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 JoaoEquer/Oficina --skill rag-architectgit clone --depth 1 https://github.com/JoaoEquer/OficinaWrote 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/joaoequer/oficina/rag-architect)<a href="https://agentmods.dev/skills/joaoequer/oficina/rag-architect"><img src="https://agentmods.dev/badge/skills/joaoequer/oficina/rag-architect/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/joaoequer/oficina/rag-architect"><img src="https://agentmods.dev/badge/skills/joaoequer/oficina/rag-architect.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 | $0.00104 | $0.01117 |
| Opus 5.5 | $0.00042 | $0.00447 |
| Sonnet 5.5 | $0.00021 | $0.00223 |
| Haiku 4.5 | $0.00010 | $0.00112 |
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 1mo 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Architect
Design, tune, and evaluate production RAG pipelines with three deterministic tools. Run the tools against the actual corpus and requirements — do not pick chunk sizes or databases by intuition.
Hard rules
- Never present model names or vendor prices as current facts. Embedding models and vector-DB pricing rot in months. Recommend a tier (see table below), name a current-generation candidate, and tell the user to verify against the provider's live pricing page.
- Every design ends with an evaluation run. A RAG design without
retrieval_evaluator.pynumbers is a hypothesis, not a deliverable. - Chunking is corpus-driven. Run
chunking_optimizer.pyon the real documents before choosing a strategy.
Embedding model tiers (pattern, not price list)
| Tier | Current-generation examples (verify before use) | When |
|---|---|---|
| Fast / self-hosted | all-MiniLM-L6-v2, bge-small |
Cost-sensitive, small scale, real-time |
| Balanced open | all-mpnet-base-v2, bge-large, e5-large |
Quality without API dependency |
| Quality API | text-embedding-3-large, voyage-3-large |
Accuracy-priority general retrieval |
| Code | voyage-code-3, CodeBERT-family |
Code search corpora |
Pricing discipline: build the cost model with a placeholder table — columns model | $/1M tokens (verify) | dims | as-of date — and have the user fill in live numbers. Same for vector DBs (Pinecone/Weaviate/Qdrant/Chroma/pgvector): the selection criteria (managed vs self-hosted, scale, filtering, existing Postgres) are durable; the dollar figures are not.
Workflow
All paths relative to this skill folder. Outputs chain: corpus analysis → design → evaluation.
1. Analyze the corpus and pick chunking
python3 chunking_optimizer.py /path/to/docs --extensions .md .txt -o chunking.json
Emits chunking.json with corpus_info, per-strategy strategy_results, a recommendation, and sample_chunks. Use recommendation.strategy and its config; show the user 2-3 sample_chunks so they can sanity-check boundaries.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 72 lines · 104 tokens per session scan A 5cdf05a3ea56
rag-architect is a skill published in the GitHub repository JoaoEquer/Oficina (2 stars, last pushed 11d ago), licensed MIT. It adds 104 tokens to every session and 1,117 once invoked, about $0.0004 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 2026-09-05.
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