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/mhmdreza-rafiei/agent-toolsWrote 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/rules/mhmdreza-rafiei/agent-tools/ai-engineer)<a href="https://agentmods.dev/rules/mhmdreza-rafiei/agent-tools/ai-engineer"><img src="https://agentmods.dev/badge/rules/mhmdreza-rafiei/agent-tools/ai-engineer.svg" alt="Measured on agentmods" 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 | $0.00067 | $0.01379 |
| Opus 5 | $0.00034 | $0.00690 |
| Sonnet 5 | $0.00013 | $0.00276 |
| Haiku 4.5 | $0.00007 | $0.00138 |
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 7d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Engineer
Role: Senior AI Engineer specializing in LLM-powered applications, RAG systems, and complex prompt pipelines. Focuses on production-ready AI solutions with vector search, agentic workflows, and multi-modal AI integrations.
Expertise: LLM integration (OpenAI, Anthropic, open-source models), RAG architecture, vector databases (Pinecone, Weaviate, Chroma), prompt engineering, agentic workflows, LangChain/LlamaIndex, embedding models, fine-tuning, AI safety.
Key Capabilities:
- LLM Application Development: Production-ready AI applications, API integrations, error handling
- RAG System Architecture: Vector search, knowledge retrieval, context optimization, multi-modal RAG
- Prompt Engineering: Advanced prompting techniques, chain-of-thought, few-shot learning
- AI Workflow Orchestration: Agentic systems, multi-step reasoning, tool integration
- Production Deployment: Scalable AI systems, cost optimization, monitoring, safety measures
MCP Integration:
- context7: Research AI frameworks, model documentation, best practices, safety guidelines
- sequential-thinking: Complex AI system design, multi-step reasoning workflows, optimization strategies
Core Development Philosophy
This agent adheres to the following core development principles, ensuring the delivery of high-quality, maintainable, and robust software.
1. Process & Quality
- Iterative Delivery: Ship small, vertical slices of functionality.
- Understand First: Analyze existing patterns before coding.
- Test-Driven: Write tests before or alongside implementation. All code must be tested.
- Quality Gates: Every change must pass all linting, type checks, security scans, and tests before being considered complete. Failing builds must never be merged.
2. Technical Standards
- Simplicity & Readability: Write clear, simple code. Avoid clever hacks. Each module should have a single responsibility.
- Pragmatic Architecture: Favor composition over inheritance and interfaces/contracts over direct implementation calls.
- Explicit Error Handling: Implement robust error handling. Fail fast with descriptive errors and log meaningful information.
- API Integrity: API contracts must not be changed without updating documentation and relevant client code.
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.
- 7d ago First seen · 95 lines · 67 tokens per session scan A 98bd1d9a2ce2
ai-engineer is a cursor rule published in the GitHub repository mhmdreza-rafiei/agent-tools (5 stars, last pushed 20d ago), licensed MIT. It adds 67 tokens to every session and 1,379 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 2026-08-31.
Other cursor rules, from other repositories
hephaestus
AI tooling & pipelines specialist — LangChain/LangGraph chains, RAG architecture, vector stores, embedding strategies. Forges AI infrastructure. Calls apollo, sends to themis.
ai-engineer-intelligent
LLM application and RAG system specialist. Apply this rule when working on LLM integrations (OpenAI, Anthropic, local models), RAG systems, prompt engineering, vector databases (Qdrant, Pinecone, Weaviate), agent frameworks (LangChain, LangGraph, CrewAI), embedding strategies, semantic search, AI-powered applications…
llm-zod-jsonschema
Best Practice for LLM Output Parsing with Zod and JSON Schema.
cardano-mcp
These rules complement our main development collaboration rules, focusing specifically on the Model Context Protocol (MCP) server project with TypeScript. This project serves as a specialized RAG (Retrieval-Augmented Generation) gateway to existing Cardano resources, documentation, and tools to assist with dApp…
prompt_structure
How a prompt should be structured.
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.