From ai
32 production-grade GenAI design patterns covering content control (Logits Masking, Grammar, Style Transfer), RAG architecture, model capabilities (Chain of Thought, Adapter Tuning), reliability (LLM-as-Judge, Reflection, Prompt Optimization), agentic systems (Tool Calling, Multiagent Collaboration), deployment optimization (SLM, Prompt Caching, Inference Optimization), safety guardrails (Self-Check, Guardrails), RAG implementation (11 source types, chunking, vector stores), and LLMOps/AgentOps (maturity L0–2, Tool/Agent Registry, Memory Governance, LLM metrics, LLMSecOps). Also covers AI performance (GPU/CUDA, LLM inference) via PERF- references. Use when designing GenAI applications, choosing RAG strategies, implementing agent architectures, optimizing LLM reliability, or operating AI systems in production. For web AI (Vercel AI SDK, LangChain.js), use integrating-ai-web-apps. For GCP-specific ML (BigQuery ML, Vertex AI), use developing-google-cloud.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ai:designing-genai-patternsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
詳細な手順・ガイドラインは `INSTRUCTIONS.md` を参照してください。
INSTRUCTIONS.mdreferences/AGENTIC-SYSTEMS.mdreferences/CONTENT-CONTROL.mdreferences/DEPLOYMENT-OPTIMIZATION.mdreferences/MODEL-CAPABILITIES.mdreferences/OPS-AGENTOPS.mdreferences/OPS-API-DEPLOYMENT.mdreferences/OPS-DATA-ENGINEERING.mdreferences/OPS-EVALUATION.mdreferences/OPS-LLMOPS-GUIDE.mdreferences/OPS-MODEL-ADAPTATION.mdreferences/OPS-SCALING-INFRASTRUCTURE.mdreferences/OPS-SECURITY-GOVERNANCE.mdreferences/PERF-CHECKLIST.mdreferences/PERF-CUDA-ADVANCED.mdreferences/PERF-CUDA-FUNDAMENTALS.mdreferences/PERF-GPU-HARDWARE.mdreferences/PERF-INFERENCE-OPTIMIZATION.mdreferences/PERF-PYTORCH-TUNING.mdreferences/PERF-SCALING-FUTURE.md詳細な手順・ガイドラインは INSTRUCTIONS.md を参照してください。
npx claudepluginhub sumik5/sumik-llm-plugin --plugin aiCreates, edits, and optimizes skills for Claude Code, including drafting, evaluating with test prompts, iterating on performance, and improving skill descriptions for better triggering accuracy.