context-pack-format
รูปแบบการจัดเตรียม context ให้กระชับ มีโครงสร้าง และใช้ token อย่างมีประสิทธิภาพ
รูปแบบการจัดเตรียม context ให้กระชับ มีโครงสร้าง และใช้ token อย่างมีประสิทธิภาพ
Generate production-quality Python code for robot motion planning algorithms that run in Pyodide (browser). Creates educational code for teaching path planning (A*, RRT), trajectory optimization, PID control, model predictive control (MPC), and whole-body control. Focuses on algorithm implementation, visualization, and educational clarity.
Automatically invokes init-explorer agent when project context is empty or unknown.
Model Context Protocol server exposing AI Factory tools to Claude Desktop
Building LLM agents with LangChain and LangGraph, covering tool-calling model initialization, state management, and observability with LangSmith. Triggers: langchain, langgraph, langsmith, agent-executor, chat-model-tools.
Implement or adjust background transcription jobs for whisper-lolo. Use when wiring Inngest events, handling long-running jobs, chunking before transcription, persisting transcripts, or maintaining the TranscriptionProvider abstraction.
Implementing comprehensive logging, tracking, and audit trails for AI systems to ensure compliance and enable debugging.
Claude間のパイプライン用に最適化されたメタプロンプトを作成します。リサーチ、計画、実行の各段階を含む多段階ワークフローを設計します。複雑なタスクの段階的処理、Claude間の連携、構造化されたワークフローが必要な場合に使用してください。
Implement reusable embedding functions using Gemini embedding models via LangChain with proper error handling and batching for sitemap-crawled content.
Improves the RAG (Retrieval-Augmented Generation) chatbot for the Physical AI & Humanoid Robotics textbook with strict grounding, citation requirements, and performance optimization.
MoAI-ADK's foundational principles - TRUST 5, SPEC-First TDD, delegation patterns, token optimization, progressive disclosure, modular architecture, agent catalog, command reference, and execution rules for building AI-powered development workflows
Design, build, deploy, and troubleshoot AI agents using AWS Strands Agents Framework (PRIMARY), Google ADK v1.0, A2A (Agent-to-Agent) protocols, and AWS Bedrock AgentCore. Use when creating new agents, implementing agent communication patterns, configuring AgentCore runtime, setting up RAG pipelines, integrating with Gemini 3.0, debugging deployment issues, or optimizing agent performance.
Improve LLM prompts using prompt engineering best practices. Invoke when user wants to optimize prompts, improve slash commands, or apply prompt engineering.
Invoke Claude Code CLI from Python orchestrators and shell scripts. Use when asked to "spawn claude as subprocess", "automate claude cli", "run claude headless", "configure --allowedTools", "set up claude hooks", or "parallel claude invocation". Covers permissions, directory access (--add-dir), hooks, sandbox mode, and async patterns.
Phase 1 of assist workflow - Classify intent as CREATE, REFACTOR, or VERIFY
Gemini 3 Pro API/SDK integration for text generation, reasoning, and chat. Covers setup, authentication, thinking levels, streaming, and production deployment. Use when working with Gemini 3 Pro API, Python SDK, Node.js SDK, text generation, chat applications, or advanced reasoning tasks.
Use when reviewing OpenAI API usage, answering questions about OpenAI endpoints, or validating implementations against the official API specification. Provides expert knowledge on chat completions, embeddings, models, audio, assistants, batch processing, and moderations endpoints. Essential for code reviews involving OpenAI integrations and determining if features/parameters are part of the official API.
효과적인 SKILL을 제작하기 위한 가이드입니다. 특화된 지식, 워크플로우 또는 도구 통합을 통해 Claude의 기능을 확장하는 새로운 SKILL을 생성하거나 기존 SKILL을 업데이트하려는 경우 이 SKILL을 사용하세요.
RAG (Retrieval Augmented Generation) implementation patterns including document chunking, embedding generation, vector database integration, semantic search, and RAG pipelines. Use when building RAG systems, implementing semantic search, creating knowledge bases, or when user mentions RAG, embeddings, vector database, retrieval, document chunking, or knowledge retrieval.