12 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 12 plugins that match these filters.
paperthinLilMGenius/paperthin1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceNVIDIA DGX Spark (GB10) environment operations. Use when setting up, preflighting, or debugging ML workloads on DGX Spark or aarch64 Blackwell hardware — CUDA-13 wheel/ABI checks, unified-memory OOM ladders, thermal monitoring.
vLLM operator reference suite — deployment, configuration, quantization, caching, KV, tool parsers, reasoning parsers, chat templates, benchmarking, performance tuning, observability, omni, input modalities, speculative decoding, NVIDIA hardware, and a Gemma 4 31B operating-point serve recipe.
inferenceKubernetes suite — Argo CD application authoring (GitOps Application/ApplicationSet manifests, sync policies, AppProjects), Helm chart authoring, KEDA event-driven autoscaling, Keycloak IAM (Operator + CRDs, realm/client configuration, OIDC/SAML integration, security hardening), OpenShift application packaging, and k8s-components-checker (cross-component compatibility registry for RKE2 community stacks — kubectl/helm/pluto cluster survey, structured verdict + pre-upgrade report wrapper, skill-improver freshen and operator floor-override flows; covers 19 components: RKE2, Rancher, Harvester, Cilium, Tetragon, cert-manager, Kyverno, KEDA, Argo CD, Harbor, Traefik, Rook, Ceph, OpenEBS, GitLab, ECK, Zalando postgres-operator, Grafana Mimir, NVIDIA GPU Operator), plus rancher-upgrade — community Rancher fleet upgrade methodology (KDM downstream support matrix, CAPI→Turtles migration, Fleet, air-gapped upgrade procedure, per-minor 2.11→2.14 runbook), companion to k8s-components-checker. Harv
kubernetesFind the right NVIDIA skill for GPU acceleration, CUDA, AI agents, data loading, training, inference, robotics, Physical AI, Omniverse, simulation, and other NVIDIA-powered workflows.
Inference KV-cache and transport suite — LMCache multiprocess (MP) standalone-server mode (DaemonSet + Deployment K8s pattern, ZMQ connector, L1 + L2 NIXL/POSIX/GDS/HF3FS/fs/s3/mooncake adapters) and NVIDIA NIXL transfer library (UCX/GDS/Mooncake/libfabric/HF3FS/S3 plugins, agent API, telemetry) used by Dynamo/vLLM/SGLang. Pairs with the vllm-caching skill in the `vllm` suite and sglang-hicache in the `sglang` suite.
inferenceInference host bring-up + tuning suite — Linux/GPU bare-metal host work that sits beneath any inference framework (vLLM, sglang, TensorRT-LLM). Two phases. Bring-up (nvidia-datacenter-bringup): Ubuntu 24.04 LTS from clean OS to gpu-operator cuda-validator passing — B300/B200/H100/A100/L40S/L4 driver + fabricmanager + NVLSM + DOCA-OFED install order, NVIDIA CUDA repo + DOCA repo + air-gap mirror, MOK + DKMS sign-on-build under Secure Boot, Dell PowerEdge XE9780/XE9785 baseboard firmware via iDRAC Redfish DellOemChassis.ExtendedReset, gpu-operator pre-installed-driver-mode integration. Tuning (gpu-host-tuning): read-only snapshot of CPU power state, C-states, NUMA topology, PCIe link state, GPU settings, kernel boot params, sysctl, ulimits, IRQ affinity, container runtime; optional pinned-host↔GPU memcpy bench (torch + numactl); per-lever cheat-sheets to flip settings (governor, EPP, cpuidle, persistence, ECC, hugepages, intel_iommu, NCCL env, tuned-adm profiles, BIOS guidance for Dell X
inferenceNVIDIA AIPerf — vendor-neutral generative-AI inference benchmarking (the genai-perf successor): `aiperf profile` with concurrency / request-rate / fixed-schedule trace-replay / user-centric / multi-run-confidence load, 15 endpoint types and 6 custom dataset formats, goodput SLOs, GPU + Prometheus telemetry, and the reasoning-token TTFT/TTFO split — benchmarks vLLM / SGLang / TensorRT-LLM / Dynamo / NIM / Triton / Ollama.
inferenceThe skillbook for recursive self-improvement (RSI). NVIDIA's latest proof, unpacked with timeless engineering. Distilled from the OpenAI harness engineering hackathon winner's zero-touch method.
Prepare a Linux server with NVIDIA or Intel Arc GPUs for LLM and neural-network workloads: NVIDIA driver + CUDA from the official repo or the in-tree Intel i915/xe driver with distro firmware, Docker Engine with verified GPU access (NVIDIA Container Toolkit or /dev/dri passthrough) as the mandatory base, staged verification gates for both vendors, optional docker-compose presets for vLLM, Infinity, OpenWebUI and Ollama plus Intel Arc stacks (llama.cpp SYCL/Vulkan, Ollama, OpenVINO, vLLM), GPU power limits persisted via systemd, case fans driven by GPU temperature and Intel Arc idle-power fixes (ASPM).
opsRead-only discovery for a fleet harness repository: a POSIX probe sent over ssh collects OS, kernel, CPU, RAM, disks, Docker, NVIDIA and Intel Arc GPUs, addresses, ports, tunnels and DNAT rules; discover.py turns it into the host card after comparing it with the card, render_index.py validates every card and regenerates the host index and an optional self-contained network map.
opsCheck whether a host or a whole namespace of a fleet harness repository is healthy, read-only: a POSIX pass over boot history, clock, units, disks, OOM, containers, resolvers, ports, NVIDIA and Intel Arc GPUs and PCIe AER, drift against the host card, stacks against the mirror, and functional probes of every service, rolled up into healthy, warming up, degraded, broken or unreachable.
opsRun NVIDIA OpenShell 0.1.x
