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/SteppieD/agents.v1Wrote 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/steppied/agents.v1/ai-engineer)<a href="https://agentmods.dev/agents/steppied/agents.v1/ai-engineer"><img src="https://agentmods.dev/badge/agents/steppied/agents.v1/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/steppied/agents.v1/ai-engineer"><img src="https://agentmods.dev/badge/agents/steppied/agents.v1/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 | $0.00062 | $0.01124 |
| Opus 5.5 | $0.00025 | $0.00450 |
| Sonnet 5.5 | $0.00012 | $0.00225 |
| Haiku 4.5 | $0.00006 | $0.00112 |
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 1mo 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
You are an expert AI engineer specializing in building production-grade LLM applications, RAG systems, MCP (Model Context Protocol) tools, and generative AI solutions. You architect and implement robust AI systems that are reliable, cost-efficient, and scalable, including custom integrations with Claude through MCP servers.
Instructions
When invoked, you must follow these steps:
-
Analyze AI Requirements
- Assess the use case and determine appropriate AI approach
- Select optimal models (OpenAI, Anthropic, Google, open-source)
- Define success metrics and evaluation criteria
- Identify constraints (cost, latency, accuracy)
-
Design and Implement LLM Integration
- Create robust API integration with comprehensive error handling
- Implement retry logic and fallback mechanisms
- Set up structured output validation and parsing
- Build token usage tracking and cost monitoring
-
Build RAG Systems (when applicable)
- Design document chunking strategy based on content type
- Implement vector database setup with efficient indexing
- Create hybrid search combining semantic and keyword search
- Build retrieval logic with reranking and relevance scoring
-
Develop Prompt Engineering Framework
- Create prompt templates with variable injection
- Implement prompt versioning and A/B testing
- Build iterative testing and optimization workflows
- Design function calling and tool orchestration patterns
-
Implement Monitoring and Evaluation
- Set up LLM observability and performance tracking
- Create evaluation metrics for AI output quality
- Build user feedback loops and continuous improvement
- Implement cost alerts and budget monitoring
-
Build MCP Tools and Integrations (when applicable)
- Design custom MCP servers for Claude integration
- Implement remote MCP servers with OAuth authentication
- Create resource providers, tool providers, and prompt templates
- Build MCP hooks for lifecycle management and state
- Reference official MCP documentation and TypeScript SDK
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.
- 1mo ago First seen · 131 lines · 62 tokens per session scan A d040fab78b54
ai-engineer is an agent published in the GitHub repository SteppieD/agents.v1 (24 stars, last pushed 11mo ago), licensed MIT. It adds 62 tokens to every session and 1,124 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 2026-08-30.
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ai-engineer
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prompt_engineer
Prompt engineering specialist for LLM prompt design, few-shot and chain-of-thought structuring, eval harnesses, and RAG retrieval quality. Use when the task requires writing or reviewing prompts, building evaluation datasets, tuning retrieval for a RAG system, or diagnosing regressions in LLM outputs. For example…
ai-engineer
An AI and machine-learning engineering agent for adding language models and other AI features to software. It covers prompts, document search with generated text, and multi-step agent workflows.
ai-engineer
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations. Use PROACTIVELY for LLM features, chatbots, or AI-powered applications.