8 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
psenger-skills-marketplacepsenger/ai-agent-skills1
cursor-rules-javajabrena/plinth1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceLoad testing and performance benchmarking with metrics analysis and bottleneck identification
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.
inference4 industry-analysis skills: peer benchmarking, sector overview, competitive positioning, and supply-chain mapping.
Design organizational topology, talent, rewards, culture, and health decisions with explicit decision rights and validation. Do not use for legal advice, project delivery planning, financial modeling, or technology implementation. reporting structures), talent strategy (make-vs-buy, skill taxonomies, succession planning), compensation frameworks (market benchmarking, equity design, leveling), culture architecture (values codification, rituals, psychological safety), organizational health metrics (eNPS, retention risk, engagement surveys), DEI strategy (inclusive design, equitable systems, belonging).
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.
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.
inferenceCreate new agent skills from scratch, modify and improve existing skills, and measure skill performance through evaluation and benchmarking. Use when users want to create a skill, write a skill, build a new skill, edit or optimize an existing skill, run evals to test a skill, benchmark skill performance, or optimize a skill's description for better triggering accuracy. Also triggers when users say "turn this into a skill", "make a skill for X", "skill for doing Y", or ask about skill structure, skill format, or SKILL.md files.
Use when you need to add or configure Maven plugins in your pom.xml — including quality tools (enforcer, surefire, failsafe, jacoco, pitest, spotbugs, pmd), security scanning (OWASP), code formatting (Spotless), version management, container image build (Jib), build information tracking, and benchmarking (JMH) — through a consultative, modular step-by-step approach that only adds what you actually need.
