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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Cfpo Counterfactual Multimodal ReasoningCounterfactual Policy Optimization for enforcing causal consistency between visual perception and textual reasoning in LVLMs. Cross-modal counterfactual enhancement regularizes policy via discrepancy maximization.Votes: 0GitHub stars: 3
- Cfspmnet Eeg Motor Imagery StrokeCFSPMNet - Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patients. Use when working with MI-EEG decoding, cross-subject BCI for stroke rehabilitation, Mamba-based EEG models, or Fourier-domain token reorganization for neural decoding.Votes: 0GitHub stars: 3
- Chaos Freezing Without Plasticity无突触可塑性的混沌冻结方法论。引入Onsager反应项使神经网络动力学稳定化，不依赖Hebbian学习即可抑制混沌波动。适用于RNN稳定性分析、神经动力学控制。触发词：混沌冻结、Onsager反应、神经网络稳定性、混沌抑制、RNN动力学、chaos freezing、Onsager reaction、gradient dynamics。Votes: 0GitHub stars: 3
- Chaosprobe Neurochaotic Transformer AnalysisChaosProbe methodology for analyzing frozen transformer input-embedding spaces using deterministic neurochaotic response signatures. Constructs response-based fingerprints by applying chaotic trajectory-based transformations and summarizing Firing Rate and Entropy channel responses to expose broad structure among transformer models.Votes: 0GitHub stars: 3
- Characterize Then Distill Mechanistic ReasoningMechanistic reasoning framework for distillation in large output spaces - two-phase process of shortlisting candidates followed by fine-grained reasoning, consistently outperforming standard distillation.Votes: 0GitHub stars: 3
- Chasmbrain Mamba Brain ReconstructionCHASMBrain层级化Mamba架构用于图像到fMRI编码。双流Mamba设计分离全局语义token和局部空间patch处理，粗到细策略实现ROI级到voxel级预测，在NSD数据集上Pearson相关达0.429。Votes: 0GitHub stars: 3
- Chat History LancedbLanceDB-based chat history system with message storage, semantic search, and RAG context retrieval.Votes: 0GitHub stars: 3
- Cheesebench Rodent NeuroscienceCheeseBench benchmark for evaluating LLMs on classical rodent behavioral neuroscience paradigms. Includes 9 tasks covering water maze, T-maze, Morris water maze, and other established behavioral tests. Cross-paradigm evaluation for neuroscience AI systems.Votes: 0GitHub stars: 3
- Chia Agentic Hardware Software CodesignCHIA framework for principled agentic AI-driven hardware/software co-design. Treat hardware/software design flows as directed cyclic graphs (CHIA loops) with nodes executing SoC design tools, simulators, AI models, and evolutionary agents. Supports Chipyard, gem5, ChampSim, FireSim, Vivado, AlphaEvolve, and more. Enables isolation, profiling, fault-tolerant execution, and reliability across heterogeneous systems.Votes: 0GitHub stars: 3
- Chimera Magnetic Field NeuronalMethodology for studying magnetic field effects on chimera states in Hindmarsh-Rose neuronal networks. Covers traveling chimera, multicluster chimera, and multicluster chimera breather transformations under spatial magnetic field applications.Votes: 0GitHub stars: 3
- Chloride Seizure DynamicsConductance-based neuronal network model for chloride-dependent seizure dynamics. Models how activity-dependent chloride dynamics drive seizure evolution and stage transitions. Activation: computational-neuroscience, seizure, chloride, conductance-based, epilepsy, neuroscience, brain, neuralVotes: 0GitHub stars: 3
- Chrome ExtensionExpert guidance for Chrome extension development with Manifest V3. Use when building Chrome extensions, browser extensions, or working with Chrome APIs. Triggers on: chrome extension, browser extension, manifest v3, chrome api, extension development.Votes: 0GitHub stars: 3
- Chronic Stress Ei Balance RnnComputational modeling methodology for chronic stress as excitatory-inhibitory (E/I) balance perturbation in recurrent working-memory networks. Use when modeling stress effects on prefrontal cortex, studying resilience mechanisms, or analyzing E/I balance disruptions in neuropsychiatric conditions.Votes: 0GitHub stars: 3
- Circuit Level Noise EstimationCircuit-Level Noise Estimation via Shuttling in Plaquette Circuits. Use when analyzing quantum algorithms, complexity bounds, quantum ML architectures, or quantum error correction involving mathematical analysis and statistical methods.Votes: 0GitHub stars: 3
