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
- 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
- Modular design: Separate retrieval, generation, and orchestration
- Evaluation: Include benchmarks and test cases
- Cost awareness: Document token usage and API costs
- Fallback strategies: Handle API failures gracefully
- 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.
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