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Deployment, cloud, containers, CI/CD, monitoring, and infrastructure
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- Comparison FormatImported skill comparison_format from openaiVotes: 0GitHub stars: 3
- Mcp Best PracticesImported skill mcp_best_practices from anthropicVotes: 0GitHub stars: 3
- ClaudeImported skill claude from agentskillsVotes: 0GitHub stars: 3
- Container DeploymentContainerize Qobuzarr plugin and automate deployments. Use when working with Docker images, container registries, deployment automation, or orchestration. Critical for establishing containerization from scratch.Votes: 0GitHub stars: 3
- Qdiffusion Time SeriesQuantum generative diffusion model for time series synthesis (QDiffusion-TS). First quantum diffusion model validated on real quantum hardware (IQM processor). Replaces feed-forward components with quantum neural networks, reducing parameters by ~3 orders of magnitude. Reduces Wasserstein distance by ~44% vs classical. Use when: quantum generative modeling, time series synthesis, quantum-enhanced data augmentation, parameter-efficient diffusion models.Votes: 0GitHub stars: 3
- Pose To Biomechanics Bridging 3d Pose BiomechanicalBioModule: lightweight plug-in temporal transformer that attaches downstream of any 3D pose estimator to predict biomechanical attributes from standard 17-joint 3D skeletons. Estimator-agnostic, requires no modification of upstream pose model. Use when working with biomechanics, pose-estimation, human-motion-analysis.Votes: 0GitHub stars: 3
- Fabrivia Lightweight Vision Language Action Multi Task ManipulationFabriVLA: lightweight VLA model combining InternVL3.5 VLM backbone with flow-matching action head. Gated self-attention across action tokens, shallow VLM layer fusion. 90.0% success on Meta-World MT50. 1B-scale VLM without billion-parameter backbone. Activation: vision-language-action, lightweight model, multi-task manipulation, flow-matching, robot learning.Votes: 0GitHub stars: 3
- Embodiedgen V2 An Agentic Simulation Ready 3d World Engine For Embodied AiWe present EmbodiedGen V2, a generative 3D world engine for building executable sim-ready environments for embodied intelligence. Sim-ready 3D asset generation has advanced rapidly, yet assembling suc. Based on arXiv:2607.07459.Votes: 0GitHub stars: 3
- Ardy Autoregressive Diffusion Interactive Human MotionStreaming generation framework for real-time 3D human motion controllable via online text prompts and kinematic constraints. Uses hybrid representation combining explicit root features with latent body embedding. Two-stage autoregressive transformer denoiser with variable history context. Use when working with autoregressive-diffusion, human-motion-generation, streaming-generation.Votes: 0GitHub stars: 3
- Working Memory Recurrent Spiking NeuralWorking memory -- the ability to store and recall precise temporal patterns of neural activity -- remains an open challenge for spiking neural networks (SNNs). We propose a recurre...Votes: 0GitHub stars: 3
- Uncertainty Token Pruning SpikingTraining-free token pruning for spiking transformers using temporal uncertainty patterns. Models token-wise class evidence with Dirichlet distribution and summarizes each token's temporal uncertainty via mean and fluctuation across spiking steps. Tokens with low uncertainty contribution are pruned during inference. Use when optimizing spiking transformer inference efficiency, reducing token redundancy in SNN vision models, or implementing plug-and-play token reduction for neuromorphic models....Votes: 0GitHub stars: 3
- Temporal Poisoning Clean Label Backdoors SnnSkill for understanding and applying clean-label backdoor attacks via event redistribution in Spiking Neural Networks (SNNs) from arXiv paper 2607.28075Votes: 0GitHub stars: 3
- Submw Neuromorphic Snn Audio Processing Applications With Rockpool And Xylo**arXiv ID:** 2208.12991 **Authors:** Hannah Bos, Dylan Muir **Published:** 2022-08-27T11:50:32Z **Abstract:** Spiking Neural Networks (SNNs) provide an efficient computational mechanism for temporal signal processing, especially when coupled with low-power SNN inference ASICs. SNNs have been historically difficult to configure, lacking a general method for finding solutions for arbitrary tasks. In recent years, gradient-descent optimization methods have been applied to SNNs with increasing e...Votes: 0GitHub stars: 3
