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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Modular Quantum Shor CompilationDistributed compilation of Shor's algorithm on modular atomic quantum processors. Methodology for large-scale integer factorization across multiple quantum modules with optimized inter-module communication and intra-module clock rates. Use when: compiling Shor's algorithm for distributed quantum hardware, designing modular quantum architectures, optimizing quantum communication between modules, analyzing resource requirements for large-scale factoring, or planning fault-tolerant quantum crypt...Votes: 0GitHub stars: 3
- Modular State Space Model 260714078Model human perception, cognition, and decision dynamics as a modular perception-cognition-decision pipeline state-space model. Provides mathematical formulation, stability conditions, and application to rehabilitation control. Use when you need interpretable dynamical models linking neural mechanisms to behavior.Votes: 0GitHub stars: 3
- Modular State Space Model Perception CognitionA modular state-space model for human perception, cognition, and decision dynamics that links sensory inputs to behavior through latent internal states while maintaining interpretable connections to neuro-cognitive mechanisms.Votes: 0GitHub stars: 3
- Module Lattice SecurityModule lattice security methodology for post-quantum cryptography. Covers unconditional verification of Weber's conjecture, Principal Ideal Problem solvability, Structured CVP Distance on the Log-Unit Lattice, Ring-LWE and Module-LWE security reductions, and cyclotomic field arithmetic. Combines computational number theory (Fukuda-Komatsu sieve, Herbrand's theorem, Coarse Lattice Theorem, Trigamma Theorem) with lattice-based cryptographic security analysis. Activation: module lattice security...Votes: 0GitHub stars: 3
- Mole Lambda Coupled Cluster ResponseMōLe-Λ methodology for learning coupled-cluster response states. Extends Molecular Orbital Learning (MōLe) to predict full CCSD response state by jointly learning T and Λ amplitudes from localized Hartree-Fock orbitals. Provides CC-quality energies, forces, dipoles, polarizabilities, electron density at ML speed. ICML 2026 AI4Physics. Activation: coupled-cluster, CCSD response, molecular orbital learning, quantum chemistry surrogate, Λ-amplitudes, equivariant quantum chemistry, wavefunction l...Votes: 0GitHub stars: 3
- Molecular Qubit Vibronic EngineeringComputational framework for analyzing vibronic relaxation channels in molecular spin qubits. Combines DFT, TD-DFT, and Redfield theory to predict T1 relaxation times and identify dominant decoherence pathways. Use when: analyzing molecular qubit coherence, designing spin qubit ligands, computing spin-lattice relaxation times, vibronic coupling analysis, quantum information processing with molecular spins.Votes: 0GitHub stars: 3
- Monkey Perceptogram Visual ReconstructionPerceptogram: visual reconstruction framework from monkey neural activity — decoding perceived images from primate visual cortex recordings using deep generative models. Activation: monkey visual reconstruction, perceptogram, primate neuroscience, visual decoding, neural-to-image, brain-to-image, visual cortex, perceptual reconstruction.Votes: 0GitHub stars: 3
- Motion Control CausalityDisentangled motion control with causality reasoning for video generation. Use when: motion-controlled video generation, disentangled control systems, motion causality modeling, active-passive motion decomposition, camera-object motion separation, forward/inverse reasoning for dynamics, physically plausible motion synthesis, or interactive motion control.Votes: 0GitHub stars: 3
- Moving Mri Brain ImagingMoving MRI (mMRI) methodology for imaging during large-scale motion. Core idea: Move subject and scanner (magnet, gradients, RF coil) as a single unit to minimize relative motion, enabling neuroimaging during movement. Demonstrates cryogen-free superconducting magnet on pneumatically actuated tilt platform. Enables vestibular function studies during natural head motion. Activation: moving MRI, mMRI, motion MRI, vestibular imaging, motion artifact MRI, naturalistic neuroimaging, superconductin...Votes: 0GitHub stars: 3
