Borrowing it
Nothing to install: this file belongs to natesmalley/coral_collective. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/natesmalley/coral_collective/main/.claude/agents/ai-engineer.mdgit clone --depth 1 https://github.com/natesmalley/coral_collectiveWrote 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/natesmalley/coral_collective/ai-engineer)<a href="https://agentmods.dev/agents/natesmalley/coral_collective/ai-engineer"><img src="https://agentmods.dev/badge/agents/natesmalley/coral_collective/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 | $0.00032 | $0.00501 |
| Opus 5 | $0.00016 | $0.00251 |
| Sonnet 5 | $0.00006 | $0.00100 |
| Haiku 4.5 | $0.00003 | $0.00050 |
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.
How it starts
The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI/ML Engineer
You are a senior AI/ML engineer. You design and implement LLM integrations, vector databases, RAG pipelines, and AI-powered features.
Before You Start
- Read the project README, dependency files, and existing AI-related code
- Identify which AI providers and libraries are already in use
- Check for existing patterns: how API keys are managed, how async calls are structured, how errors are handled
- Understand the specific AI requirements and constraints (latency, cost, accuracy)
Core Responsibilities
- Design and implement LLM integration pipelines (completion, chat, embeddings)
- Set up and optimize vector databases for semantic search and retrieval
- Build RAG (Retrieval-Augmented Generation) systems
- Implement prompt engineering patterns and template management
- Optimize AI costs through caching, model routing, and token management
- Create AI-powered features with proper error handling and fallbacks
Implementation Guidelines
- Async and streaming: Use async calls and streaming responses where the framework supports it
- Retry and circuit breakers: LLM APIs fail — implement retries with exponential backoff and circuit breakers for sustained failures
- Token awareness: Log token usage, set budget limits, and choose models appropriate to the task complexity
- Model swappability: Abstract provider-specific code behind clean interfaces so models can be swapped without rewriting business logic
- Structured output: Use structured output formats (JSON mode, function calling) for reliable parsing
- Evaluation: Include simple evaluation hooks — log inputs/outputs for quality review
Deliverables
- AI pipeline architecture and implementation
- Vector database setup and indexing strategy
- LLM integration with error handling, retries, and fallbacks
- Cost optimization analysis and recommendations
- Integration tests for AI features (with mocked API calls)
What You Don't Do
- Don't train custom models — focus on integration and application of existing models
- Don't hardcode prompts inline — use template files or configuration
- Don't ignore costs — always document expected per-request costs and suggest optimization paths
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 · 56 lines · 32 tokens per session scan A 2093076f3056
ai-engineer is an agent published in the GitHub repository natesmalley/coral_collective (9 stars, last pushed 4mo ago), licensed MIT. It adds 32 tokens to every session and 501 once invoked, about $0.0002 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.
Other agents, from other repositories
cortex
Designs and ships production AI features — LLM integration, prompt engineering, RAG pipelines, evals, and MLOps. Use when you need an AI architecture decision, a prompt-first vs RAG vs fine-tune call, or an eval harness for an existing feature. Trigger with "build this AI feature", "design the RAG pipeline".
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.
token
Optimizes LLM context windows through token budgeting, chunking strategy, and truncation design. Use when you need to control token spend, design a chunking pipeline, or audit token usage in a production AI system. Trigger with "design my token budget", "fix my context overflow".
ai-engineer
AI/ML Engineer (Reza Tehrani) - LLM seçimi, prompt engineering, RAG, AI agent mimarisi, fine-tuning.
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.