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Deployment, cloud, containers, CI/CD, monitoring, and infrastructure
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- Microsoft Skills .Github Plugins Azure Skills Skills Azure Cloud MigrateAssess and migrate cross-cloud workloads to Azure with reports and code conversion. Supports Lambda→Functions, Beanstalk/Heroku/App Engine→App Service, Fargate/Kubernetes/Cloud Run→Container Apps. WHEN: migrate Lambda to Functions, AWS to Azure, migrate Beanstalk, migrate Heroku, migrate App Engine, Cloud Run migration, Fargate to ACA, ECS/Kubernetes/GKE/EKS to Container Apps, cross-cloud migration.Votes: 0GitHub stars: 3
- Microsoft Skills .Github Plugins Azure Sdk Python Skills Azure Monitor Opentelemetry Exporter PyAzure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights. Triggers: "azure-monitor-opentelemetry-exporter", "AzureMonitorTraceExporter", "AzureMonitorMetricExporter", "AzureMonitorLogExporter".Votes: 0GitHub stars: 3
- Microsoft Skills .Github Plugins Azure Sdk Python Skills Azure Identity PyAzure Identity SDK for Python authentication with Microsoft Entra ID. Use for DefaultAzureCredential, managed identity, service principals, and token caching. Triggers: "azure-identity", "DefaultAzureCredential", "authentication", "managed identity", "service principal", "credential".Votes: 0GitHub stars: 3
- Microsoft Skills .Github Plugins Azure Sdk Python Skills Azure Containerregistry PyAzure Container Registry SDK for Python. Use for managing container images, artifacts, and repositories. Triggers: "azure-containerregistry", "ContainerRegistryClient", "container images", "docker registry", "ACR".Votes: 0GitHub stars: 3
- Microsoft Skills .Github Plugins Azure Sdk Python Skills Azure Ai Language Conversations PyImplement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.Votes: 0GitHub stars: 3
- Huggingface Skills Skills Hf CliHugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; con...Votes: 0GitHub stars: 3
- Brycewang Stanford Awesome Agent Skills For Empirical Research Skills 42 Wanshuiyin ARIS Skills Run ExperimentDeploy and run ML experiments on local or remote GPU servers. Use when user says "run experiment", "deploy to server", "跑实验", or needs to launch training jobs.Votes: 0GitHub stars: 3
- Ubsoft Benchmark EvalThis evaluation protocol assesses the computational efficiency, physical realism, and policy-learning effectiveness of the UBSoft simulation platform for robotic tasks in unbounded soft environments. It benchmarks how well different algorithms (RL, trajectory optimization, heuristics) perform on eight manipulation and locomotion tasks, and evaluates the fidelity of open-loop sim-to-real transfer. Use when the user wants to benchmark on UBSoft Benchmark, or asks about evaluating this task. Rep...Votes: 0GitHub stars: 3
- Tf2aif Inference Latency EvalEvaluates the execution latency and code-generation efficiency of automated, platform-specific AI inference engines across heterogeneous cloud-edge hardware (CPU, GPU, ARM, FPGA, SoC) for image classification tasks. Use when the user wants to benchmark on Image classification dataset (unspecified), or asks about evaluating this task. Reports execution latency.Votes: 0GitHub stars: 3
- Text Classification Energy EvalThis benchmark evaluates the trade-off between model accuracy, inference energy consumption, and runtime across diverse text classification models and hardware configurations. It probes whether larger or more complex models consistently outperform smaller or traditional ones in accuracy while highlighting the energy costs of different architectures and deployment strategies. Use when the user wants to benchmark on Text classification test set, or asks about evaluating this task. Reports accur...Votes: 0GitHub stars: 3
- Reef Substrate Classification EvalEvaluates an AI model's ability to classify underwater substrates for autonomous coral reseeding deployment. It probes both fine-grained patch-level semantic segmentation (distinguishing coral, deploy, and no-deploy zones) and coarse-grained image-level decision making for real-time marine robotics. Use when the user wants to benchmark on Great Barrier Reef ReefScan Dataset, or asks about evaluating this task. Reports Macro F1.Votes: 0GitHub stars: 3
- Proai Hardware Benchmark EvalEvaluates the power efficiency, throughput, and real-time inference performance of embedded AI hardware platforms running multitask and single-task deep neural networks for automotive vision tasks. Use when the user wants to benchmark on COCO test2017, or asks about evaluating this task. Reports FPS, inference time, memory usage, energy efficiency (Wtotal, W/fps).Votes: 0GitHub stars: 3
- Pick And Spin EvalEvaluates a Kubernetes-based multi-model orchestration framework for self-hosted LLMs, measuring how hybrid routing and adaptive scaling affect inference reliability, latency, GPU utilization, and cost across diverse benchmarks. Use when the user wants to benchmark on HumanEval, GSM8K, MBPP, TruthfulQA, ARC, HellaSwag, MATH, MMLU Pro, or asks about evaluating this task. Reports success.Votes: 0GitHub stars: 3
