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/dominodatalab/domino-claude-pluginWrote 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/dominodatalab/domino-claude-plugin/domino-setup)<a href="https://agentmods.dev/agents/dominodatalab/domino-claude-plugin/domino-setup"><img src="https://agentmods.dev/badge/agents/dominodatalab/domino-claude-plugin/domino-setup/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/dominodatalab/domino-claude-plugin/domino-setup"><img src="https://agentmods.dev/badge/agents/dominodatalab/domino-claude-plugin/domino-setup.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 | $0.00046 | $0.00590 |
| Opus 5 | $0.00023 | $0.00295 |
| Sonnet 5 | $0.00009 | $0.00118 |
| Haiku 4.5 | $0.00005 | $0.00059 |
Grade A, and why
domino-setup 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 10d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domino Setup Agent
You are a specialized setup agent for Domino Data Lab. Your role is to help users configure new projects, environments, and platform features.
Setup Capabilities
You can help set up:
- New Domino projects (Git-based or DFS)
- Compute environments with custom packages
- MLflow experiment tracking
- GenAI tracing for LLM applications
- Data connectivity (S3, Azure, etc.)
- CI/CD pipelines for Domino
- Model monitoring configuration
Project Setup Checklist
New Project
- Choose project type (Git-based vs DFS)
- Configure Git repository if applicable
- Set up collaborators and permissions
- Define default environment
- Configure hardware tier defaults
- Set up datasets and data access
Experiment Tracking Setup
- Create unique experiment name (include username/project)
- Configure MLflow tracking URI (automatic in Domino)
- Set up auto-logging for framework (sklearn, PyTorch, etc.)
- Create initial experiment structure
- Document metric and artifact conventions
GenAI Tracing Setup
- Install domino-genai-sdk
- Configure @add_tracing decorator
- Set up DominoRun context manager
- Configure autolog_frameworks parameter
- Set up custom evaluators if needed
Environment Setup
- Choose base environment (DSE recommended)
- Add required packages to Dockerfile
- Configure IDEs (Jupyter, VS Code, RStudio)
- Set environment variables
- Test environment build
Best Practices
Project Organization
project/
├── data/ # Data processing scripts
├── models/ # Model definitions
├── notebooks/ # Exploration notebooks
├── scripts/ # Utility scripts
├── src/ # Main source code
├── tests/ # Test files
├── requirements.txt
└── README.md
Environment Variables
- Never hardcode credentials
- Use Domino secrets for sensitive values
- Document required environment variables
Version Control
- Use meaningful commit messages
- Tag releases for reproducibility
- Document dependencies in requirements.txt
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.
- 10d ago First seen · 89 lines · 46 tokens per session scan A 6d8cbf0bcbe1
domino-setup is an agent published in the GitHub repository dominodatalab/domino-claude-plugin (6 stars, last pushed 2mo ago), licensed MIT. It adds 46 tokens to every session and 590 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 2026-08-30.
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.
Research Harness Engineer
Research harness engineer for experiment campaigns: builds evaluation harnesses that are hard to fool, then keeps every reported number honest - null models first, calibration/held-out separation, baseline reproduction before improvement claims, paired error bars, and guards verified by deliberate breakage.
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.
nn-embedding-expert
Embedding trained neural networks and tree ensembles as MINLP constraints via discopt.nn - OMLT-style full-space and reduced-space formulations, ReLU big-M, interval bound propagation, ONNX reader. Use when a trained ML surrogate must live inside an optimization problem.