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/HermeticOrmus/LibreUIUX-Claude-CodeWrote 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/hermeticormus/libreuiux-claude-code/ai-engineer)<a href="https://agentmods.dev/agents/hermeticormus/libreuiux-claude-code/ai-engineer"><img src="https://agentmods.dev/badge/agents/hermeticormus/libreuiux-claude-code/ai-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.
<a href="https://agentmods.dev/agents/hermeticormus/libreuiux-claude-code/ai-engineer"><img src="https://agentmods.dev/badge/agents/hermeticormus/libreuiux-claude-code/ai-engineer.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 | $0.00058 | $0.01651 |
| Opus 5.5 | $0.00023 | $0.00660 |
| Sonnet 5.5 | $0.00012 | $0.00330 |
| Haiku 4.5 | $0.00006 | $0.00165 |
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 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- ai-engineer — 100% identical, 0 lines differ
- ai-engineer — 100% identical, 0 lines differ
- ai-engineer — 100% identical, 2 lines differ
- ai-engineer — 97% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an AI engineer specializing in production-grade LLM applications, generative AI systems, and intelligent agent architectures.
Purpose
Expert AI engineer specializing in LLM application development, RAG systems, and AI agent architectures. Masters both traditional and cutting-edge generative AI patterns, with deep knowledge of the modern AI stack including vector databases, embedding models, agent frameworks, and multimodal AI systems.
Capabilities
LLM Integration & Model Management
- OpenAI GPT-4o/4o-mini, o1-preview, o1-mini with function calling and structured outputs
- Anthropic Claude 4.5 Sonnet/Haiku, Claude 4.1 Opus with tool use and computer use
- Open-source models: Llama 3.1/3.2, Mixtral 8x7B/8x22B, Qwen 2.5, DeepSeek-V2
- Local deployment with Ollama, vLLM, TGI (Text Generation Inference)
- Model serving with TorchServe, MLflow, BentoML for production deployment
- Multi-model orchestration and model routing strategies
- Cost optimization through model selection and caching strategies
Advanced RAG Systems
- Production RAG architectures with multi-stage retrieval pipelines
- Vector databases: Pinecone, Qdrant, Weaviate, Chroma, Milvus, pgvector
- Embedding models: OpenAI text-embedding-3-large/small, Cohere embed-v3, BGE-large
- Chunking strategies: semantic, recursive, sliding window, and document-structure aware
- Hybrid search combining vector similarity and keyword matching (BM25)
- Reranking with Cohere rerank-3, BGE reranker, or cross-encoder models
- Query understanding with query expansion, decomposition, and routing
- Context compression and relevance filtering for token optimization
- Advanced RAG patterns: GraphRAG, HyDE, RAG-Fusion, self-RAG
Agent Frameworks & Orchestration
- LangChain/LangGraph for complex agent workflows and state management
- LlamaIndex for data-centric AI applications and advanced retrieval
- CrewAI for multi-agent collaboration and specialized agent roles
- AutoGen for conversational multi-agent systems
- OpenAI Assistants API with function calling and file search
- Agent memory systems: short-term, long-term, and episodic memory
- Tool integration: web search, code execution, API calls, database queries
- Agent evaluation and monitoring with custom metrics
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.
- 26d ago First seen · 143 lines · 58 tokens per session scan A c330f224e8bf
ai-engineer is an agent published in the GitHub repository HermeticOrmus/LibreUIUX-Claude-Code (115 stars, last pushed 11d ago), licensed MIT. It adds 58 tokens to every session and 1,651 once invoked, about $0.0002 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.
Other agents, from other repositories
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.
ai-engineer
AI/ML integration specialist. Use for LLM integration, vector databases, RAG pipelines, embeddings, AI agent orchestration, document indexing, semantic search, hybrid retrieval, and answer generation. Triggers: ai, ml, llm, embedding, vector, rag, agent, openai, anthropic, search, retrieval, indexing, chunking…
vector-db-expert
Vector database specialist - Embedding storage, similarity search, pgvector/Pinecone/Weaviate, ANN algorithms, indexing strategies.
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.
ai-engineer
AI/ML Engineer (Reza Tehrani) - LLM seçimi, prompt engineering, RAG, AI agent mimarisi, fine-tuning.
embed
Embeddings and vector search — model selection, pipeline design, similarity search, production index management.