ai-llm-skills-guide

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

RAG (Retrieval-Augmented Generation)

Observability & Tracing

Memory Systems

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