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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Jet Eeg Flow MatchingJust EEG Transformer (JET) — generative EEG framework using conditional flow matching to model neural signals as continuous trajectories, preserving spectral structure, temporal stationarity, and signal statistics. ICML 2026. Reduces TS-FID by >40% on large-scale benchmarks. arXiv:2605.21280Votes: 0GitHub stars: 3
- Karma Economy Resource AllocationNon-monetary karma economy for fair distributed resource allocation. Online karma auctions for EV charging, distributed scheduling, and capacity management. Use when: fair resource allocation, non-monetary economies, distributed scheduling, EV charging optimization, karma auctions, Dynamic Population Games, Stationary Nash Equilibrium, intertemporal allocation.Votes: 0GitHub stars: 3
- Kast Brain AutoregressiveKAST-BAR methodology: Knowledge-Anchored Semantically-Dynamic Topology Brain Autoregressive Modeling for universal neural interpretation. Integrates Dual-Stream Hierarchical Attention (DSHA) encoder for brain topology, Knowledge-Anchored Semantic Profiler (KASP) for expert-level text profiles, and Semantic Text-Aware Refiner (STAR) with Latent Expert Queries. Pre-trained on 21 datasets, evaluated on 6 downstream tasks. Use when: building EEG foundation models, brain topology representation le...Votes: 0GitHub stars: 3
- Kg Research WorkflowEnd-to-end academic research workflow using knowledge graphs. Searches papers from arxiv/web, imports to KG database, generates embeddings, runs graph algorithms (PageRank, vector search), and extracts patterns for skill creation. Use for: automated research workflows, paper analysis pipelines, KG-based literature review.Votes: 0GitHub stars: 3
- Kinematic Zero Shot Bci DecodingZero-shot handwriting BCI decoding via conserved kinematic representations. Aligns neural activity to imagined kinematics for open-vocabulary character decoding without per-character training data. Activation: zero-shot BCI, handwriting decoding, kinematic primitives, intracortical BCI, logographic language BCI, motor cortex representation, imagined handwriting.Votes: 0GitHub stars: 3
- Klr Hopfield Event Driven RetrievalEfficient event-driven retrieval in high-capacity kernel Hopfield networks. Asynchronous sequential updates achieve statistically indistinguishable trajectories from synchronous dynamics with storage capacity P/N ≈ 30. Suitable for low-power neuromorphic associative memory.Votes: 0GitHub stars: 3
- Arxiv 2608 25500v1 Caskg Counterfactual Causal Skill Graphs For Scala**arXiv ID:** 2608.25500v1 **Authors:** Zhiyuan Li, Linyuan Gao, Xuechun Ding, Hongwei Chen, Yuan Wu, Yi Chang **URL:** http://arxiv.org/abs/2608.25500v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 26836v1 Symbollkg Towards Verifiable Logical Reasoning Via**arXiv ID:** 2608.26836v1 **Authors:** Haizhao Fan, Yuchi Xiong, Jize Wang, Xinping Guan, Xinyi Le **URL:** http://arxiv.org/abs/2608.26836v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 30524v1 Beamforming Design Via Gnn In Mmwave Cell Free Mas**arXiv ID:** 2608.30524v1 **Authors:** Sina Tavakolian, Abolfazl Zakeri, Ahmed Alkhateeb, Markku Juntti, Nhan Thanh Nguyen **URL:** http://arxiv.org/abs/2608.30524v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 02364v1 Towards A Foundational Ontology For Identifying An**arXiv ID:** 2609.02364v1 **Authors:** Maitreyee Tewari, Michele Persiani **URL:** http://arxiv.org/abs/2609.02364v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 08869v1 Ontokg Eq A Provenance Grounded Competency Questio**arXiv ID:** 2609.08869v1 **Authors:** Furqan Nasir, Muhammad Atif Saeed, Muhammad Ehsan, Sher Jeel Ahmad, Abdul Moiz Altaf **URL:** http://arxiv.org/abs/2609.08869v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 10413v1 Fortunate Recall Ontology Driven Memory Lifecycle**arXiv ID:** 2609.10413v1 **Authors:** Ansuman Mullick, Eray Tüzün **URL:** http://arxiv.org/abs/2609.10413v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Explicit World Model Based Data First Ontology Daoql Multimodal Storage Validation CounteSkill derived from arXiv:2607.17269 - An Explicit World Model Based on Data-First Ontology: DaoQL Multimodal Storage Validation and CounteVotes: 0GitHub stars: 3
