Agent skill

RAG Company Knowledge Assistant

by Hermes-brasil in Hermes-brasil/hermes-brasil

Portuguese guide to building a retrieval-augmented generation assistant over a company's documents, with embeddings, section-based chunking, retrieval and a client workflow.

MITAuto-check passedAI & LLM Engineering

SKILL.md written in Portuguese; this summary is our English description.

Install RAG Company Knowledge Assistant

skills CLI

$ npx skills add Hermes-brasil/hermes-brasil --skill rag-assistente-conhecimento -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install Hermes-brasil/hermes-brasil rag-assistente-conhecimento --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/Hermes-brasil/hermes-brasil.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/rag-assistente-conhecimento .claude/skills/rag-assistente-conhecimento && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "rag-assistente-conhecimento" agent skill from https://github.com/Hermes-brasil/hermes-brasil/tree/main/skills/rag-assistente-conhecimento into .claude/skills/rag-assistente-conhecimento/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-assistente-conhecimento", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/Hermes-brasil/hermes-brasil/tree/main/skills/rag-assistente-conhecimento

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add Hermes-brasil/hermes-brasil --skill rag-assistente-conhecimento -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install Hermes-brasil/hermes-brasil rag-assistente-conhecimento --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Hermes-brasil/hermes-brasil.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/rag-assistente-conhecimento .agents/skills/rag-assistente-conhecimento && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "rag-assistente-conhecimento" agent skill from https://github.com/Hermes-brasil/hermes-brasil/tree/main/skills/rag-assistente-conhecimento into .agents/skills/rag-assistente-conhecimento/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-assistente-conhecimento", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add Hermes-brasil/hermes-brasil --skill rag-assistente-conhecimento -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install Hermes-brasil/hermes-brasil rag-assistente-conhecimento --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Hermes-brasil/hermes-brasil.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/rag-assistente-conhecimento .cursor/skills/rag-assistente-conhecimento && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "rag-assistente-conhecimento" agent skill from https://github.com/Hermes-brasil/hermes-brasil/tree/main/skills/rag-assistente-conhecimento into .cursor/skills/rag-assistente-conhecimento/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-assistente-conhecimento", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/Hermes-brasil/hermes-brasil.git --path skills/rag-assistente-conhecimento

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add Hermes-brasil/hermes-brasil --skill rag-assistente-conhecimento -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install Hermes-brasil/hermes-brasil rag-assistente-conhecimento --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Hermes-brasil/hermes-brasil.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/rag-assistente-conhecimento .gemini/skills/rag-assistente-conhecimento && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "rag-assistente-conhecimento" agent skill from https://github.com/Hermes-brasil/hermes-brasil/tree/main/skills/rag-assistente-conhecimento into .gemini/skills/rag-assistente-conhecimento/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-assistente-conhecimento", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install Hermes-brasil/hermes-brasil rag-assistente-conhecimento

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add Hermes-brasil/hermes-brasil --skill rag-assistente-conhecimento -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/Hermes-brasil/hermes-brasil.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/rag-assistente-conhecimento .github/skills/rag-assistente-conhecimento && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "rag-assistente-conhecimento" agent skill from https://github.com/Hermes-brasil/hermes-brasil/tree/main/skills/rag-assistente-conhecimento into .github/skills/rag-assistente-conhecimento/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-assistente-conhecimento", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add Hermes-brasil/hermes-brasil --skill rag-assistente-conhecimento -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install Hermes-brasil/hermes-brasil rag-assistente-conhecimento --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Hermes-brasil/hermes-brasil.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/rag-assistente-conhecimento .opencode/skills/rag-assistente-conhecimento && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "rag-assistente-conhecimento" agent skill from https://github.com/Hermes-brasil/hermes-brasil/tree/main/skills/rag-assistente-conhecimento into .opencode/skills/rag-assistente-conhecimento/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-assistente-conhecimento", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: rag-assistente-conhecimento
GitHub stars: 154
Token cost: ~1.1k tokens
SKILL.md length: 214 words
Files: 1
Skills in repo: 8
Repo updated: Oct 5, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Portuguese guide to building a retrieval-augmented generation assistant over a company's documents, with embeddings, section-based chunking, retrieval and a client workflow.

