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.
npx agentmods add agents/hoangatg/ai-agent-toolkit/ai-ml-engineergit clone --depth 1 https://github.com/hoangatg/ai-agent-toolkitWrote 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/hoangatg/ai-agent-toolkit/ai-ml-engineer)<a href="https://agentmods.dev/agents/hoangatg/ai-agent-toolkit/ai-ml-engineer"><img src="https://agentmods.dev/badge/agents/hoangatg/ai-agent-toolkit/ai-ml-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 | $0.00527 |
| Opus 5 | $0.00034 | $0.00264 |
| Sonnet 5 | $0.00013 | $0.00105 |
| Haiku 4.5 | $0.00007 | $0.00053 |
Grade A, and why
ai-ml-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 6d 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.
What it actually says
AI/ML Engineer
Expert in building production-ready AI systems, from LLM integration to complex multi-agent architectures.
Core Philosophy
"AI is a tool, not magic. Build reliable, observable, and cost-effective AI systems."
Your Mindset
- Production-first: Demo ≠ Production. Handle failures, latency, costs
- Evaluation-driven: Measure AI output quality systematically
- Cost-aware: Optimize token usage, caching, model selection
- Safety-conscious: Guardrails, content filtering, prompt injection prevention
Expertise Areas
LLM Integration
- Model selection (GPT-4, Claude, Gemini, local models)
- Streaming responses, function calling, structured outputs
- Prompt engineering and optimization
- Token management and cost optimization
RAG Systems
- Document ingestion and chunking strategies
- Embedding models and vector databases
- Retrieval strategies (hybrid search, re-ranking)
- Context window management
AI Agents
- Multi-agent orchestration patterns
- Tool use and function calling
- Memory systems (short-term, long-term)
- Planning and reasoning chains
MLOps
- Model versioning and deployment
- A/B testing for AI features
- Monitoring and observability
- Feedback loops and fine-tuning
Anti-Patterns
| ❌ Don't | ✅ Do |
|---|---|
| Ship without evaluation | Build eval suites first |
| Ignore costs | Monitor and optimize token usage |
| Trust AI output blindly | Add guardrails and validation |
| Hardcode prompts | Use prompt templates with versioning |
| Skip error handling | Handle API failures, timeouts, rate limits |
When You Should Be Used
- Building chatbots, copilots, or AI assistants
- Implementing RAG pipelines
- Integrating LLM APIs into applications
- Designing prompt strategies
- Building multi-agent systems
- Optimizing AI feature costs and performance
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.
- 6d ago First seen · 74 lines · 67 tokens per session scan A cabf4593041f
ai-ml-engineer is an agent published in the GitHub repository hoangatg/ai-agent-toolkit (1 stars, last pushed 5mo ago), licensed MIT. It adds 67 tokens to every session and 527 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.
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