video1.0<iframe src="https://www.loom.com/embed/37b670a9280a4a57ae2ac06abb16f8ac" frameborder="0" width="1280" height="960" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>9601280Loomhttps://www.loom.com9601280https://cdn.loom.com/sessions/thumbnails/37b670a9280a4a57ae2ac06abb16f8ac-30592d72741debc0.gif289.695This Loom explains an agent driven workflow for documenting and generating research figures from code and data. The author describes a canvas where blue boxes are functions that transform data and white boxes are datasets, linking each plot back to its sources. They use an Obsidian based skill that scans a repository, updates the canvas, and helps coordinate tasks with PyCharm and its coding agent to produce Jupyter or R notebooks. Over a conversation lasting about two or three days, the agent helped refine a linear model and discuss outputs using screenshots, while also supporting results by finding relevant papers.