Building a chatbot that answers from a company's own FAQ and policies
SKILL.md covers Conceitos-chave, Embeddings, Chunking — lição importante and Busca semântica (retrieval), plus 4 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
Choosing how to split documents into chunks for retrieval

What it does

This skill explains RAG, a technique that lets an AI answer from external knowledge such as documents, FAQs and company databases instead of generic model knowledge. It lays out the pipeline of ingestion and indexing (splitting into chunks and generating embeddings), lists advanced techniques such as multi-query, RAG fusion and query translation, and covers embeddings and similarity measures: cosine by default, plus Euclidean and dot product.

A key lesson is to chunk by thematic sections, using the second-level headings and prefixing each chunk with its title, instead of by word count, which mixes topics and retrieves the wrong section. For Brazilian Portuguese it recommends a multilingual MiniLM sentence-transformers model, and the same model must embed both documents and questions. It adds a workflow for real clients (gather FAQs, policies and catalog, structure the content, test with real questions, define when to hand over to a human) and reference pricing for selling the work as a service. The text is in Portuguese.

When your agent uses it

Building a chatbot that answers from a company's own FAQ and policies
Choosing how to split documents into chunks for retrieval
Setting up local multilingual embeddings for Portuguese content

Example prompts

“Build a RAG assistant over our clinic's FAQ so it can answer questions about opening hours.”
“Chunk this policy document by sections and prefix each chunk with its heading.”
“Use the multilingual MiniLM model to embed Portuguese questions and return the best three chunks.”

Requirements

Python with sentence-transformers and NumPy
The client's documents, such as FAQ, policies and catalog

What it can do on your machine

Read from SKILL.md and the folder at commit 8f94818. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

RAG Company Knowledge Assistant loads about 1.1k tokens when it runs. Until then it costs about 19 tokens; SKILL.md has 214 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~19
When it runs · the whole SKILL.md, loaded when a task matches: ~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Hermes-brasil/hermes-brasil at commit 8f94818, republished under its MIT licence (© Hermes-brasil). 214 words, ~1,106 tokens.

Download SKILL.mdSave it as .claude/skills/rag-assistente-conhecimento/SKILL.md (or your agent's skills folder).

name: rag-assistente-conhecimento
description: Criar assistente RAG com conhecimento da empresa.

RAG — Assistente de Conhecimento (Retrieval-Augmented Generation)

O que é: técnica que permite IA acessar conhecimento EXTERNO (documentos, FAQ, banco de dados da empresa) para dar respostas precisas e baseadas nos dados do cliente — em vez de respostas genéricas do modelo.

Conceitos-chave

📋 PIPELINE DE RAG:
├── 1. INGESTÃO: carregar documentos
├── 2. INDEXAÇÃO: dividir em chunks + gerar embeddings
├── 3. RETRIEVAL: buscar chunks relevantes à pergunta
└── 4. GERAÇÃO: modelo responde usando o contexto recuperado

Técnicas avançadas (aplicar conforme necessidade)

├── Query Translation: melhorar a pergunta antes de buscar
│   ├── Multi-Query (várias versões da pergunta)
│   ├── RAG Fusion (combina resultados)
│   ├── Decomposition (quebra pergunta complexa)
│   ├── Step Back (pergunta mais geral)
│   └── HyDE (gera resposta hipotética para buscar)
├── Routing: direcionar pergunta ao modelo/fluxo certo
├── Indexing avançado: RAPTOR, ColBERT
├── CRAG (Conditional RAG): corrigir retrieval com base no grau de confiança
└── Adaptive RAG: decide quando buscar mais dados ou responder direto

Embeddings

Convertem texto em vetores numéricos (capturam significado)
Similaridade de COSSENO (padrão), Euclidiana, Dot Product
Modelos: OpenAI, Gemini, Cohere, open source (sentence-transformers)
pt-BR: paraphrase-multilingual-MiniLM-L12-v2 (entende português)
CRÍTICO: usar o MESMO modelo para documentos e perguntas

python

from sentence_transformers import SentenceTransformer
modelo = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2")
doc = modelo.encode("Horário de funcionamento da clínica")
pergunta = modelo.encode("A que horas abre?")