- Mpc Drl Autonomous DrivingMPC-RL integrated framework for autonomous driving in multi-agent scenarios. Combines Model Predictive Control's structured constraint handling with Deep Reinforcement Learning's adaptive behavior learning. Use for: autonomous vehicle control, multi-agent navigation at unsignalized intersections, balancing safety and efficiency in automated driving systems.Votes: 0GitHub stars: 3
- Mpc Stability SuboptimalityModel Predictive Control (MPC) stability and suboptimality analysis under plant-model mismatch. Covers discounted and undiscounted infinite-horizon optimal control, stability guarantees with model uncertainty, and suboptimality bounds. Use when analyzing MPC robustness, handling model-plant mismatch in control systems, or implementing robust MPC controllers.Votes: 0GitHub stars: 3
- Mqt Quantum Classical CompilerMQT Compiler Collection - Future-proof quantum-classical compilation framework built on MLIR. Supports complex optimizations and HPC integration. Activation: quantum compiler, MQT, quantum-classical compilation, MLIR quantum, quantum circuit optimization.Votes: 0GitHub stars: 3
- Mt Direction Maps SpatiotemporalSpatiotemporal TDANN for modeling self-organized MT direction selectivity maps in the dorsal stream. Uses 3D ResNet with Momentum Contrast (MoCo) self-supervised learning and biological spatial loss to produce direction-selective pinwheel structures matching macaque MT physiology. Use when modeling cortical topographic self-organization, dorsal stream computation, direction selectivity, or spatiotemporal contrastive learning for visual neuroscience. arXiv: 2605.11718 (q-bio.NC, cs.AI, cs.NE)....Votes: 0GitHub stars: 3
- Mtc Conductance Spiking NetworksMulti-Timescale Conductance Spiking Networks (MTC-SNN) — gradient-trainable spiking neural networks where neural dynamics emerge from shaping the I-V curve via fast, slow, and ultra-slow conductances. Enables tonic, phasic, and bursting firing regimes within a single model, trainable via exact BPTT without surrogate gradients. arXiv: 2605.11835 (May 2026).Votes: 0GitHub stars: 3
- Mtc Spiking NetworksMulti-Timescale Conductance Spiking Networks (MTC-SN): A sparse, gradient-trainable SNN framework with rich firing dynamics for enhanced temporal processing. Uses fast/slow/ultra-slow conductances to shape I-V curves, enabling direct BPTT without surrogate gradients. Activation: multi-timescale conductance, MTC-SN, conductance-based SNN, gradient-trainable spiking, temporal regression SNN, neuromorphic regression.Votes: 0GitHub stars: 3
- Mteeg Multi Task Eeg LoraMulti-task EEG analysis framework using Low-Rank Adaptation (LoRA) for efficient adaptation of pre-trained models to multiple downstream tasks. Addresses EEG signal heterogeneity and task conflicts through task-specific low-rank decomposition. Activation: multi-task EEG, LoRA adaptation, MTEEG, parameter-efficient fine-tuning, cross-subject EEG, task conflict resolution.Votes: 0GitHub stars: 3
- Mtt Bench Social Dominance Mice多模态大语言模型预测小鼠社会优势行为的基准测试框架。MLLM分析原始行为视频预测优势等级。Votes: 0GitHub stars: 3
- A Comparison Of Controller Architectures And Learning Mechanisms For Arbitrary Robot Morphologies**arXiv ID:** 2309.13908 **Authors:** Jie Luo, Jakub Tomczak, Karine Miras, Agoston E. Eiben **Published:** 2023-09-25T07:11:43Z **Abstract:** The main question this paper addresses is: What combination of a robot controller and a learning method should be used, if the morphology of the learning robot is not known in advance? Our interest is rooted in the context of morphologically evolving modular robots, but the question is also relevant in general, for system designers interested in widely...Votes: 0GitHub stars: 3
