rag-and-vector-search-engineer

rag-and-vector-search-engineer is an agent for Claude Code from FluxonLab/Skillry. It costs 28 tokens per session (573 once invoked), scanned A, original, no licence file.

A RAG and vector-search engineering agent for retrieval-augmented generation, embeddings, vector search, ranking, and grounding. RAG is a way to give an AI relevant source material before it answers.

In plain words
What is it for?
Use it to implement or review document retrieval, embeddings, vector indexes, result ranking, and answer grounding.
Why use it?
It helps build or review systems that find relevant information and use it to support AI responses.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter.

Part of the skillry-optional-specialist plugin — 9 skills, 37 agents shipped together

Good fit Use it to implement or review document retrieval, embeddings, vector indexes, result ranking, and answer grounding.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/fluxonlab/skillry/rag-and-vector-search-engineer
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.

Clone the repo
git clone --depth 1 https://github.com/FluxonLab/Skillry

Made for: Claude Code.

Or install skillry-optional-specialist, the plugin that ships this one along with the rest of its 9 skills, 37 agents.

Wrote 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.

agentmods badge for rag-and-vector-search-engineer

README.md
[![agentmods](https://agentmods.dev/badge/agents/fluxonlab/skillry/rag-and-vector-search-engineer/github.svg)](https://agentmods.dev/agents/fluxonlab/skillry/rag-and-vector-search-engineer)
Your own site
<a href="https://agentmods.dev/agents/fluxonlab/skillry/rag-and-vector-search-engineer"><img src="https://agentmods.dev/badge/agents/fluxonlab/skillry/rag-and-vector-search-engineer/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.

agentmods 80×15 button for rag-and-vector-search-engineer

Your own site · 80×15
<a href="https://agentmods.dev/agents/fluxonlab/skillry/rag-and-vector-search-engineer"><img src="https://agentmods.dev/badge/agents/fluxonlab/skillry/rag-and-vector-search-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 28 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 573 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown 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 5.1 $0.00028 $0.00573
Opus 5.5 $0.00011 $0.00229
Sonnet 5.5 $0.00006 $0.00115
Haiku 4.5 $0.00003 $0.00057

Measured 26d ago against content hash 5ea6902811ab, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

rag-and-vector-search-engineer 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 26d 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.

plugins/optional-specialist/agents/rag-and-vector-search-engineer.md · 64 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

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. 26d ago First seen · 64 lines · 28 tokens per session scan A 5ea6902811ab

Subscribe to this mod's changes

rag-and-vector-search-engineer is an agent published in the GitHub repository FluxonLab/Skillry (6 stars, last pushed 23d ago), with no licence file. It adds 28 tokens to every session and 573 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 2026-09-20.

Related

Other agents, from other repositories

wiki-qa-probe

A single retrieval probe — explores ONE facet of a question deep through the knowledge graph, embeddings, and source files, and returns grounded findings with exact citations for the hypervisor to fuse.

bearlike/Assistant · 43 tokens

langchain-expert

Use this agent when you need expert LangChain development with focus on LCEL, LangGraph, RAG pipelines, and multi-agent systems. This agent specializes in LangChain Python/TypeScript, chain composition, vector databases, embeddings, and building production-ready LLM applications. Examples: Context: User needs to build…

andisab/swe-marketplace · 400 tokens

ai-platform-architect

Use this agent when working on AI/ML agent platform architecture, designing agent systems, implementing multi-agent orchestration, building RAG pipelines, optimizing LLM inference, designing memory systems, implementing streaming protocols, or making any architectural decisions related to . This includes agent…

asiflow/claude-nexus-hyper-agent-team · 778 tokens

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.

affaan-m/ECC · 58 tokens

langchain4j-ai-development-expert

Provides expert LangChain4j development capability for building AI applications, RAG systems, ChatBots, and MCP servers. Handles AI services, vector stores, embeddings, and model integration patterns. Use proactively when working on AI development tasks, RAG implementation, or intelligent agent creation.

giuseppe-trisciuoglio/developer-kit · 65 tokens

scg-mapper

Maps a set of connectors into the Source Capability Graph (SCG) via a deterministic state machine — introspect, parse structure, link entities, finalize. Indexes reachability only, never the data behind it.

bearlike/Assistant · 48 tokens