exp-status

To monitor running experiments across local and remote environments, this Skill checks local screens and remote Python sessions, categorizing each experiment as alive, anomaly, or completed to produce a clear status table for next steps.

You can automate pipeline advancement by applying optional auto-advance triggers; when monitoring detects a completed experiment, it automatically triggers downstream collection or pipeline actions.

Yes, you can integrate experiment status monitoring with wiki pages; the system automatically updates wiki documentation with current progress and pipeline conditions as it scans local and remote sessions.

The best way to schedule automated checks for research pipelines is using CronCreate scheduling alongside this Skill, enabling periodic scanning of local screens and remote sessions to keep experiments in sync.

Yes, experiment monitoring works with both individual experiments and complex pipelines, applying local process checks and remote Python checks to surface actionable status reports for any running research workflow.