- Graph Network Models To Detect Illicit Transactions In Block Chain**arXiv ID:** 2410.07150 **Authors:** Hrushyang Adloori, Vaishnavi Dasanapu, Abhijith Chandra Mergu **Published:** 2024-09-23T04:38:44Z **Abstract:** The use of cryptocurrencies has led to an increase in illicit activities such as money laundering, with traditional rule-based approaches becoming less effective in detecting and preventing such activities. In this paper, we propose a novel approach to tackling this problem by applying graph attention networks with residual network-like architec...Votes: 0GitHub stars: 3
- Kg Research WorkflowEnd-to-end academic research workflow using knowledge graphs. Searches papers from arxiv/web, imports to KG database, generates embeddings, runs graph algorithms (PageRank, Louvain, vector search), and extracts patterns for skill creation. Use for: automated research workflows, paper analysis pipelines, KG-based literature review.Votes: 0GitHub stars: 3
- Kg Research WorkflowEnd-to-end academic research workflow using knowledge graphs. Searches papers from arxiv/web, imports to KG database, generates embeddings, runs graph algorithms (PageRank, vector search), and extracts patterns for skill creation. Use for: automated research workflows, paper analysis pipelines, KG-based literature review.Votes: 0GitHub stars: 3
- Kgpf Knowledge Graph Foundation ModelKnowledge Graph Foundation Model using Prior-data Fitted Network (PFN) for in-context learning. Unifies transferable relational regularities with inference-time context from structured neighborhoods. Use when: building KG reasoning systems, cross-graph transfer learning, in-context KG completion, multi-graph pretraining, or zero/few-shot adaptation to unseen knowledge graphs. Triggers: knowledge graph foundation model, in-context KG reasoning, PFN for graphs, cross-graph transfer, NBFNet neig...Votes: 0GitHub stars: 3
- Knowledge Graph OpsOperations for the SQLite knowledge graph (kg.db) at /Users/hiyenwong/.openclaw/workspace/kg.db. Use when importing papers, generating embeddings, running PageRank/community detection, or querying the research knowledge graph. Covers the kg_tool CLI and raw SQLite approaches. NOTE: ~/wiki/kg.db is a symlink to the workspace kg.db — approaches. WARNING: kg_tool has a schema mismatch — see references. NOTE: vectors contain mixed dimensions (256-dim and 384-dim) — see Vector Format.Votes: 0GitHub stars: 3
- Language Specific Vs Cross Lingual Knowledge GraphsComparative methodology for language-specific versus cross-lingual knowledge graphs in implicit aspect identification for lower-resource languages, with task-specific fine-tuning strategies.Votes: 0GitHub stars: 3
- Motif Based Filtrations Persistent Homology Framework GraphResearch methodology from paper 'Motif-based filtrations for persistent homology: A framework for graph isomorphism and property prediction'. arXiv:2604.15265v1. Covers key techniques and approaches for neuroscience research. Activation: motif, based, filtrations, math.ATVotes: 0GitHub stars: 3
- Mpp Gnn Subject Adaptive Community DetectionMPP-GNN: Subject-Adaptive Community Detection for fMRI-Based Alzheimer's Disease ClassificationVotes: 0GitHub stars: 3
- Mt Gnn Brain Morphometry PredictionMT-GNN methodology for predicting brain morphometry evolution using graph-based metric tensor embeddings and continuous-time mesh evolution for subcortical structure shape prediction.Votes: 0GitHub stars: 3
- Neural Enquirer Learning To Query Tables With Natural Language**arXiv ID:** 1512.00965 **Authors:** Pengcheng Yin, Zhengdong Lu, Hang Li, Ben Kao **Published:** 2015-12-03T06:46:27Z **Abstract:** We proposed Neural Enquirer as a neural network architecture to execute a natural language (NL) query on a knowledge-base (KB) for answers. Basically, Neural Enquirer finds the distributed representation of a query and then executes it on knowledge-base tables to obtain the answer as one of the values in the tables. Unlike similar efforts in end-to-end training...Votes: 0GitHub stars: 3
- Neural Graph Embedding QuboNeural-powered unit disk graph embedding for QUBO-to-quantum-annealer mapping. Uses neural network methods to solve the minor graph embedding problem for quantum annealers. Use when mapping QUBO problems to quantum hardware, solving graph embedding for D-Wave/quantum annealers, or optimizing qubit connectivity for optimization problems.Votes: 0GitHub stars: 3
- Neuron Soup Shared Neuron Temporal Graphpopulation = initialize_population(pop_size=100, genome_len=14602) for gen in range(max_generations): fitnesses = [] for genome in population: substrate = decode_genome(genome) # paths, weights, delays outputs = simulate(substrate, batch_X) loss = compute_loss(outputs, batch_y) fitnesses.append(-loss) # higher is better population = evolve(population, fitnesses) # select, crossover, mutate best_substrate = decode_genome(argmax(fitnesses)) ``` - Replace the genetic algorit...Votes: 0GitHub stars: 3
