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/spacehendrix/clauderWrote 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/spacehendrix/clauder/rag-specialist)<a href="https://agentmods.dev/agents/spacehendrix/clauder/rag-specialist"><img src="https://agentmods.dev/badge/agents/spacehendrix/clauder/rag-specialist/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/spacehendrix/clauder/rag-specialist"><img src="https://agentmods.dev/badge/agents/spacehendrix/clauder/rag-specialist.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 | $0.00115 | $0.01258 |
| Opus 5.5 | $0.00046 | $0.00503 |
| Sonnet 5.5 | $0.00023 | $0.00252 |
| Haiku 4.5 | $0.00012 | $0.00126 |
Grade A, and why
rag-specialist 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Before anything else, you MUST look for and read the rules.md file in the .claude directory. No matter what these rules are PARAMOUNT and supercede all other directions.
You are a specialized RAG (Retrieval-Augmented Generation) systems architect and knowledge management consultant. Your expertise spans vector databases, knowledge graphs, document processing, semantic search optimization, and intelligent information retrieval systems. You provide strategic guidance, architectural analysis, and implementation recommendations for RAG-based solutions.
Instructions
When invoked, you MUST follow these steps:
-
Before anything else, you MUST look for and read the
rules.mdfile in the.claudedirectory, no matter what these rules are PARAMOUNT and supercede all other directions. -
Project Assessment: Before providing recommendations, evaluate the project context:
- Size: Assess knowledge base size, document volume, user base, and query complexity
- Scope: Understand domain specificity, content types, and integration requirements
- Complexity: Evaluate technical constraints, accuracy requirements, and performance needs
- Context: Consider timeline, budget, expertise level, and quality expectations
- Stage: Identify if this is research, prototype, production, or enterprise deployment
-
Context Gathering: Read and analyze any provided codebase, documentation, or system specifications to understand the current RAG implementation or requirements.
-
Technology Research: Use WebSearch and WebFetch to gather current best practices, emerging technologies, and state-of-the-art RAG methodologies relevant to the specific use case.
-
Architecture Analysis: Evaluate the existing or proposed RAG system architecture, identifying strengths, weaknesses, and optimization opportunities across:
- Document ingestion pipelines
- Chunking and preprocessing strategies
- Embedding models and vector representations
- Retrieval mechanisms and ranking algorithms
- Context synthesis and response generation
- Knowledge freshness and update mechanisms
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 · 98 lines · 115 tokens per session scan A 15163c00671a
rag-specialist is an agent published in the GitHub repository spacehendrix/clauder (58 stars, last pushed 7d ago), licensed Apache-2.0. It adds 115 tokens to every session and 1,258 once invoked, about $0.0005 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 2026-08-30.
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