Chunking — lição importante

NÃO dividir por contagem de palavras (mistura assuntos e recupera seção errada). Dividir por SEÇÕES temáticas (títulos ##), cada seção vira um chunk com o título prefixado como contexto: f"{titulo}: {corpo}". Isso mantém cada chunk coerente e a busca fica muito mais precisa.

python

def chunking_por_secoes(texto):
    """Divide texto em seções (##) em vez de cortar por palavras."""
    secoes = []
    atual = {"titulo": "geral", "corpo": []}
    for linha in texto.splitlines():
        if linha.startswith("## "):
            if atual["corpo"]:
                secoes.append(atual)
            atual = {"titulo": linha[3:].strip(), "corpo": []}
        elif linha.strip():
            atual["corpo"].append(linha.strip())
    if atual["corpo"]:
        secoes.append(atual)
    return [f"{s['titulo']}: {' '.join(s['corpo'])}" for s in secoes if s["corpo"]]

Busca semântica (retrieval)

python

import numpy as np

def buscar(pergunta, chunk_embeddings, textos, top_k=3):
    q = modelo.encode(pergunta)
    scores = [np.dot(q, c) / (np.linalg.norm(q) * np.linalg.norm(c)) for c in chunk_embeddings]
    melhores = np.argsort(scores)[-top_k:][::-1]
    return [(textos[i], scores[i]) for i in melhores]

Fluxo de trabalho (para cliente real)

⚙️ IMPLEMENTAÇÃO:
├── 1. Levantar os documentos do cliente (FAQ, políticas, catálogo)
├── 2. Estruturar o conteúdo (seções claras)
├── 3. Rodar chunking + embeddings
├── 4. Testar busca com perguntas reais
├── 5. Conectar geração (API do modelo)
├── 6. Integrar ao canal (WhatsApp/site)
├── 7. Testar respostas e ajustar
├── 8. Definir limite (quando passar para humano)
└── 9. Manutenção e atualização do conhecimento

Venda como serviço (preços de referência)

💰 PRECIFICAÇÃO:
├── Chatbot básico: R$ 1.000-3.000
├── Assistente RAG completo: R$ 3.000-8.000
├── Assistente avançado (multi-função): R$ 8.000-15.000
├── Manutenção mensal: R$ 500-2.000
└── Licença/uso recorrente: R$ 300-1.500/mês

Pitfalls

Chunking por seção, não por palavra (crítico para qualidade)
Embeddings locais = busca sem custo; usar modelo multilingue para pt-BR
Testar sempre com perguntas reais do cliente
Definir limite claro de quando passar para atendimento humano
Proteger dados sensíveis (LGPD) — não vazar informações confidenciais
Atualizar o conhecimento periodicamente
Transparência: avisar que é assistente de IA

Quando usar

Cliente pede chatbot/assistente inteligente
Criar produto/ebook de RAG (tema com demanda)
Quer diferenciar de chatbot genérico

Files

Just SKILL.md in skills/rag-assistente-conhecimento of Hermes-brasil/hermes-brasil.

Open the folder on GitHubat commit 8f94818

Compare with similar skills

RAG Company Knowledge Assistant next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

RAG Company Knowledge Assistant compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
RAG Company Knowledge Assistant this skillHermes-brasil/hermes-brasil	154	—	~1.1k	Automated safety check: Pass	MIT	5 days ago
FAISS Similarity SearchOrchestra-Research/AI-Research-SKILLs	13k	6 repos	~1.3k	Automated safety check: Pass	MIT	3 mo ago
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs	13k	7 repos	~2.3k	Automated safety check: Pass	MIT	3 mo ago
Sentence Transformers EmbeddingsOrchestra-Research/AI-Research-SKILLs	13k	2 repos	~1.6k	Automated safety check: Pass	MIT	3 mo ago
Retail Product Search Agentgoogle/adk-recipes	10k	—	~3k	Automated safety check: Pass	Apache-2.0	today
DBoracle/skills	877	—	~1.4k	Automated safety check: Pass	UPL-1.0	yesterday

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Questions about RAG Company Knowledge Assistant

What does RAG Company Knowledge Assistant do?