- Neuronsoup Temporal GraphsSkill for understanding and applying the NeuronSoup methodology from arXiv:2607.15217v1.Votes: 0GitHub stars: 3
- Nh Gcat Depression Hierarchical GraphNH-GCAT: Nested Hierarchical Graph Causal Attention Networks for Explainable Depression Identification from fMRI. Neurocircuitry-inspired architecture integrating brain hierarchy, causal interactions, and attention mechanisms for Major Depressive Disorder diagnosis.Votes: 0GitHub stars: 3
- Nope Non Selfish Graph CoarseningNOPE graph coarsening methodology using non-selfishness principle for near-linear complexity graph dimensionality reduction, replacing pairwise similarity matching.Votes: 0GitHub stars: 3
- Ontology Driven Cps DataspaceOntology-driven dataspace approach for reproducible test annotation in Cyber-Physical Energy Systems using three-viewpoint ontology framework (HTD-O, SCM-O, OPMW).Votes: 0GitHub stars: 3
- Research Literature KgBuild and analyze knowledge graphs from research literature. Automated pipeline: arxiv search → entity extraction → KG construction → vector embeddings → semantic search → skill pattern extraction. Use when user asks to analyze papers, build research knowledge bases, find related work, or extract reusable patterns from academic literature.Votes: 0GitHub stars: 3
- S3gnn Efficient Graph Mixing"S³GNN (Spectral-Spatial Scalable Graph Neural Network) — efficient global mixing and local message passing for long-range graph learning. Use when building GNNs for tasks with long-range dependencies: (1) graph datasets where oversquashing limits MPNN performance, (2) molecular/biological graph analysis requiring long-range interactions, (3) point cloud or mesh-based physics simulation, (4) knowledge graph QA with multi-hop reasoning.Votes: 0GitHub stars: 3
- Shapley Valueguided Adaptive Ensemble Learning For Explainable Financial Fraud Detection With Us Regulatory Compliance Validation**arXiv ID:** 2604.14231 **Authors:** Mohammad Nasir Uddin, Md Munna Aziz **Published:** 2026-04-14T19:00:20Z **Abstract:** Financial crime costs U.S. institutions over $32 billion each year. Although AI tools for fraud detection have become more advanced, their use in real-world systems still faces a major obstacle: many of these models operate as black boxes that cannot provide the transparent, auditable explanations required by regulations such as OCC Bulletin 2011-12 and Federal Reserve S...Votes: 0GitHub stars: 3
- Sqlite Knowledge GraphSQLite-based lightweight graph database with RAG and graph algorithms. Use when working with knowledge graphs, graph traversal, community detection, or semantic search.Votes: 0GitHub stars: 3
- Towards A Spectrum Of Graph Convolutional Networks**arXiv ID:** 1805.01837 **Authors:** Mathias Niepert, Alberto Garcia-Duran **Published:** 2018-05-04T16:13:36Z **Abstract:** We present our ongoing work on understanding the limitations of graph convolutional networks (GCNs) as well as our work on generalizations of graph convolutions for representing more complex node attribute dependencies. Based on an analysis of GCNs with the help of the corresponding computation graphs, we propose a generalization of existing GCNs where the aggregation ...Votes: 0GitHub stars: 3
- Unified Dynamics Graph Neural ComputationUnifying dynamical systems and graph theory to mechanistically understand computation in neural networks. Combines spectral analysis, community detection, and dynamical systems theory to decompose RNN computation into interpretable sub-circuits. Activation: graph theory neural networks, dynamical systems RNN, mechanistic interpretability, spectral analysis RNN, community detection neural computation.Votes: 0GitHub stars: 3
- Unifying Dynamics Graph Neural ComputationFramework unifying dynamical systems and graph theory to mechanistically understand computation in neural networks. Uses resolvent-based multi-hop pathway analysis to recover input-output routing structure from connectivity, introduces R-RNNs with resolvent-based regularization for temporally structured sparsity. Activation: multi-hop, resolvent RNN, graph computation, neural network interpretability, structure-function mapping, temporal routing, R-RNN, network communication.Votes: 0GitHub stars: 3
