rag-specialist

A consultation agent for retrieval-augmented generation (RAG), a method that lets an AI find relevant information in stored documents before answering. It advises on knowledge bases, document processing, semantic search and related system design.

In plain words
What is it for?
Assessing RAG architecture, designing knowledge systems, choosing document-processing strategies, and improving semantic search.
Why use it?
It helps plan systems that need reliable answers from a collection of documents, without requiring you to work out the search and data-processing approach alone. It provides recommendations rather than changing code.

Agent for Claude Code

Install

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.

agentmods
npx agentmods add agents/spacehendrix/clauder/rag-specialist
Clone the repo
git clone --depth 1 https://github.com/spacehendrix/clauder

Made for: Claude Code.

Per session 115 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,258 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00115 $0.01258
Opus 5 $0.00057 $0.00629
Sonnet 5 $0.00023 $0.00252
Haiku 4.5 $0.00012 $0.00126

Measured 3d ago against content hash 15163c00671a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 3d 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.

.claude-expansion-packs/ai-dev/agents/rag-specialist.md · 98 lines

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:

  1. 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.

  2. 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
  3. Context Gathering: Read and analyze any provided codebase, documentation, or system specifications to understand the current RAG implementation or requirements.

  4. 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.

  5. 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

Read the full file on GitHub · 98 lines

Changes

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.

  1. 3d ago First seen · 98 lines · 115 tokens per session scan A 15163c00671a

Subscribe to this mod's changes

rag-specialist is an agent published in the GitHub repository spacehendrix/clauder (58 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 115 tokens to every session and 1,258 once invoked, about $0.0006 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 2026-08-30.

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