- Spear Structured Pruning For Spiking Neural Networks Via Synaptic Operation Estimation And Reinforcement Learning**arXiv ID:** 2507.02945 **Authors:** Hui Xie, Yuhe Liu, Shaoqi Yang, Jinyang Guo, Yufei Guo, Yuqing Ma, Jiaxin Chen, Jiaheng Liu, Xianglong Liu **Published:** 2025-06-28T15:21:05Z **Abstract:** While deep spiking neural networks (SNNs) demonstrate superior performance, their deployment on resource-constrained neuromorphic hardware still remains challenging. Network pruning offers a viable solution by reducing both parameters and synaptic operations (SynOps) to facilitate the edge deployment ...Votes: 0GitHub stars: 3
- Snn Quantization Emd Beyond AccuracyEarth Mover's Distance (EMD) framework for evaluating SNN quantization beyond accuracy. Shows uniform quantization causes firing distribution drift even when accuracy is preserved. Proposes LQ-Net style learned quantization for maintaining firing behavior.Votes: 0GitHub stars: 3
- Snn Quantization Beyond AccuracyQuantization of Spiking Neural Networks beyond accuracy - evaluating SNN quantization using Earth Mover's Distance (EMD) and firing distribution preservation, not just task accuracy.Votes: 0GitHub stars: 3
- Snn Learning NeuromorphicSpiking Neural Network learning methods for neuromorphic computing — covering surrogate gradient, STDP, three-factor learning, DECOLLE, and sharpness-aware training. Use when training SNNs on neuromorphic hardware, implementing event-based learning, optimizing sparsity in spiking networks, or deploying energy-efficient AI. Trigger words: spiking neural network, SNN training, surrogate gradient, STDP, three-factor learning, DECOLLE, neuromorphic learning, event-based learning, temporal credit ...Votes: 0GitHub stars: 3
- Snn Fpga Hardware Software CodesignFPGA accelerator design for Spiking Neural Networks using Spiking Recurrent Cell (SRC) neurons, with mathematical simplifications to remove costly unary operators and avoid floating-point arithmetic. Covers piecewise approximations, LUT-register weight storage, and accuracy/energy trade-off analysis. Use when deploying SNNs on FPGA hardware, designing neuromorphic accelerators, or optimizing SNN inference energy efficiency. Trigger: SNN FPGA, spiking recurrent cell, SRC neuron, neuromorphic h...Votes: 0GitHub stars: 3
- Pdds Spiking Insulin DeliveryPDDS event-driven spiking neural network pipeline for personalized insulin delivery — LIF neurons with Poisson encoding on OhioT1DM dataset achieving 85.24% accuracy. Activation: insulin delivery, spiking neural network, healthcare, event-driven, personalized medicine, blood glucose, SNN, neuromorphic.Votes: 0GitHub stars: 3
- Neurotrain Local Learning Snn BenchmarkingNeuroTrain methodology for surveying and benchmarking local learning rules in Spiking Neural Networks (SNNs). Covers comprehensive taxonomy of SNN training algorithms (surrogate-gradient BP, local/three-factor rules, bio-inspired plasticity, ANN-to-SNN conversion, non-standard optimization) and provides open-source snnTorch-based benchmarking framework. Use when: benchmarking SNN training algorithms, comparing local learning rules, implementing biologically plausible plasticity, or surveying ...Votes: 0GitHub stars: 3
- Neuromorphic Parameter Estimation Power ConverterNeuromorphic parameter estimation using Spiking Neural Networks for power converter health monitoring on edge devices. SNN-based real-time parameter estimation for DC-DC converters deployed on neuromorphic hardware. Based on arXiv:2604.15714. Use when: power converter health, edge SNN, neuromorphic hardware, DC-DC converter, real-time health monitoring, energy harvesting, embedded systems.Votes: 0GitHub stars: 3
- Hardware Aware Mixed Signal Snn FrameworkOpen-source hardware-aware simulation framework for mixed-signal SNNs enabling comparative analysis across neuron models (LIF, HH, AH), synapse types (floating-gate, ReRAM), and architectures. Reports accuracy with hardware metrics (area, power, quantization sensitivity).Votes: 0GitHub stars: 3
- Gradientfree Continual Learning In Spiking Neural Networks Via Interspike Interval Regularization**arXiv ID:** 2604.16496 **Authors:** Samrendra Roy, Kazuma Kobayashi, Souvik Chakraborty, Sajedul Talukder, Syed Bahauddin Alam **Published:** 2026-04-14T03:16:26Z **Abstract:** Continual learning, the ability to acquire new tasks sequentially without forgetting prior knowledge, is essential for deploying neural networks in dynamic real-world environments, from nuclear digital twin monitoring to grid-edge fault detection. Existing synaptic importance methods, such as Elastic Weight Consolida...Votes: 0GitHub stars: 3
- Gelneuro Neuromorphic Tactile SystemFully integrated sensing-computing neuromorphic visuo-tactile system for texture recognition on edge hardwareVotes: 0GitHub stars: 3