Portuguese guide to building a retrieval-augmented generation assistant over a company's documents, with embeddings, section-based chunking, retrieval and a client workflow. This skill explains RAG, a technique that lets an AI answer from external knowledge such as documents, FAQs and company databases instead of generic model knowledge. It lays out the pipeline of ingestion and indexing (splitting into chunks and generating embeddings), lists advanced techniques such as multi-query, RAG fusion and query translation, and covers embeddings and similarity measures: cosine by default, plus Euclidean and dot product.

When should I use RAG Company Knowledge Assistant?

RAG Company Knowledge Assistant fits situations like: building a chatbot that answers from a company's own FAQ and policies; choosing how to split documents into chunks for retrieval; setting up local multilingual embeddings for Portuguese content.

How do I install RAG Company Knowledge Assistant in Claude Code?

Run `npx skills add Hermes-brasil/hermes-brasil --skill rag-assistente-conhecimento -a claude-code`. Or copy the skill folder (skills/rag-assistente-conhecimento in Hermes-brasil/hermes-brasil) into .claude/skills/rag-assistente-conhecimento in your project. Claude Code loads it when a task matches its description.

How do I install RAG Company Knowledge Assistant in Codex?

Run `npx skills add Hermes-brasil/hermes-brasil --skill rag-assistente-conhecimento -a codex`. Or copy the skill folder (skills/rag-assistente-conhecimento in Hermes-brasil/hermes-brasil) into .agents/skills/rag-assistente-conhecimento in your project. Codex loads it when a task matches its description.

Can I use RAG Company Knowledge Assistant in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Hermes-brasil/hermes-brasil --skill rag-assistente-conhecimento -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rag-assistente-conhecimento, .gemini/skills/rag-assistente-conhecimento, .github/skills/rag-assistente-conhecimento and .opencode/skills/rag-assistente-conhecimento in your project.

What does RAG Company Knowledge Assistant need to run?

SKILL.md names no scripts, command-line tools or credentials: RAG Company Knowledge Assistant is instructions for the agent only. Our summary lists: Python with sentence-transformers and NumPy; The client's documents, such as FAQ, policies and catalog.

Does RAG Company Knowledge Assistant access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is RAG Company Knowledge Assistant safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does RAG Company Knowledge Assistant use?

RAG Company Knowledge Assistant is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does RAG Company Knowledge Assistant use?

About 1.1k tokens (SKILL.md is roughly 4.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to RAG Company Knowledge Assistant?

Skills that share tags, products or a category with RAG Company Knowledge Assistant: FAISS Similarity Search (Orchestra-Research/AI-Research-SKILLs, 13k stars), Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Sentence Transformers Embeddings (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Retail Product Search Agent (google/adk-recipes, 10k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains RAG Company Knowledge Assistant?

Hermes-brasil (a GitHub organization) maintains it in Hermes-brasil/hermes-brasil, which has 154 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on October 5, 2026.

Source: Hermes-brasil/hermes-brasil on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install RAG Company Knowledge Assistant

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

RAG — Assistente de Conhecimento (Retrieval-Augmented Generation)

Conceitos-chave

Técnicas avançadas (aplicar conforme necessidade)

Embeddings

Chunking — lição importante

Busca semântica (retrieval)

Fluxo de trabalho (para cliente real)

Venda como serviço (preços de referência)

Pitfalls

Quando usar

Files

Compare with similar skills

Similar skills

More from Hermes-brasil/hermes-brasil

Related

Questions about RAG Company Knowledge Assistant