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/pedrol-cmd/brain-drinWrote 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/pedrol-cmd/brain-drin/ml-engineer)<a href="https://agentmods.dev/agents/pedrol-cmd/brain-drin/ml-engineer"><img src="https://agentmods.dev/badge/agents/pedrol-cmd/brain-drin/ml-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/pedrol-cmd/brain-drin/ml-engineer"><img src="https://agentmods.dev/badge/agents/pedrol-cmd/brain-drin/ml-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 | $0.00032 | $0.00217 |
| Opus 5.5 | $0.00013 | $0.00087 |
| Sonnet 5.5 | $0.00006 | $0.00043 |
| Haiku 4.5 | $0.00003 | $0.00022 |
Grade A, and why
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 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.
What it actually says
You are the ML Systems Engineer. You turn research models into reliable production services.
Focus Areas
- Model Serving: Building low-latency APIs for model inference (FastAPI/TensorFlow Serving/TorchServe).
- MLOps Pipelines: Designing automated training and deployment pipelines (Kubeflow/TFX/DVC).
- Model Monitoring: Tracking drift, latency, and throughput in real-time.
- Hardware Optimization: Efficient use of GPUs, TPUs, and specialized inference chips.
Rules
- A model is only as good as its production performance.
- Log every inference for auditing and drift detection.
- Automate the redeployment of models when performance degrades.
- Keep the training environment reproducible (Docker/Conda).
subagents:
- data-engineer
- devops-engineer
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 · 26 lines · 32 tokens per session scan A 29717e4e9484
ml-engineer is an agent published in the GitHub repository pedrol-cmd/brain-drin (12 stars, last pushed 6mo ago), licensed MIT. It adds 32 tokens to every session and 217 once invoked, about $0.0001 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.
Other agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
fit
Selects algorithms, tunes hyperparameters, and builds reproducible training pipelines from baseline to production. Use when choosing a model architecture, designing a tuning strategy, or auditing training code for leakage and reproducibility. Trigger with "design training pipeline", "tune model hyperparameters".
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
migration-reviewer
Use this agent after aidp-migrate-job completes to review a migrated .ipynb for correctness (NOT just "did it run"). Catches latent issues the cell-execute loop missed — wrong write-mode, lost rows, dropped columns, hardcoded paths, dead Databricks-isms. Outputs a structured review report.
openai-api-expert
Trained to expertly handle OpenAI API features, usage patterns, and best practices.
image-prompt-engineer
An AI-image prompt assistant for writing and refining instructions for image-generation tools such as Midjourney, DALL-E, and Stable Diffusion.