- Circuit Level Spiking Neuron RobustnessCircuit-level spiking neuron model for hardware robustness analysis. Studies how transistor-level variations affect SNN reliability on neuromorphic chips. Activation: circuit-level SNN, neuromorphic hardware reliability, transistor variation spiking, hardware spiking neuron, CMOS spiking, SNN fault toleranceVotes: 0GitHub stars: 3
- Circulate Firing Snn TrainingCirculate-Firing Spiking Neural Network Training methodology - advancing direct training with CFSN model, TSL-SG learnable gradients, and PNB-Loss. Addresses SNN training bottlenecks: limited information capacity and imprecise gradient propagation. Activation: circulate-firing, CFSN, learnable surrogate gradient, TSL-SG, direct training SNN, PNB-Loss, SNN training, spiking neuron model.Votes: 0GitHub stars: 3
- Citizen Science Video Games CognitionCitizen science and video games framework for cognitive science research. Transforms players into research participants through gamified experiments, enabling large-scale data collection for understanding human cognition. Activation: citizen science, video games cognition, gamified research, cognitive science games, participatory research.Votes: 0GitHub stars: 3
- Citras Fm Tiny Timeseries FoundationCITRAS-FM tiny 7M-parameter time series foundation model with covariate-informed zero-shot forecasting using Shifted Attention and CovSynth synthetic covariate generationVotes: 0GitHub stars: 3
- Clad Federated Anomaly DetectionClustered Label-Agnostic Federated Learning (CLAD) framework for anomaly detection in distributed systems. Combines unsupervised clustering with supervised detection via DM²A (Dual-Mode Multi-Stage Aggregation), enabling privacy-preserving anomaly detection across heterogeneous federated clients without shared labels.Votes: 0GitHub stars: 3
- Classical Disjunction Effect ModelClassical probability model that reproduces the disjunction effect in human decision making without violating the law of total probability. Use when analyzing the disjunction effect, Prisoner's Dilemma decision paradox, quantum-like cognition models, classical vs quantum decision models, or ambiguity representation in choice behavior.Votes: 0GitHub stars: 3
- Classical Shadow Unitary Channel EstimationClassical Shadow Estimation of Unitary Channels (CSEU) methodology for efficient quantum process learning with Heisenberg-limited query complexity.Votes: 0GitHub stars: 3
- Claude CodeDelegate coding to Claude Code CLI (features, PRs).Votes: 0GitHub stars: 3
- Claude Plays RoboticsClaude plays roboticsVotes: 0GitHub stars: 3
- Claude Riemann Zeta Mathematical CapabilitiesUse for AI-assisted Riemann hypothesis bound improvement.Votes: 0GitHub stars: 3
- Claudes Values Across Models And LanguagesClaude’s values across models and languagesVotes: 0GitHub stars: 3
- Claw R1 Agentic Rl Data MiddlewareStep-level data middleware system for agentic RL. Captures multi-turn agent-environment interactions and organizes them as managed data assets.Votes: 0GitHub stars: 3
- Clear Mind Meditation Functional SnrProposes that diverse findings in meditation science can be mapped onto a single, empirically tractable construct: **functional signal-to-noise ratio in the brain (f-SNR)**.Votes: 0GitHub stars: 3
- Clockless Asynchronous Neuromorphic ComputingScalable neuromorphic computing via autonomous spiking dynamics in clockless (asynchronous) digital circuits implemented on FPGAs. Boolean spiking neurons with configurable excitatory/inhibitory weights, spike-encoded data processing pipeline. Bridges gap to analog neuromorphic systems without specialized hardware. Based on Oliveira Gomes & Rontani (arXiv: 2605.16114). Use when designing energy-efficient neuromorphic systems on FPGAs, exploring clockless asynchronous digital circuits for neur...Votes: 0GitHub stars: 3
- Clockless Neuromorphic SnnClockless (asynchronous) digital Boolean spiking neural networks for neuromorphic computing. Based on arXiv:2605.16114 (May 2026). Use when: designing clockless/async neuromorphic hardware, implementing Boolean spiking neurons on FPGA, liquid state machines with spike-based encoding, energy-efficient neuromorphic processors, bridging digital and analog neuromorphic systems, Boolean spiking neural network design, autonomous digital circuits for neural dynamics. Activation: clockless neuromorph...Votes: 0GitHub stars: 3
