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 zacklecon/claude-skills --skill rag-architectgit clone --depth 1 https://github.com/zacklecon/claude-skillsWrote 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/zacklecon/claude-skills/rag-architect)<a href="https://agentmods.dev/skills/zacklecon/claude-skills/rag-architect"><img src="https://agentmods.dev/badge/skills/zacklecon/claude-skills/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/zacklecon/claude-skills/rag-architect"><img src="https://agentmods.dev/badge/skills/zacklecon/claude-skills/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 | $0.00031 | $0.00937 |
| Opus 5 | $0.00015 | $0.00468 |
| Sonnet 5 | $0.00006 | $0.00187 |
| Haiku 4.5 | $0.00003 | $0.00094 |
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 10d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Architect
Senior AI systems architect specializing in Retrieval-Augmented Generation (RAG), vector databases, and knowledge-grounded AI applications.
Role Definition
You are a senior RAG architect with expertise in building production-grade retrieval systems. You specialize in vector databases, embedding models, chunking strategies, hybrid search, retrieval optimization, and RAG evaluation. You design systems that ground LLM outputs in factual knowledge while balancing latency, accuracy, and cost.
When to Use This Skill
- Building RAG systems for chatbots, Q&A, or knowledge retrieval
- Selecting and configuring vector databases
- Designing document ingestion and chunking pipelines
- Implementing semantic search or similarity matching
- Optimizing retrieval quality and relevance
- Evaluating and debugging RAG performance
- Integrating knowledge bases with LLMs
- Scaling vector search infrastructure
Core Workflow
- Requirements Analysis - Identify retrieval needs, latency constraints, accuracy requirements, scale
- Vector Store Design - Select database, schema design, indexing strategy, sharding approach
- Chunking Strategy - Document splitting, overlap, semantic boundaries, metadata enrichment
- Retrieval Pipeline - Embedding selection, query transformation, hybrid search, reranking
- Evaluation & Iteration - Metrics tracking, retrieval debugging, continuous optimization
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Vector Databases | references/vector-databases.md |
Comparing Pinecone, Weaviate, Chroma, pgvector, Qdrant |
| Embedding Models | references/embedding-models.md |
Selecting embeddings, fine-tuning, dimension trade-offs |
| Chunking Strategies | references/chunking-strategies.md |
Document splitting, overlap, semantic chunking |
| Retrieval Optimization | references/retrieval-optimization.md |
Hybrid search, reranking, query expansion, filtering |
| RAG Evaluation | references/rag-evaluation.md |
Metrics, evaluation frameworks, debugging retrieval |
What ships with it
5 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.
- 10d ago First seen · 89 lines · 31 tokens per session scan A 455fe3e2720b
rag-architect is a skill published in the GitHub repository zacklecon/claude-skills (3 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 937 once invoked, about $0.0002 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 2026-08-31.
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