- A Diagnostic Framework For Ai Agent BehaviorDerived from arXiv:2607.17149 - A Diagnostic Framework for AI Agent BehaviorVotes: 0GitHub stars: 3
- A Hierarchical Memory Architecture Overcomes Context Limits In Long HorizonLarge language models (LLMs) demonstrate remarkable reasoning capabilities, yet their stateless architecture fundamentally limits deployment in long-horizon research workflows requiring multi-session. Based on arXiv:2607.07666.Votes: 0GitHub stars: 3
- A Large Language Model Driven Agent Based Modeling Framework With Multi RoundRecently, Large Language Models (LLMs) have been utilized in various applications of computational social science and provide the possibility to integrate such models into agent-based modeling to expl. Based on arXiv:2607.07387.Votes: 0GitHub stars: 3
- A Multi Agent System For 5g Throughput PredictionDerived from arXiv:2607.16930 - A Multi-Agent System for 5G Throughput Prediction in Multi-Operator Urban EnvironmentsVotes: 0GitHub stars: 3
- A Multi Agent System For Autonomous Fine Tuning FrA Multi-Agent System for Autonomous, Fine-Tuning-Free Clinical Symptom Detection: Development and Validation Study - Clinical notes contain many of the signs and symptoms that bring patients to care, yet this information rarely reaches structured fields. Existing ext...Votes: 0GitHub stars: 3
- A Natureinspired Colony Of Artificial Intelligence System With Fast Detailed And Organized Learner Agents For Enhancing Diversity And Quality**arXiv ID:** 2504.05365 **Authors:** Shan Suthaharan **Published:** 2025-04-07T12:13:14Z **Abstract:** The concepts of convolutional neural networks (CNNs) and multi-agent systems are two important areas of research in artificial intelligence (AI). In this paper, we present an approach that builds a CNN-based colony of AI agents to serve as a single system and perform multiple tasks (e.g., predictions or classifications) in an environment. The proposed system impersonates the natural environ...Votes: 0GitHub stars: 3
- A Reinforcement Learning Approach For Rebalancing Electric Vehicle Sharing Systems**arXiv ID:** 2010.02369 **Authors:** Aigerim Bogyrbayeva, Sungwook Jang, Ankit Shah, Young Jae Jang, Changhyun Kwon **Published:** 2020-10-05T22:24:36Z **Abstract:** This paper proposes a reinforcement learning approach for nightly offline rebalancing operations in free-floating electric vehicle sharing systems (FFEVSS). Due to sparse demand in a network, FFEVSS require relocation of electrical vehicles (EVs) to charging stations and demander nodes, which is typically done by a group of driv...Votes: 0GitHub stars: 3
- A Reinforcement Learning Based Encoderdecoder Framework For Learning Stock Trading Rules**arXiv ID:** 2101.03867 **Authors:** Mehran Taghian, Ahmad Asadi, Reza Safabakhsh **Published:** 2021-01-08T13:19:01Z **Abstract:** A wide variety of deep reinforcement learning (DRL) models have recently been proposed to learn profitable investment strategies. The rules learned by these models outperform the previous strategies specially in high frequency trading environments. However, it is shown that the quality of the extracted features from a long-term sequence of raw prices of the inst...Votes: 0GitHub stars: 3
- A Reinforcement Learningassisted Genetic Programming Algorithm For Team Formation Problem Considering Personjob Matching**arXiv ID:** 2304.04022 **Authors:** Yangyang Guo, Hao Wang, Lei He, Witold Pedrycz, P. N. Suganthan, Yanjie Song **Published:** 2023-04-08T14:32:12Z **Abstract:** An efficient team is essential for the company to successfully complete new projects. To solve the team formation problem considering person-job matching (TFP-PJM), a 0-1 integer programming model is constructed, which considers both person-job matching and team members' willingness to communicate on team efficiency, with the pers...Votes: 0GitHub stars: 3
