27 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 27 plugins that match these filters.
claude-code-plugins-plusjeremylongshore/tons-of-skills-marketplace2
agents-incagents-inc/skills2
research-agent-skillsKalarisLabs/research-agent-skills1
skillsmicrosoft/skills1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceEvidence-gated Vast.ai operator pack for GPU rentals, Serverless, governance, recovery, and cost control (24 skills)
Inference serving, GPU infrastructure, MLOps and observability.
researchAKS operational skills for troubleshooting, advanced cost-efficiency analysis, known issues, GPU inference, and packet capture.
AKS operational skills for troubleshooting, advanced cost-efficiency analysis, known issues, GPU inference, and packet capture.
Track new token launches, detect rugpulls, and analyze contract security for early-stage crypto projects
Detect and optimize CPU and GPU workload bottlenecks with zymtrace continuous profiling — analyze flamegraphs through the MCP, plus install, upgrade, expose, and troubleshoot the platform.
Kubernetes 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
kubernetesSelf-contained web motion skills with TypeGPU, Motion React, CSS, WAAPI, Tailwind, Lottie, Three.js/R3F, and Rive references.
Find the right NVIDIA skill for GPU acceleration, CUDA, AI agents, data loading, training, inference, robotics, Physical AI, Omniverse, simulation, and other NVIDIA-powered workflows.
From nothing to a working AI setup you own. Scans the machine for models that fit, installs and wires up the right app with consent at every download, shares it over a private URL, and rents a cloud GPU when a model is too big. Needs shell access.
Serverless GPU compute platform for AI model deployment — web endpoints, GPU functions, model serving, and TypeScript client patterns
React Native Skia GPU-accelerated 2D graphics - Canvas, declarative drawing, shaders, image filters, Paragraph text, Atlas batch rendering, Reanimated animations
Operate, configure, benchmark, and troubleshoot llama.cpp across CPU, Metal, CUDA, HIP/ROCm, Vulkan, SYCL, and hybrid or multi-GPU systems. Use when installing or building llama.cpp, selecting or inspecting GGUF models, running llama-cli or bounded llama-completion, serving an OpenAI-compatible API with llama-server, tuning memory and performance, or diagnosing backend, context, template, and API failures. Do not use for model training or fine-tuning, general inference-framework selection, llama-cpp-python or other bindings, LlamaIndex, Ollama, or LM Studio operation.
Install, operate, and troubleshoot Unsloth Core, Studio, and Desktop for local inference and model training. Use for Unsloth environment setup, LoRA/QLoRA supervised fine-tuning, coding specialization, verifier audits, chat templates, GRPO and preference training, vision and audio workflows, decision models, local coding agents and tools, dynamic quantization, checkpoint recovery, multi-GPU training, and model export to GGUF or serving engines. Do not use for framework-neutral ML strategy or operating a standalone inference server unrelated to Unsloth.
Operate, configure, benchmark, and troubleshoot vLLM inference servers: Docker and Kubernetes deployment, quantization-aware model configuration (tensor parallelism, KV cache), OpenAI-compatible API serving, throughput and latency benchmarking, continuous batching tuning, GPU operation, and upgrade/rollback. Use when deploying or running a vLLM server (vllm serve, vllm/vllm-openai), sizing a model and its KV cache for GPUs, selecting quantization and parallelism, serving via /v1 endpoints, measuring serving throughput or latency, tuning batching, or diagnosing GPU, OOM, or startup failures in a vLLM deployment. Do not use for model training, fine-tuning, evaluation-set design, or engine-selection methodology (that is ml-engineering), or for operating the llama.cpp stack with GGUF models (that is llama-cpp); other inference engines (TGI, Ollama, Triton) are out of scope.
Inference 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
inferenceSGLang operator reference suite — HiCache (three-tier hierarchical prefix cache: GPU HBM → host DRAM → distributed L3 via Mooncake/3FS/NIXL/AIBrix/EIC/SiMM/file/LMCache) and the SGLang Model Gateway (`sgl-model-gateway`, formerly `sgl-router`) — Rust router fronting vLLM/SGLang inference workers on Kubernetes with cache-aware/prefix-hash/consistent-hash routing, K8s label-selector service discovery, PD-disaggregation, and `--enable-mesh` CRDT sync between gateway replicas. Pairs with the `vllm` suite for vLLM-side reference and `inference-cache` for engine-agnostic KV-cache transport.
inferenceExpert FinOps guidance for cloud, AI, SaaS and data-platform spend. Ask Claude how to reduce a bill, size a commitment, allocate shared costs or justify AI spend, and it answers with billing mechanics, decision rules and worked examples, not generic advice. Cloud: AWS, Azure, GCP, OCI, Kubernetes and on-premises or hybrid estates, including Savings Plans, Reserved Instances, Azure Hybrid Benefit and contract negotiation. AI: Anthropic, Bedrock, Azure OpenAI, Vertex AI, open-weight model APIs, AI coding tools, agent costs, self-hosted vs managed inference. Data platforms: Databricks, Snowflake, Microsoft Fabric. FinOps practice: allocation, showback and chargeback, forecasting, operating model, FOCUS multi-cloud cost data, tagging, anomalies, KPIs, workload onboarding, SaaS and IT asset management, GreenOps. Waste playbooks: 44 runbooks for waste patterns such as idle NAT gateways, orphaned disks, snapshot sprawl, unused reservations, oversized GPUs and coding-agent token waste. Each gi
NVIDIA 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.
inferenceDrop a photo in Claude Code and get a video. Self-hosted LTX-2.3 (22B): text-to-video, image-to-video, keyframe interpolation, video-to-video, and IC-LoRA canny/depth/pose control, with synced audio. Deploys to your own Modal serverless GPU — a few cents a clip, no per-clip meter.
creative
