llm-ops
LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.
npx skills add majiayu000/claude-skill-registry --skill llm-ops-sickn33-antigravity-awesome --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LLM-OPS -- IA de Producao ## Overview LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao. Ativar para: implementar RAG, criar pipeline de embeddings, Pinecone/Chroma/pgvector, fine-tuning, prompt engineering, reducao de custos de LLM, evals, cache semantico, streaming, agents. ## When to Use This Skill - When you need specialized assistance with this domain ## Do Not Use This Skill When - The task is unrelated to llm ops - A simpler, more specific tool can handle the request - The user needs general-purpose assistance without domain expertise ## How It Works > A diferenca entre um prototipo de IA e um produto de IA e operabilidade. > LLM-Ops e a engenharia que torna IA confiavel, escalavel e economica. --- ## Arquitetura Rag Completa [Documentos] -> [Chunking] -> [Embeddings] -> [Vector DB] | [Query] -> [Embed query] -> [Semantic Search] -> [Top K chunks] | [LLM + Context] -> [Resposta] ## Pipeline De Indexacao from anthropic import Anthropic import chromadb client = Anthropic() chroma = chromadb.PersistentClient(path="./chroma_db") def chunk_text(text, chunk_size=500,
- Overview
- When to Use This Skill
- Do Not Use This Skill When
- How It Works
- Arquitetura Rag Completa
- Pipeline De Indexacao
- Pipeline De Query Com Rag
- Escolha Do Vector Db
- Pgvector
- Estrutura De Prompt De Elite
- Chain-Of-Thought
- Cache Semantico
- Estimativa De Custos Claude
- Framework De Avaliacao
What does the llm-ops skill do?
LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill llm-ops-sickn33-antigravity-awesome --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.