Guide for AI Agents and LLM development skills including RAG, multi-agent systems, prompt engineering, memory systems, and context engineering.
インストール
name: ai-llm-skills-guide
description: Guide for AI Agents and LLM development skills including RAG, multi-agent systems, prompt engineering, memory systems, and context engineering.
AI Agents & LLM Development Skills
Scope
Use this skill when:
Finding or adding AI/LLM related skills
Understanding agent architecture patterns
Working with RAG, embeddings, or vector databases
Implementing multi-agent systems
Key Skill Categories
Agent Frameworks
Framework
Description
LangGraph
Stateful, multi-actor AI applications
CrewAI
Role-based multi-agent orchestration
AutoGen
Microsoft's multi-agent framework
RAG (Retrieval-Augmented Generation)
Component
Skills
Embeddings
Text embedding models, chunking strategies
Vector DBs
Pinecone, Weaviate, Chroma, Qdrant
Retrieval
Hybrid search, reranking, context optimization
Observability & Tracing
Tool
Purpose
Langfuse
Open-source LLM observability
LangSmith
LangChain tracing and debugging
Weights & Biases
ML experiment tracking
Memory Systems
Type
Description
Short-term
Conversation buffer, sliding window
Long-term
Vector store persistence, entity memory
Episodic
Experience-based memory recall
Context Engineering Skills
Core Concepts
Context fundamentals: What context is and why it matters