- A State Aggregation Approach For Solving Knapsack Problem With Deep Reinforcement Learning**arXiv ID:** 2004.12117 **Authors:** Reza Refaei Afshar, Yingqian Zhang, Murat Firat, Uzay Kaymak **Published:** 2020-04-25T11:52:24Z **Abstract:** This paper proposes a Deep Reinforcement Learning (DRL) approach for solving knapsack problem. The proposed method consists of a state aggregation step based on tabular reinforcement learning to extract features and construct states. The state aggregation policy is applied to each problem instance of the knapsack problem, which is used with Advan...Votes: 0GitHub stars: 3
- A Survey Of Reinforcement Learning For Optimization In Automation**arXiv ID:** 2502.09417 **Authors:** Ahmad Farooq, Kamran Iqbal **Published:** 2025-02-13T15:40:39Z **Abstract:** Reinforcement Learning (RL) has become a critical tool for optimization challenges within automation, leading to significant advancements in several areas. This review article examines the current landscape of RL within automation, with a particular focus on its roles in manufacturing, energy systems, and robotics. It discusses state-of-the-art methods, major challenges, and upco...Votes: 0GitHub stars: 3
- A Symbolic Neural Cpu For Quantizationsimulated Writeback And Interpretable Program Execution**arXiv ID:** 2607.10021 **Authors:** Jose Luis Lima de Jesus Silva **Published:** 2026-07-10T22:55:23Z **Abstract:** Neural networks can learn algorithmic input-output mappings, but trusting a learned executor requires more than a correct final answer because the state transitions that produce it are usually hidden. To make those transitions visible, we introduce a trace-supervised symbolic neural CPU, a factorized learned execution architecture that combines recurrent control, an explicit o...Votes: 0GitHub stars: 3
- Actrce Augmenting Experience Via Teachers Advice For Multigoal Reinforcement Learning**arXiv ID:** 1902.04546 **Authors:** Harris Chan, Yuhuai Wu, Jamie Kiros, Sanja Fidler, Jimmy Ba **Published:** 2019-02-12T18:43:56Z **Abstract:** Sparse reward is one of the most challenging problems in reinforcement learning (RL). Hindsight Experience Replay (HER) attempts to address this issue by converting a failed experience to a successful one by relabeling the goals. Despite its effectiveness, HER has limited applicability because it lacks a compact and universal goal representation. ...Votes: 0GitHub stars: 3
- Aflow Agentic Workflow GenerationSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Agent Autonomous Dreaming基于神经科学研究的 Agent 自主梦境系统，模拟人类睡眠中的记忆巩固过程。适用于 AI Agent 的记忆管理、知识整理、自动学习场景。触发词：自主梦境、记忆重塑、agent dreaming、memory consolidation、每日做梦Votes: 0GitHub stars: 3
- Agent Delivery Engineering Predictive Reliability FrameworkLong-horizon LLM multi-agent systems face reliability risks invisible to infrastructure monitoring. We propose the ADE Predictive Reliability Framework (ADE-PRF), enabling proactive health trajectory. Based on arXiv:2607.07689.Votes: 0GitHub stars: 3
- Agent First BootstrapInitialize projects with Agent-First methodology. Supports Codex, Claude Code, Qwen Code, GitHub Copilot, Gemini CLI. Generates AGENTS.md, tool-specific configs, and documentation structure.Votes: 0GitHub stars: 3
- Agent Memory ForgettingImplement adaptive memory forgetting for autonomous AI agents. Use when building long-horizon conversational agents, managing agent memory growth, preventing false memory propagation, or implementing relevance-guided memory scoring. Based on arXiv:2604.02280 - Novel Memory Forgetting Techniques for Autonomous AI Agents.Votes: 0GitHub stars: 3
- Agent Memory FrameworkDesign and implement memory-augmented AI agents using modular architecture (extraction, update, retrieval, response). Inspired by MemFactory (arxiv:2603.29493) - unified training/inference framework for agent memory with RL-driven policy optimization (GRPO). Use when building long-term AI agents, memory management systems, or implementing Memory-R1/RMM/MemAgent paradigms. Keywords: agent memory, memory-augmented LLM, MemFactory, Memory-R1, memory lifecycle, GRPO, memory extraction, memory ret...Votes: 0GitHub stars: 3