- Koopman Quantum Molecular DynamicsKoopman-von Neumann (KvN) molecular dynamics methodology for computing Green-Kubo transport coefficients as quantum algorithm readout problems. Formulates classical NVE and NVT dynamics as unitary evolutions on Hilbert spaces, enabling quantum speedup for molecular property estimation with O(log(1/ε)) qubit scaling.Votes: 0GitHub stars: 3
- Koopman Spectral Certification Multi AgentKoopman spectral analysis methodology for certifying collective reasoning in multi-agent systems. Provides machine-checkable certificates for convergence, coherent factions, and auditable message basis using Koopman operator theory on interaction traces.Votes: 0GitHub stars: 3
- Krylov Complexity Loschmidt AmplitudeRelate Krylov complexity to the Loschmidt amplitude to diagnose quantum dynamics and quantum chaos. Use when analyzing operator growth, OTOCs, Lyapunov exponents, or Krylov-space methods in many-body quantum systems. Triggers: Krylov complexity, Loschmidt amplitude, operator growth, quantum chaos, Lyapunov exponent, Lanczos, eigenvalues of the Krylov operator.Votes: 0GitHub stars: 3
- Krylov Mean Field Chaos Predictability 2026 06 10Theoretical framework demonstrating that mean-field chaos in random recurrent networks is predictable from continuous past historyVotes: 0GitHub stars: 3
- Kuramoto Brain NetworkKuramoto模型脑网络相位动力学分析方法论。使用振荡器同步框架研究脑网络相位耦合，分析催产素等神经调节物质对脑网络动态的影响。适用于脑网络同步性分析、神经调节研究、网络神经科学。触发词：Kuramoto模型、脑网络、相位耦合、同步性、神经调节、催产素、oxytocin、phase coupling、synchronization、brain network dynamics。Votes: 0GitHub stars: 3
- Kuramoto Control TheoryUnified control-theoretic framework for complex-valued Kuramoto networks. Based on arxiv:2604.07249 'Complex-Valued Kuramoto Networks: A Unified Control-Theoretic Framework' by Giordano et al. Use when analyzing Kuramoto network synchronization, phase locking control, switched feedforward control, sliding-mode control for oscillators, or when asked 'Kuramoto control', 'oscillator synchronization', 'phase locking design', 'complex-valued Kuramoto'.Votes: 0GitHub stars: 3
- Kuramoto Von Mises Coupled OscillatorsKuramoto-von Mises时间序列模型用于耦合振荡器的概率建模。无需假设热力学平衡，通过Langevin动力学构造实现非平衡 regime的准确建模，在高采样率下具有闭式代数解。Votes: 0GitHub stars: 3
- L Spine Simd Snn EngineL-SPINE low-precision SIMD spiking neural compute engine with unified multi-precision datapath (INT2/4/8). Multiplier-less shift-add model for FPGA-based edge SNN inference with 3 orders of magnitude energy efficiency improvement. Activation: L-SPINE, SIMD SNN, low-precision SNN, FPGA SNN inference, shift-add SNNVotes: 0GitHub stars: 3
- Lacuna Llm Unlearning TestbedLACUNA testbed methodology for evaluating LLM unlearning localization precision. Use when assessing whether unlearning truly erases knowledge from model parameters or merely obfuscates it, benchmarking unlearning methods, detecting resurfacing attacks, or implementing parameter-level knowledge removal in large language models. Activation: LLM unlearning, knowledge erasure, parameter localization, resurfacing attack, post-hoc removal, PII removal, gradient-based unlearningVotes: 0GitHub stars: 3
- Landau Ginzburg Sleep Stage TransitionsMethodology for modeling sleep-stage transitions using Landau-Ginzburg phenomenology with spatially extended neural fields, treating different sleep boundaries as distinct phase transitions (fold, crossover, first-order-like switch).Votes: 0GitHub stars: 3
- Language Models Need SleepSleep paradigm for LLMs that enables continual learning through memory consolidation and dreaming phases. Use when: (1) implementing continual learning for LLMs; (2) designing memory consolidation mechanisms; (3) creating autonomous self-improvement systems; (4) addressing catastrophic forgetting in sequential tasks; (5) developing RL-based curriculum generation for synthetic data. Trigger words: sleep paradigm, memory consolidation, dreaming process, knowledge seeding, LLM sleep.Votes: 0GitHub stars: 3
- Lapis Laplacian Spiking AttentionLapis spiking attention mechanism.Votes: 0GitHub stars: 3
- Lapis Spiking AttentionLapis: multiplication-free spiking attention.Votes: 0GitHub stars: 3
- Large Fluctuations Open QuantumLarge fluctuation theory for open quantum systems — analyzing atypical measurement outcomes in driven dissipative steady states. Shows large-deviation functions develop lines and surfaces with discontinuous derivatives, unlike equilibrium analytic Wigner functions. Provides framework for rare event statistics in non-equilibrium quantum systems. Activation: large fluctuations, open quantum systems, large-deviation, non-equilibrium, driven dissipative, Wigner function, rare events, steady state...Votes: 0GitHub stars: 3