What is rag-architecture?

A guide to building retrieval-augmented generation, or RAG, systems that find relevant information and add it to a language model's prompt before generating an answer.

Use it to design document search pipelines, embeddings, vector retrieval, and answers based on your own data.

A model may lack access to private, current, or project-specific information. RAG supplies supporting data at query time to make answers more grounded.

rag-architecture — Skill for Claude Code and Codex by fabioc-aloha

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add fabioc-aloha/Alex_Skill_Mall --skill rag-architecture

Clone the repo

git clone --depth 1 https://github.com/fabioc-aloha/Alex_Skill_Mall

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/rag-architecture/github.svg)](https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/rag-architecture)

Your own site

<a href="https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/rag-architecture"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/rag-architecture/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/rag-architecture"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/rag-architecture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,275 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00020	$0.03275
Opus 5.5	$0.00008	$0.01310
Sonnet 5.5	$0.00004	$0.00655
Haiku 4.5	$0.00002	$0.00328

Measured 2mo ago against content hash f1bac2801708, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

rag-architecture scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/ai-agents/rag-architecture/skills/rag-architecture/SKILL.md · 426 lines

How it starts

The opening of the file, as written. The whole thing — 426 lines — stays where its author put it; the contents beside it link to each section on GitHub.

RAG Architecture Skill

Build retrieval-augmented generation systems that ground LLMs in your data.

Last validated: April 2026 (text-embedding-3 family, contextual retrieval patterns)

Core Principle

RAG = Retrieval + Generation. Instead of relying solely on the model's training data, retrieve relevant context at query time and include it in the prompt. This reduces hallucination and enables access to private/current data.

RAG Pipeline

┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Query     │────▶│   Embed     │────▶│  Retrieve   │────▶│   Augment   │
│  "How do I  │     │  Query to   │     │  Top-K      │     │  Add to     │
│   deploy?"  │     │  Vector     │     │  Documents  │     │  Prompt     │
└─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘
                                                                   │
                                                                   ▼
┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Answer    │◀────│  Generate   │◀────│   Format    │◀────│  Context    │
│  Grounded   │     │  With LLM   │     │   Prompt    │     │  + Query    │
│  Response   │     │             │     │             │     │             │
└─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘

Indexing Pipeline

Document Processing

┌──────────────┐     ┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│    Load      │────▶│    Clean     │────▶│    Chunk     │────▶│    Embed     │
│  Documents   │     │  & Parse     │     │   Content    │     │   Chunks     │
└──────────────┘     └──────────────┘     └──────────────┘     └──────────────┘
                                                                      │
                                                                      ▼
                                                               ┌──────────────┐
                                                               │    Store     │
                                                               │  in Vector   │
                                                               │     DB       │
                                                               └──────────────┘

Read the full file on GitHub · 426 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 426 lines · 20 tokens per session scan A f1bac2801708

Subscribe to this mod's changes

rag-architecture is a skill published in the GitHub repository fabioc-aloha/Alex_Skill_Mall (5 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 3,275 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Data and AI RAG and vectors agentic-ai ai-assistant ai-skills best-practices github-copilot knowledge-base

rag-architecture next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
rag-architecturefabioc-aloha/Alex_Skill_Mall	Skill	5	yesterday	MIT	A	20	Claude Code, Codex
world-model-diagnosticrjmurillo/ai-agents	Skill	49	yesterday	MIT	A	94	Claude Code
ai-orchestration-langchainagents-inc/skills	Skill	26	11d ago	MIT	A	43	Claude Code, Codex
ai-orchestration-llamaindexagents-inc/skills	Skill	26	11d ago	MIT	A	37	Claude Code, Codex
ai-orchestration-vercel-ai-sdkagents-inc/skills	Skill	26	11d ago	MIT	A	38	Claude Code, Codex
ai-provider-cohere-sdkagents-inc/skills	Skill	26	11d ago	MIT	A	40	Claude Code, Codex
api-vector-db-pineconeagents-inc/skills	Skill	26	11d ago	MIT	A	31	Claude Code, Codex