- Agent Reliability Come Cross Benchmark Decomposition Verification Loops SpeciSkill derived from arXiv:2607.17044 - Where Does Agent Reliability Come From? A Cross-Benchmark Decomposition of Verification Loops, SpeciVotes: 0GitHub stars: 3
- Agent Safety LayerRuntime safety and observability layer for local AI agents. Intercepts every proposed agent action before execution, evaluates against declarative policy, requires human approval for sensitive operations, and records complete audit trails. Use when building safe local AI agents, implementing action interception, designing agent guardrails, or creating policy enforcement for autonomous systems. Covers AgentWall methodology (arXiv: 2605.16265).Votes: 0GitHub stars: 3
- Agentgfm A Graph Foundation Model With Node AgentAgentGFM: A Graph Foundation Model with Node-Agent Information-Flow ControlVotes: 0GitHub stars: 3
- Agentgwo Collaborative Agents For Dynamic Prompt Optimization In Large Language Models**arXiv ID:** 2604.18612 **Authors:** Xudong Wang, Chaoning Zhang, Chenghao Li, Shuxu Chen, Qigan Sun, Jiaquan Zhang, Fachrina Dewi Puspitasari, Tae-Ho Kim, Jiwei Wei, Malu Zhang, Guoqing Wang, Yang Yang, Heng Tao Shen **Published:** 2026-04-14T07:35:37Z **Abstract:** Large Language Models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, while recent prompting strategies such as Chain-of-Thought (CoT) have further elevated their performance in handling complex logical ...Votes: 0GitHub stars: 3
- Agentic Agile VAgentic Agile-V framework for verified AI-assisted engineering — SCOPE-V micro-cycle (Specify, Constrain, Orchestrate, Prove, Evolve, Verify) for software, firmware, and hardware development using AI coding agents.Votes: 0GitHub stars: 3
- Agentic Coding And Persistent Returns To ExpertiseAgentic coding and persistent returns to expertiseVotes: 0GitHub stars: 3
- Agentic Coding SecuritySecurity framework for protecting agentic AI coding assistants from indirect prompt injection attacks via external artifacts. Addresses the risk that hidden instructions in code repositories, documentation, StackOverflow posts, and other external artifacts can hijack coding agents, turning them into attacker shells. Use when: evaluating AI coding assistant security, designing secure agent workflows, auditing agent attack surfaces, implementing defense against prompt injection in agentic syste...Votes: 0GitHub stars: 3
- Agentic Coding Without The Cloud Evaluating Open WeightAgentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasksVotes: 0GitHub stars: 3
- Agentic Context Management Solving Agent Memory And CostAgentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture ProblemsVotes: 0GitHub stars: 3
- Agentic Control MemorySkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Agentic Data EnvironmentsAutonomous agents promise substantial gains in speed, scale, and labor efficiency, but their failures can impose abrupt and often irreversible costs. The central challenge for agentic automation is th. Based on arXiv:2607.07397.Votes: 0GitHub stars: 3
- Agentic Human In The Loop CpsReactor model-of-computation based framework for robustness and determinism in agentic AI-powered human-in-the-loop cyber-physical systems. Uses Lingua Franca framework to address nondeterminism from unpredictable human users, AI agents, and dynamic physical environments. Use for: agentic CPS design, human-in-the-loop systems, Lingua Franca implementation, reactor MoC, AI-powered CPS. Activation: agentic CPS, human-in-the-loop, Lingua Franca, reactor model, AI-powered CPS.Votes: 0GitHub stars: 3
- Agentic Information Fusion Test MaintenanceMulti-agent framework (MAST) for predicting which test cases require maintenance after production code changes. Uses agentic information fusion across code diffs, test history, and semantic analysis to identify tests needing updates. Activation: test maintenance, multi-agent testing, agentic information fusion, MAST, test prediction, code evolution.Votes: 0GitHub stars: 3