Dynamic Neural Manifolds Neuromorphic ControlDynamic neural manifolds for flexible closed-loop control on neuromorphic hardware. Implements a ring attractor spiking network on the SpiNNaker 2 chip where sensory-modulated heterogeneous inhibition, multiplicative gain, and transient currents drive rapid subspace rotations and fine-grained trajectory control within low-dimensional neural manifolds. Validated with a robotic maze-navigation simulation. Provides an explainable, neuroscience-grounded framework for mapping world-model plans ont...Votes: 0GitHub stars: 3
- Dynamic Neural Manifolds Snn ControlDynamic neural manifolds methodology for flexible closed-loop control on neuromorphic hardware — using circuit mechanisms (heterogeneous inhibition, gain modulation, transient currents) as control knobs for manifold geometry, enabling explainable, energy-efficient autonomous behavior. Implemented on SpiNNaker 2 chip with robotic maze navigation validation.Votes: 0GitHub stars: 3
- Dynamic Path Brain ConnectivityModel dynamic path trajectories in brain functional connectivity to capture temporal evolution of connections between functional communities. Based on arXiv 2510.24025 NeuroPathNet.Votes: 0GitHub stars: 3
- Dynamic Pauli Constraints QcDynamic Pauli Constraints methodology for quantum circuit design - software-oriented model motivated by near-term hardware constraintsVotes: 0GitHub stars: 3
- Dynamical Blueprint Brain State OrganizationA Dynamical Blueprint for Brain State Organization methodology — framework for understanding dynamic organization of brain states through attractor dynamics and neural population trajectoriesVotes: 0GitHub stars: 3
- Dynamical Isometry PlasticityContinual learning framework preserving plasticity via dynamical isometry - Neural Tangent Kernel analysis showing layer-wise Jacobian singular values near 1 prevents plasticity loss, with isometry-promoting regularization and dormant ReLU reactivation mechanisms.Votes: 0GitHub stars: 3
- Dynamical Quantum Optimal TransportDynamical quantum optimal transport (QOT) methodology based on Benamou-Brenier formulation for computing geodesics between positive semidefinite matrices. Use when: computing quantum state transport distances, solving quantum chemistry problems via optimal transport, analyzing numerical convergence of QOT distances, or implementing interior-point methods for quantum density matrix geodesics. Activation: quantum optimal transport, dynamical QOT, Benamou-Brenier, quantum chemistry optimal trans...Votes: 0GitHub stars: 3
- Dysco Latent Dynamics ExtractionDYSCO (Dynamics via Contrastive Learning) - 多视角对比学习从噪声观测中提取潜在动力学系统。通过独立噪声视角分离信号与噪声，恢复潜在轨迹和支配动力学方程。Votes: 0GitHub stars: 3
- Dysco Multiview Latent Dynamics ExtractionDYSCO (Dynamics via Contrastive Learning) methodology for extracting governing equations from latent dynamics via multi-view temporal contrastive learning. Identifies latent dynamical systems from noisy high-dimensional measurements and recovers symbolic governing equations.Votes: 0GitHub stars: 3
- Earable Eeg Auditory PlatformIn-ear EEG monitoring platform methodology for simultaneous EEG sensing and auditory stimulation. Covers personalized IEEM device design, closed-loop neuromodulation, and in-ear electrode validation.Votes: 0GitHub stars: 3
- Eccentricity Confound Eeg Visual Attention DecodingEEG-based visual attention decoding methodology addressing the eccentricity confound. Uses spatial attention tasks with controlled stimulus eccentricity and phase-scrambling controls to demonstrate that decodable EEG signals reflect genuine attention modulation, not visual processing confounds. Use when building EEG-based BCI attention decoders, visual attention research, or addressing confounds in neural decoding.Votes: 0GitHub stars: 3
- Eccentricity Confound Eeg Visual AttentionEccentricity confound analysis for EEG-based visual attention decoding during natural video viewing. Methodological framework for separating true neural attention from stimulus and eye movement artifacts. Keywords: visual attention, EEG, eye movements, eccentricity, natural video, artifact removal.Votes: 0GitHub stars: 3
- Eccentricity Constrained Cnn TrainingMethodology for training CNNs with eccentricity-constrained egocentric video data to reveal adaptive information coding that mirrors primate visual system organization, showing differential task-relevance between foveal and peripheral vision.Votes: 0GitHub stars: 3
- Eccentricity Constrained Cnn Visual CodingEccentricity-constrained CNN training on egocentric video reveals adaptive, task-aligned information coding across the visual field, with fovea-preferred models advantaged for face and object tasks and periphery-preferred models favored in scene-selective cortex.Votes: 0GitHub stars: 3
- Eccentricity Constrained Cnn Visual FieldEccentricity-Constrained CNN Training methodology for adaptive visual information coding around the visual field using egocentric dataVotes: 0GitHub stars: 3
- Ecram Short Term Plasticity NeuromorphicCross-layer device-circuit-system co-design framework for leveraging non-equilibrium ECRAM dynamics as computational resources for short-term plasticity in neuromorphic circuits. ECRAM devices naturally exhibit volatile ionic dynamics that produce transient conductance modulation, which can be exploited for STP rather than treated as unwanted variability.Votes: 0GitHub stars: 3
- Ecram Short Term PlasticityCross-layer device-circuit-system co-design framework for implementing short-term plasticity (STP) in neuromorphic hardware using non-equilibrium ECRAM dynamics. Transforms volatile ionic dynamics from device artifacts into computational resources. Use when studying: neuromorphic short-term plasticity, ECRAM synaptic devices, temporal information processing in spiking networks, delay-feedback LIF neurons, hardware-software co-design for neuromorphic circuits, or activity-dependent conductance...Votes: 0GitHub stars: 3
- Edgespike Edge Iot SnnEdgeSpike: SNN framework for low-power autonomous sensing on edge IoT. Covers hybrid surrogate-gradient training, hardware-aware NAS, event-driven runtime for Loihi 2/SpiNNaker 2/ARM Cortex-M, and local plasticity for on-device adaptation. Activation: edge SNN, IoT sensing, low-power neural networks, neuromorphic edge deployment.Votes: 0GitHub stars: 3
- Eeg Biomarker Robustness Cross PopulationCross-population framework for evaluating robustness and generalizability of EEG biomarkers in multi-site clinical settings. Addresses cross-subject and cross-platform variation for reliable Parkinson's disease detection. Keywords: EEG biomarkers, cross-population, generalization, multi-site, Parkinson's disease, clinical reliability.Votes: 0GitHub stars: 3
- Eeg Brain Connectivity BciEEG脑连接BCI分析方法论。通过功能连接分析理解脑网络在BCI中的机制，用于神经康复和外骨骼控制。适用于脑机接口、神经康复、步态训练。触发词：EEG、脑连接、BCI、脑机接口、功能网络、神经康复、brain-computer interface、neurorehabilitation。Votes: 0GitHub stars: 3
- Eeg Channel Adaptation BenchmarkSystematic benchmark of channel adaptation methods for EEG foundation models. Compares Conv1d, SSI, source-space decomposition, and Riemannian re-centering across 5 FMs (5M-157M params), 5 tasks, revealing architecture-dependent optimal methods and probe-SFT asymmetry.Votes: 0GitHub stars: 3
- Eeg Criticality Deep Sleep Classification NeurofeedbackDeep Sleep Classification via EEG Signal Criticality using Detrended Fluctuation Analysis (DFA) for passive Brain-Computer Interface (pBCI) neurofeedback applications. Probabilistic decoding of EEG criticality features for state-dependent sleep improvement interventions.Votes: 0GitHub stars: 3