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.
npx skills add fabioc-aloha/Alex_Skill_Mall --skill rag-architecturegit clone --depth 1 https://github.com/fabioc-aloha/Alex_Skill_MallWrote 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.
[](https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/rag-architecture)<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.
<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>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 | $0.00010 | $0.01638 |
| Sonnet 5 | $0.00004 | $0.00655 |
| Haiku 4.5 | $0.00002 | $0.00328 |
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 8d 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.
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 │
└──────────────┘
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.
- 8d ago First seen · 426 lines · 20 tokens per session scan A f1bac2801708
rag-architecture is a skill published in the GitHub repository fabioc-aloha/Alex_Skill_Mall (4 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. 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.
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