- Closed Form Predictive Coding HgfClosed-form predictive coding via hierarchical Gaussian filters (HGF) methodology from arXiv:2605.20293. Restores precision-weighted prediction errors to predictive coding networks by expressing them as deep hierarchical Gaussian filters. Enables biologically plausible, local learning without backpropagation, with dynamic uncertainty estimates and Hebbian-compatible update rules. Activation: predictive coding, hierarchical Gaussian filter, free energy principle, precision-weighted prediction ...Votes: 0GitHub stars: 3
- Closed Loop Quantum Probabilities UnitarityClosed-loop decomposition of quantum probabilities as direct consequence of unitarity, identifying Bargmann invariants as phase-invariant loop quantities and Born rule as quadratic forward-reverse amplitude product. arXiv:2606.02504Votes: 0GitHub stars: 3
- Cloud Continuum Cps InfrastructureTwo-level reference architecture for Cloud Continuum experimentation separating research-infrastructure layer from application layer with Edge-Fog-Cloud patterns.Votes: 0GitHub stars: 3
- Clp Snn Loihi2 Continual LearningOnline Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network (CLP-SNN). Covers self-normalizing local learning rules, spike-driven neural state machines for autonomous on-chip learning, and breakthrough efficiency gains on neuromorphic hardware. Achieves 113x lower latency and 6,600x lower energy than edge-GPU baselines while matching replay-based accuracy rehearsal-free. Use when: implementing continual learning on neuromorphic hardware, designing SNNs for edge AI depl...Votes: 0GitHub stars: 3
- Clsa Cross Layer Sparse AttentionCross-layer sparse attention sharing routing index across decoder layers for 7.6x decoding speedup and 17.1x throughput improvement at 128K contextVotes: 0GitHub stars: 3
- Cluster Based Distributed Stability CertificatesCluster-based distributed small-signal stability certification for grid-forming inverter networks. Use when: (1) certifying stability of large-scale inverter networks without a fully assembled global model; (2) designing cluster-based or decentralized stability certificates for grid-forming inverters; (3) analyzing voltage and angle-frequency subsystem stability via small-gain and energy arguments; (4) selecting network partitioning resolution to match operational boundaries. Activation: grid...Votes: 0GitHub stars: 3
- Cmos Invertible Logic Stochastic ComputingCMOS invertible logic using spiking stochastic computing methodology. Implements bidirectional (forward/reverse) logic gates via Boltzmann machines with simple spiking neural networks. Activation: invertible logic, stochastic computing, CMOS neuromorphic, Boltzmann machine, factorization hardware, spiking logic gate.Votes: 0GitHub stars: 3
- Cmos Nonlinear Classification Biologically RealisticBiologically Realistic Dynamics for Nonlinear Classification in CMOS+X Neurons. CMOS+X technology for realizing biologically realistic nonlinear neuronal dynamics for efficient spiking neural network classification. Activation: CMOS+X neurons, nonlinear classification, biologically realistic dynamics, neuromorphic hardware.Votes: 0GitHub stars: 3
- Cmut Transcranial Ultrasound BbbCapacitive Micromachined Ultrasonic Transducer (CMUT)-based transcranial focused ultrasound system for blood-brain barrier opening and drug delivery. Includes phase-inversion transmission, microbubble monitoring, and closed-loop control. Activation: CMUT ultrasound, blood-brain barrier, BBB opening, transcranial focused ultrasound, drug delivery to brain.Votes: 0GitHub stars: 3
- Cnn Snn Eeg Imagined SpeechHybrid CNN-SNN architecture for EEG-based imagined speech decoding. First integration of spiking neural networks into imagined speech BCI, achieving 80.13% accuracy on BCI Competition III benchmark. Activation: imagined speech, EEG decoding, CNN-SNN hybrid, spike-based BCI, neuromorphic BCIVotes: 0GitHub stars: 3
- Cnn Snn Imagined Speech DecodingEEG-based imagined speech decoding using hybrid CNN-SNN architecture. First integration of spiking neural networks for imagined speech BCI, achieving 80.13% accuracy on BCI Competition III benchmark. Covers CNN feature extraction, SNN temporal classification, and neuromorphic BCI pipeline design.Votes: 0GitHub stars: 3
