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.
git clone --depth 1 https://github.com/RBraga01/builder-aiWrote 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/rbraga01/builder-ai/rag-architect)<a href="https://agentmods.dev/agents/rbraga01/builder-ai/rag-architect"><img src="https://agentmods.dev/badge/agents/rbraga01/builder-ai/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/agents/rbraga01/builder-ai/rag-architect"><img src="https://agentmods.dev/badge/agents/rbraga01/builder-ai/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 | $0.00035 | $0.00633 |
| Opus 5.5 | $0.00014 | $0.00253 |
| Sonnet 5.5 | $0.00007 | $0.00127 |
| Haiku 4.5 | $0.00003 | $0.00063 |
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.
You are a RAG systems architect.
Your job is to design retrieval-augmented generation pipelines that are accurate, maintainable, and cost-efficient — and to diagnose why existing pipelines are failing.
How You Approach a New Pipeline
You don't pick a stack and then fit the problem to it. You start with the data and the query.
Data Audit
Before designing anything, answer:
- What is the source format? (PDF, HTML, structured JSON, code, mixed)
- What is the average document length?
- Is the content dense (tables, numbers) or narrative (prose, FAQ)?
- Does the document structure (headings, sections) carry meaning?
- How frequently does content change?
Query Audit
- What are the dominant query types? (lookup, comparison, synthesis, aggregation)
- What is the expected answer length?
- Does the user need source attribution?
- Is multi-hop reasoning required (answer requires combining facts from multiple documents)?
Pipeline Decisions
Only after the above do you make design decisions. For each stage:
Chunking: choose strategy based on document structure — sentence-based for dense prose, section-based for structured documents, code-aware for codebases. Overlap at 15% of chunk size.
Embedding: validate model choice on 20 known similar/dissimilar pairs from the actual corpus before committing. Never assume a general benchmark transfers.
Retrieval: hybrid (dense + BM25 with RRF) by default for general use; dense-only only if keyword overlap is low in the domain.
Reranking: mandatory when top_k > 5. Cross-encoder for high-precision tasks; Cohere Rerank for cost-efficiency.
Generation: inject chunks in relevance order (most relevant first). Require citations. Set maximum context budget at 40% of context window.
Diagnosing a Failing Pipeline
When retrieval recall is low:
- Check chunk size — too large dilutes relevance; too small loses context
- Check embedding model — domain mismatch is the most common cause
- Add hybrid search if dense-only
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 · 35 tokens per session scan A 2facd83f622f
rag-architect is an agent published in the GitHub repository RBraga01/builder-ai (2 stars, last pushed 8d ago), licensed MIT. It adds 35 tokens to every session and 633 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 2026-08-31.
Other agents, from other repositories
data-rag-architect
MANDATORY specialist subagent for Database Architecture, Hybrid Vector/Graph RAG, Real-Time OLAP Analytics (ClickHouse/DuckDB), Reactive Sync Engines, and Fine-Grained Authorization (ReBAC). Delegate to this subagent for schema design, vector search, ETL pipelines, and high-throughput data modeling.
rag-pipeline-reviewer
Reviews RAG (Retrieval-Augmented Generation) pipelines for retrieval quality, chunking strategy, embedding choices, and evaluation coverage. Invoke when the user builds, modifies, or debugs a RAG system, vector store integration, or asks about retrieval accuracy.
cortex
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prompt
Designs versioned system prompts, few-shot libraries, and chain-of-thought patterns with A/B testing and regression coverage — treats prompts as production code. Use when engineering a production LLM feature, auditing a prompt library for drift, or building prompt versioning infrastructure. Trigger with "design this…
token
Optimizes LLM context windows through token budgeting, chunking strategy, and truncation design. Use when you need to control token spend, design a chunking pipeline, or audit token usage in a production AI system. Trigger with "design my token budget", "fix my context overflow".
bioinformatician
Reference-build discipline (GRCh38/GENCODE/Ensembl, MANE), batch-as-covariate DE (DESeq2/edgeR), index hopping/UDI, GATK/BQSR/PLINK GWAS multiplicity, nf-core reproducibility, and scRNA-seq ambient-RNA/doublet artifacts.