experiment-tracking
bydominodatalab· 23 skills
Track traditional ML experiments in Domino using the MLflow-based Experiment Manager. Covers experiment setup, auto-logging for sklearn/TensorFlow/PyTorch, manual logging, artifact storage, run comparison, and model registration. Use when training ML models, logging metrics and parameters, comparing model runs, or registering models.
How to install experiment-tracking?
dominodatalab/domino-claude-plugin/experiment-tracking
$npx -y skills add dominodatalab/domino-claude-plugin --skill experiment-trackingInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/dominodatalab/domino-claude-plugin" --skill "dominodatalab/domino-claude-plugin/experiment-tracking"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/dominodatalab/domino-claude-plugin" that are relevant to the current task. Run `npx skills add "https://github.com/dominodatalab/domino-claude-plugin"` and select the relevant skills, then follow their instructions.
Related
6 picksType
- azure-kustoQuery and analyze data in Azure Data Explorer (Kusto/ADX) using KQL for log analytics, telemetry, and time series analysis.Jul 2026
- analyticsWhen the user wants to set up, improve, or audit analytics tracking and measurement.Jul 2026
- firecrawl-dashboard-reportingPull metrics from analytics dashboards and internal web tools with Firecrawl browser.Jun 2026
- data-storytellingTransform data into compelling narratives using visualization, context, and persuasive structure.Jul 2026
- data-visualizationCreate effective data visualizations with Python (matplotlib, seaborn, plotly).Jul 2026
- extension-oqlMake a canister's data queryable by the Caffeine Data Intelligence agent.Jul 2026
