Claude Code プラグイン
公開されている Claude Code marketplace からの 10 件のプラグインです。それぞれにスキル、スラッシュコマンド、エージェント、フック、MCP サーバーがまとめてインストールされます。
これらのフィルターに一致するプラグインを 10 件表示しています。 フィルターをクリア
AI/ML engineering skills for shipping AI features responsibly: Model Card, AI Eval Plan, RAG Design Doc, Agent Spec, Dataset Datasheet, AI Feature PRD, LLM Cost & Latency Budget, Prompt Optimizer, Eval Rubric Designer, RAG Architecture Review, LLM Guardrails Spec, Agent Design Review, and Model Selection Advisor.
Chaos testing for resilience with failure injection, latency simulation, and system resilience validation
Analyze network latency and optimize request patterns
Prompt engineering workbench with a Lean persona layer that cuts LLM output size, cost, and latency.
productivity
Search relevance testing for Elasticsearch, OpenSearch, Solr, vector and hybrid search: judgment lists and NDCG, MRR and recall@k regression checks. Builds judgment lists with rater guidelines, benchmarks vector recall against latency, and measures fusion lift in hybrid pipelines. A review agent (relevance-regression-reviewer) compares before and after metrics per query.
ai-ml
Operate an evaluated agent with tools and authority in production through a runtime control plane covering versioning, staged rollout, fallback, cost and latency budgets, tool health, human escalation, disablement, and trace-to-eval feedback. Do not use for building agents, designing evals, or general release, SRE, security, or platform engineering — those methods stay with their specialist skills.
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.
Performance engineering: frontend performance budgets and Core Web Vitals, backend latency, memory and resource profiling, caching, and Python performance.
Search 42,000+ curated agent skills with a natural-language description of your task; get ranked matches with install commands. Local FAISS index — no API calls, no latency.
Comprehensive guide for building high-performance Solana apps with MagicBlock Ephemeral Rollups. Use when you need sub-10ms latency, gasless transactions, or rollup architecture guidance.