How do I install the AI LLM Skills Guide skill?

Run npx skillmds@latest add diegosouzapw/ai-llm-skills-guide in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Which AI agents work with AI LLM Skills Guide?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is AI LLM Skills Guide free to use?

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Who published AI LLM Skills Guide?

diegosouzapw (@diegosouzapw) published this skill. Their other Agent Skills are listed on their SkillMD profile.

AI LLM Skills Guide by diegosouzapw

Name: AI LLM Skills Guide
Rating: 4.5 (4 reviews)
Author: diegosouzapw

File contents

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.

---name: ai-llm-skills-guidedescription: 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## ScopeUse 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 Practices1. **Modular design**: Separate retrieval, generation, and orchestration2. **Evaluation**: Include benchmarks and test cases3. **Cost awareness**: Document token usage and API costs4. **Fallback strategies**: Handle API failures gracefully5. **Streaming**: Support streaming responses where possible## Full Resource ListFor 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.

Frequently asked questions

AI LLM Skills Guide