Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Expert in Model Context Protocol (MCP) server development. Use when building MCP servers, creating tools for Claude, implementing resources, debugging MCP connections, or integrating databases with Claude Code.
An easy way to force agent to think again if it's statement that the "Job is done and production ready" is actually done - usually it's not. This skill ensures that that the AI agent verifies their task and user does not have to check after the agent if they did their job.
Gestisci progetti di qualsiasi dimensione con contesto infinito e consumo minimo di token. Pattern ispirato a RLM (Recursive Language Models).
This skill should be used when the user asks to "create an AI agent", "build a multi-agent system", "implement agent handoffs", "add guardrails to agents", "create function tools for agents", "implement agent tracing", "build agentic workflows", "create a triage agent", "implement input/output guardrails", "add tools to agents", or mentions OpenAI Agents SDK, agents, handoffs, guardrails, Runner, or multi-agent orchestration.
Production deployment and operationalization of AI agents on Databricks. Use when deploying agents to Model Serving, setting up MLflow logging and tracing for agents, implementing Agent Evaluation frameworks, monitoring agent performance in production, managing agent versions and rollbacks, optimizing agent costs and latency, or establishing CI/CD pipelines for agents. Covers MLflow integration patterns, evaluation best practices, Model Serving configuration, and production monitoring strategies.
Persistent context/memory management MCP server. Stores and retrieves context across AI sessions.
Use when making architectural or business logic decisions during conversations - adds entry to DECISIONS.md
Use when "LangChain", "LLM chains", "ReAct agents", "tool calling", or asking about "RAG pipelines", "conversation memory", "document QA", "agent tools", "LangSmith"
Guidelines for selecting appropriate AI model (Sonnet vs Haiku) based on task complexity, ensuring cost efficiency while maintaining quality. Use when assigning work.
LangGraph 패턴 및 이 Coding Agent 프로젝트의 아키텍처 지식. "LangGraph", "노드", "상태", "워크플로우" 관련 질문 시 자동 로드.
Generates FPF-compliant Agent Skills from the FPF Specification (Strict E.8 format).
Generate realistic AI voices with ElevenLabs - create speech, clone voices, and manage audio projects
Use when "writing prompts", "prompt optimization", "few-shot learning", "chain of thought", or asking about "RAG systems", "agent workflows", "LLM integration", "prompt templates"
Use when creating new Claude Code skills, setting up marketplace repositories, or packaging skills for distribution - complete workflow from skill creation to marketplace publication
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Create custom sub-agents for Claude Code. Use when creating a new agent in .claude/agents/ for specialized tasks like exploration, verification, implementation, or API lookups.
Fetch and explain Claude Code changelog for specific versions. Use when users ask about Claude Code updates, version changes, new features, bug fixes, or want to understand what changed between versions. Triggers on queries like "What's new in Claude Code X.Y.Z?", "Claude Code changelog", "What changed in the latest version?", or "Explain Claude Code updates".
Prepare optimal context package before delegating tasks to sub-agents
効果的なスキルを作成するためのガイド。ユーザーが専門知識、ワークフロー、ツール統合でClaudeの能力を拡張する新しいスキルを作成（または既存のスキルを更新）したい場合に使用します。
Build Claude agents using TypeScript with the @anthropic-ai/claude-agent-sdk. Use this skill when implementing conversational agents, building tools for agents, setting up streaming responses, or debugging agent implementations. Covers the tool wrapping pattern, SDK initialization, agent architecture, and best practices.
