Agent skill · data analytics · sickn33
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.
Why this skill is useful
Adds executable scripts for RAG implementation and embeddings pipeline that significantly enhance LLM operational capabilities.
What it needs
Requires chromadb installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
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="./chromadb") def chunktext(text, chunksize=500, overlap=50): words = text.split() chunks = [] for i in range(0, len(words), chunksize - overlap): chunk = " ".join(words[i:i + chunksize]) if chunk: chunks.append(chunk) return chunks def indexdocument(docid, contenttext, metadata=None): chunks = chunktext(contenttext) ids = [f"{docid}chunk{i}" for i in range(len(chunks))] collection.upsert(ids=ids, documents=chunks) return len(chunks) Pipeline De Query Com Rag def ragquery(query, topk=5, system=None): results = collection.query( querytexts=[query], nresults=topk, include=["documents", "metadatas", "distances"]) contextparts = [] for doc, meta, dist in zip(results["documents"][0], results["metadatas"][0], results["distances"][0]): if dist < 1.5: src = meta.get("source", "doc") contextparts.append(f"[Fonte: {src}] {doc}") context = " --- ".join(contextparts) response = client.m …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/llm-ops