- Peernet Profiling EvalEvaluates a profiling framework's ability to measure granular, end-to-end latency and network asymmetry across heterogeneous hardware and live wireless networks in robotic systems. It probes how well the tool captures component-level timing, inference variance, and transmission delays in real-world deployments. Use when the user wants to benchmark on ImageNet, Waymo Open Dataset, Franka Emika Panda Teleoperation Setup, or asks about evaluating this task. Reports end-to-end latency.Votes: 0GitHub stars: 3
- Parma Performance EvalMeasures the computational, network, and database performance overhead of running containerized workloads inside an AMD SEV-SNP enclave with Parma's attested execution policies compared to a baseline outside the enclave. Use when the user wants to benchmark on nginx (wrk2), redis (redis-benchmark), SPEC2017 intspeed, NVIDIA Triton Inference Server (perf-analyzer), or asks about evaluating this task. Reports performance_overhead.Votes: 0GitHub stars: 3
- Openfgl EvalEvaluates federated graph learning (FGL) algorithms across effectiveness, robustness, and efficiency dimensions. It probes how well distributed GNN training handles data heterogeneity, local noise/sparsity, low client participation, and privacy constraints across diverse graph topologies and simulation scenarios. Use when the user wants to benchmark on MUTAG, BZR, COX2, ENZYMES, DD, PROTEINS, COLLAB, BINARY, MULTI, Cora, CiteSeer, PubMed, Photo, Computers, Products, Chameleon, Actor, Ratings,...Votes: 0GitHub stars: 3
- Nuscenes 3d Detection EvalEvaluates the capability of multi-view 3D object detection models to accurately localize and classify objects in autonomous driving scenes using camera inputs. It measures detection accuracy alongside computational efficiency and inference latency to assess real-time deployment feasibility. Use when the user wants to benchmark on NuScenes, or asks about evaluating this task. Reports NDS.Votes: 0GitHub stars: 3
- Mobile Vlm Deployment EvalEvaluates the runtime efficiency, hardware utilization, and thermal/energy impact of deploying vision-language models on mobile devices. It measures latency breakdowns, CPU/GPU/NPU usage, power consumption, and output characteristics across different inference frameworks. Use when the user wants to benchmark on Custom Mobile VLM Inference Test Set, or asks about evaluating this task. Reports Latency.Votes: 0GitHub stars: 3
- Mlperf Inference EvalEvaluates ML inference systems across diverse hardware and software stacks under realistic deployment scenarios. It measures both model quality against strict baselines and system performance (latency/throughput) to enable architecture-neutral comparisons of production-like workloads. Use when the user wants to benchmark on ImageNet, COCO, WMT16 EN-DE, or asks about evaluating this task. Reports Top-1 accuracy.Votes: 0GitHub stars: 3
- Medsam Laptop EvalEvaluates the segmentation accuracy and inference efficiency of lightweight, promptable medical image models across diverse imaging modalities. It probes the trade-off between mask quality (overlap and boundary alignment) and computational speed on both 2D and 3D medical data. Use when the user wants to benchmark on MedSAMSlicer Competition Dataset, or asks about evaluating this task. Reports Dice Similarity Coefficient (DSC).Votes: 0GitHub stars: 3
- Mec Offloading EvalEvaluates the stability, convergence, and resource efficiency of online computation offloading algorithms in dynamic mobile-edge networks under stochastic task arrivals and time-varying channel conditions. It probes whether an algorithm can maintain queue stability and power constraints while maximizing computation throughput. Use when the user wants to benchmark on Simulated MEC Offloading Environment, or asks about evaluating this task. Reports weighted sum computation rate.Votes: 0GitHub stars: 3
- Mcity Data Engine EvalEvaluates an open-vocabulary data selection pipeline for iteratively improving object detection models on rare classes. Specifically, it tests whether adding newly selected and labeled frames of vulnerable road users (pedestrians, cyclists) to a seed dataset improves detection performance on fisheye traffic camera data. Use when the user wants to benchmark on SIP VRU Detection Dataset, or asks about evaluating this task. Reports mAP@0.5.Votes: 0GitHub stars: 3
- K8s Misconfig Detection EvalEvaluates the ability of security tools to detect network misconfigurations in Kubernetes Helm charts, focusing on discrepancies between declared configurations and actual runtime behavior, including port exposure, label collisions, and network policy effectiveness. Use when the user wants to benchmark on Kubernetes Helm Charts (287 apps), or asks about evaluating this task. Reports misconfiguration detection (found/partially found/missed).Votes: 0GitHub stars: 3
- Healthslm Bench EvalEvaluates small language models on health prediction tasks using wearable sensor data. It probes the models' ability to infer physiological and mental health states (e.g., stress, fatigue, depression) from temporal behavioral and physiological features under zero-shot, few-shot, and instruction-tuned settings. Use when the user wants to benchmark on PMData, GLOBEM, AW-FB, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3