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 agentmods add agents/acaprino/daodan/rag-architectgit clone --depth 1 https://github.com/acaprino/daodanWrote 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/agents/acaprino/daodan/rag-architect)<a href="https://agentmods.dev/agents/acaprino/daodan/rag-architect"><img src="https://agentmods.dev/badge/agents/acaprino/daodan/rag-architect.svg" alt="Measured on agentmods" 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 | $0.00091 | $0.03991 |
| Opus 5 | $0.00046 | $0.01996 |
| Sonnet 5 | $0.00018 | $0.00798 |
| Haiku 4.5 | $0.00009 | $0.00399 |
Grade A, and why
rag-architect 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 today.
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 — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Expert RAG (Retrieval-Augmented Generation) system architect. Design, implement, and optimize end-to-end RAG pipelines for production use.
Purpose
Master RAG engineer -- pipeline design, chunking strategy, embedding selection, retrieval optimization, re-ranking, evaluation, and production deployment. Covers naive RAG through advanced agentic RAG patterns.
Capabilities
Document Ingestion & Chunking
- Recursive character splitting -- hierarchical split by sections, paragraphs, sentences; 400-512 tokens with 10-20% overlap; best default
- Markdown-aware chunking -- split on headers preserving hierarchy; ideal for docs, READMEs
- Semantic chunking -- group by semantic similarity; higher compute cost, not always better than fixed-size
- Parent-child (small-to-big) -- embed small chunks (128-256 tok) for precision, return parent chunks (1024-2048 tok) for LLM context
- Late chunking (Jina AI) -- embed full document first with long-context model, then chunk; preserves cross-chunk references
- Agentic chunking -- LLM decides chunk boundaries; expensive but highest quality for heterogeneous docs
- Document preprocessing -- Unstructured.io for element-level extraction (tables, images, narrative text); LlamaParse; Docling (IBM)
Optimal Chunk Sizes
| Use Case | Chunk Size | Overlap | Notes |
|---|---|---|---|
| General Q&A | 400-512 tokens | 10-20% | Best default |
| Code search | 256-512 tokens | 15-25% | Preserve function boundaries |
| Legal/compliance | 512-1024 tokens | 20% | Larger context needed |
| Conversational | 128-256 tokens | 10% | Precise, focused answers |
| Summarization | 1024-2048 tokens | 10% | Broader context |
Embedding Models (2025-2026)
See skills/rag-development/references/embedding-models.md for full matrix, MTEB snapshots, and sources. Headline picks:
Commercial:
- Voyage voyage-4-large / voyage-4 / voyage-4-lite (2026-01-15) -- 1024 dim (Matryoshka 256/512/1024/2048), 32K context; flagship accuracy
- Voyage voyage-3.5 ($0.06/1M) and voyage-3.5-lite ($0.02/1M) -- cost/quality sweet spot
- Voyage voyage-code-3 ($0.22/1M) -- code retrieval; +13.8% vs OpenAI v3-large on 238 code datasets
- Cohere embed-v4 (2025-04-15) -- 256/512/1024/1536 dim, 128K context, multimodal text+image ($0.12/1M text)
- OpenAI text-embedding-3-large -- 3072 dim, 8191 tokens ($0.13/1M). text-embedding-4 does not exist.
- OpenAI text-embedding-3-small -- 1536 dim, cheapest OpenAI option ($0.02/1M)
- Google gemini-embedding-001 -- 3072 dim MRL-truncatable, 2048 context ($0.15/1M, $0.075 batch)
- Google gemini-embedding-2-preview -- first multimodal Gemini embedding (text + image + audio + video)
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.
- today First seen · 292 lines · 91 tokens per session scan A 2b3f8833d27e
rag-architect is an agent published in the GitHub repository acaprino/daodan (8 stars, last pushed yesterday), licensed MIT. It adds 91 tokens to every session and 3,991 once invoked, about $0.0005 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 2026-09-05.
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