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 skills add dominodatalab/domino-claude-plugin --skill experiment-trackinggit 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/skills/dominodatalab/domino-claude-plugin/experiment-tracking)<a href="https://agentmods.dev/skills/dominodatalab/domino-claude-plugin/experiment-tracking"><img src="https://agentmods.dev/badge/skills/dominodatalab/domino-claude-plugin/experiment-tracking/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/skills/dominodatalab/domino-claude-plugin/experiment-tracking"><img src="https://agentmods.dev/badge/skills/dominodatalab/domino-claude-plugin/experiment-tracking.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 | $0.00069 | $0.00668 |
| Opus 5 | $0.00034 | $0.00334 |
| Sonnet 5 | $0.00014 | $0.00134 |
| Haiku 4.5 | $0.00007 | $0.00067 |
Grade A, and why
domino-experiment-tracking 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domino Experiment Tracking Skill
This skill provides comprehensive knowledge for tracking ML experiments in Domino Data Lab using the built-in MLflow-based Experiment Manager.
Key Concepts
Experiment Manager Overview
Domino's Experiment Manager is built on MLflow and provides:
- Automatic and manual logging of parameters, metrics, and artifacts
- Run comparison and visualization
- Model versioning and registry
- Integration with Domino projects and jobs
Critical Configuration
Experiment names must be unique across the entire Domino deployment. Always append username or project name to ensure uniqueness.
Related Documentation
- MLFLOW-BASICS.md - Auto-logging, manual logging
- COMPARING-RUNS.md - Run comparison, export
- MODEL-REGISTRY.md - Model registration & stages
Quick Start
import mlflow
import os
# CRITICAL: Experiment names must be unique across Domino deployment
username = os.environ.get('DOMINO_STARTING_USERNAME', 'unknown')
experiment_name = f"my-experiment-{username}"
# Set the experiment
mlflow.set_experiment(experiment_name)
# Enable auto-logging (easiest approach)
mlflow.autolog()
# Run training
with mlflow.start_run(run_name="my-first-run"):
model.fit(X_train, y_train)
# Optional: manually log additional items
mlflow.log_param("custom_param", "value")
mlflow.log_metric("custom_metric", 0.95)
Supported Frameworks
| Framework | Auto-log Command |
|---|---|
| Scikit-learn | mlflow.sklearn.autolog() |
| TensorFlow/Keras | mlflow.tensorflow.autolog() |
| PyTorch | mlflow.pytorch.autolog() |
| XGBoost | mlflow.xgboost.autolog() |
| LightGBM | mlflow.lightgbm.autolog() |
| All at once | mlflow.autolog() |
Environment Variables
Domino automatically configures MLflow to use the built-in tracking server. These variables are pre-set:
| Variable | Description |
|---|---|
MLFLOW_TRACKING_URI |
Domino's MLflow server URL |
DOMINO_STARTING_USERNAME |
User running the experiment |
DOMINO_PROJECT_NAME |
Current project name |
DOMINO_RUN_ID |
Domino job run ID |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 81 lines · 69 tokens per session scan A 4fbe9f4cbdf4
domino-experiment-tracking is a skill published in the GitHub repository dominodatalab/domino-claude-plugin (6 stars, last pushed 2mo ago), licensed MIT. It adds 69 tokens to every session and 668 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 2026-08-30.
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