- Coarse Feedback Human Aligned VisualShows that extremely coarse feedback signals (distinguishing as few as 8 broad categories) produce neural representations matching or exceeding 1000-class supervised models in brain alignment with primate vision. Trains hundreds of CNNs and ViTs across granularity levels, comparing against macaque electrophysiology and human fMRI. Use when analyzing brain-aligned vision models, role of supervision granularity in neural alignment, or human perceptual similarity learning.Votes: 0GitHub stars: 3
- Cocot Eeg Contrastive FoundationContrastive pretraining methodology for EEG foundation models using multiscale convolutional Transformer architecture. Demonstrates contrastive learning as a superior alternative to masked reconstruction pretraining for EEG, which has high noise and narrow-band information. Achieves SOTA on heterogeneous electrode configurations. Activation: EEG contrastive learning, CoCoT, EEG foundation model, masked reconstruction, multiscale temporal convolution, self-supervised EEG, electrode heterogeneo...Votes: 0GitHub stars: 3
- Code Modulated Motion Vep BciCode-Modulated Motion Visual Evoked Potential (c-MVEP) methodology for brain-computer interfacing using motion stimulation instead of flickering. Use when: designing BCI paradigms, visual evoked potential stimulation, motion-based BCI, reducing visual fatigue in SSVEP/c-VEP systems, EEG-based BCI with pseudo-random sequences. Activation: c-MVEP, motion VEP BCI, code-modulated motion, visual evoked potential BCI, flicker-free BCI, SSVEP alternative, c-VEP alternative.Votes: 0GitHub stars: 3
- Codex Session ManagerMonitor and manage OpenAI Codex CLI sessions. List history, view session content, resume via ACP, track statistics. Use when user asks about Codex sessions, wants to check progress, or resume previous work.Votes: 0GitHub stars: 3
- Coding Agents Social Sciences ResearchMethodology from Anthropic economic research (May 27, 2026) on using AI coding agents to accelerate social science research workflows — experimental design, data analysis, literature review, and reproducibility.Votes: 0GitHub stars: 3
- Coflow Scheduling OcsCoflow Scheduling in Multi-Core Optical Circuit Switching Networks with Performance GuaranteesVotes: 0GitHub stars: 3
- Cogeegagent Autonomous Cognitive Eeg AnalysisCogEEGAgent methodology for autonomous cognitive EEG analysis with grounded execution and selection-aware verification. Uses MNE-Python framework with LLM agents for flexible language understanding while maintaining fail-closed control over inference and release. Provides auditable automation framework for cognitive-EEG workflows with participant-disjoint confirmation and capability hazard blocking.Votes: 0GitHub stars: 3
- Cognisnn Brain Inspired SnnCogniSNN (Cognition-aware Spiking Neural Network) is a comprehensive framework that bridges artificial intelligence and computational neuroscience through Random Graph Architecture (RGA). It implements three core brain-inspired mechanisms: Neuron-Expandability, Pathway-Reusability, and Dynamic-Configurability for advanced SNN design and training. Based on: "CogniSNN: Enabling Neuron-Expandability, Pathway-Reusability, and Dynamic-Configurability with Random Graph Architectures in Spiking Neu...Votes: 0GitHub stars: 3
- Cognisnn Random Graph ArchitectureSkill for understanding and applying the CogniSNN framework: a Spiking Neural Network paradigm that incorporates Random Graph Architecture to achieve Neuron-Expandability, Pathway-Reusability, and Dynamic-Configurability for brain-inspired intelligence.Votes: 0GitHub stars: 3
- Cognisnn Random Graph SnnCogniSNN: Cognition-aware Spiking Neural Network with Random Graph Architecture enabling neuron-expandability, pathway-reusability, and dynamic-configurability. Uses Key Pathway-based Learning (KP-LwF) for multi-task transfer and Dynamic Growth Learning (DGL) algorithm for temporal dimension growth. Achieves SOTA on neuromorphic datasets. Keywords: SNN architecture, random graph, pathway reusability, dynamic growth, neuromorphic hardware, continual learning, brain-inspired AI.Votes: 0GitHub stars: 3