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
• Context degradation: Lost-in-middle, poisoning, distraction patterns
• Context compression: Summarization, trimming strategies
• Context optimization: Caching, masking, compaction

Multi-Agent Patterns

• Orchestrator pattern
• Peer-to-peer collaboration
• Hierarchical delegation
• Tool-using agents

Where to Add in README

• Agent frameworks:

  AI Agents & LLM Development

• RAG tools:

  AI Agents & LLM Development

  or

  Data & Analysis

• Observability:

  AI Agents & LLM Development

• Context engineering:

  Context Engineering

Key Repositories

sickn33/antigravity-awesome-skills/skills/
├── langgraph/
├── crewai/
├── langfuse/
├── rag-engineer/
├── prompt-engineer/
├── voice-agents/
├── agent-memory-systems/
└── autonomous-agents/

muratcankoylan/Agent-Skills-for-Context-Engineering/skills/
├── context-fundamentals/
├── context-degradation/
├── context-compression/
├── multi-agent-patterns/
└── memory-systems/

Best Practices

1. Modular design: Separate retrieval, generation, and orchestration
2. Evaluation: Include benchmarks and test cases
3. Cost awareness: Document token usage and API costs
4. Fallback strategies: Handle API failures gracefully
5. Streaming: Support streaming responses where possible

Full Resource List

For more detailed skill resources, complete link lists, or the latest information, use WebFetch to retrieve the full README.md:

https://raw.githubusercontent.com/gmh5225/awesome-skills/refs/heads/main/README.md

The README.md contains the complete categorized resource list with all links.