An agent for building applications that use large language models, including retrieval-augmented generation, multimodal AI, and other agents. Retrieval-augmented generation, or RAG, lets an AI use information retrieved from a document or data collection when answering.

It helps build LLM features, chatbots, RAG systems, multimodal applications, intelligent agents, and agent orchestration.

It helps with the engineering work needed to turn AI models into applications rather than isolated experiments. It covers retrieval, coordination between agents, and integrations for business use.

ai-engineer — Agent for Claude Code by aRustyDev

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/aRustyDev/dotfiles

Made for: Claude Code.

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.

README.md

[![agentmods](https://agentmods.dev/badge/agents/arustydev/dotfiles/ai-engineer.svg)](https://agentmods.dev/agents/arustydev/dotfiles/ai-engineer)

Your own site

<a href="https://agentmods.dev/agents/arustydev/dotfiles/ai-engineer"><img src="https://agentmods.dev/badge/agents/arustydev/dotfiles/ai-engineer.svg" alt="Measured on agentmods" height="20"></a>

Per session 58 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,649 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.

Model	Per session	Once invoked
Fable 5.1	$0.00058	$0.01649
Opus 5	$0.00029	$0.00825
Sonnet 5	$0.00012	$0.00330
Haiku 4.5	$0.00006	$0.00165

Measured 2d ago against content hash c5984ea4f7d2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

ai-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 2d 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.

.ai/plugins/llm-application-dev/agents/ai-engineer.md · 143 lines

The source is not reproduced here

No licence file

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.

2d ago First seen · 143 lines · 58 tokens per session scan A c5984ea4f7d2

Subscribe to this mod's changes

ai-engineer is an agent published in the GitHub repository aRustyDev/dotfiles (1 stars, last pushed 4mo ago), with no licence file. It adds 58 tokens to every session and 1,649 once invoked, about $0.0003 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 2026-09-04.

Building agents Data and AI RAG and vectors

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.

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qdrant-expert

Configure and operate the vector store in production. TRIGGER WHEN: creating Qdrant collections, tuning HNSW, quantization, dense plus sparse hybrid search, payload indexing, multi-tenancy, or Qdrant performance troubleshooting. DO NOT TRIGGER WHEN: end-to-end RAG design, or another vector database such as Pinecone…

acaprino/daodan · 91 tokens

FAI LangChain Expert

LangChain framework specialist — LCEL expression language, chains, agents with tool use, retrievers, memory, callbacks, LangSmith tracing, and production RAG pipeline patterns.

frootai/frootai · 41 tokens

rag-evaluator

Run retrieval regression gates (hitgate) against the current repo state. Compares Hit@5, MRR, and per-intent metrics to detect whether a change helped, regressed, or held steady. Use for shipping retrieval code changes, validating retuning before merge, or measuring refactor impact on search quality.

LucasSantana-Dev/sharekit · 68 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

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LLM integration specialist in RAG, embeddings, prompt engineering. Use PROACTIVELY for LLM features.

dotclaude/marketplace · 28 tokens