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Figures

The figures plugin composes publication-quality figures at exact journal dimensions (Nature, Science, PNAS, Cell, and others) and QAs them before they go anywhere near a submission.

The figure pipeline

A figure moves through five steps, each with its own mechanical defense against the most common failure at that step, and a boomerang from validation straight back to building when a font fails:

Figure pipeline: Plan, Build, Compose, Validate, Export, with a boomerang from Validate back to Build on font failure

  1. Plan: journal, size, panel grid, palette and theme
  2. Build: plot-styling for data plots, svg-figure/svg-primitives for schematics, icons, or an AI-generated substrate
  3. Compose: svgutils places panels at exact mm coordinates, with text preserved as inspectable <text> elements
  4. Validate: validate_fonts.py reports the effective point size against the journal minimum
  5. Export: Inkscape when available on $PATH, cairosvg fallback otherwise

When the validator fails, the fix is mechanical: rescale the panel up, increase the source point size, or widen the canvas, not a redesign.

The plugin map

The plugin is a composer at the center, four element-builder skills that feed it, and a QA agent that runs on every figure regardless of how it was built:

Figures plugin map: scientific-figure composer at the center, fed by plot-styling, svg-figure, transparent-icons, and ai-full-figure, with figure-qa running on every output

How figure-qa decides what to check

figure-qa dispatches on input type, runs the matching deterministic check script, then always adds a VLM aesthetic pass on top:

Figure QA dispatch: detects SVG, raster, plot-script, or composed-figure-directory input, runs the matching check script, then a VLM aesthetic pass on every input type

  • SVG → check_svg.py (bbox / arrow-tip-to-target / point size / palette)
  • Raster (PNG/JPG/TIFF) → check_raster.py (DPI / embedded fonts / alpha channel)
  • Plot script → check_plot_script.py (savefig kwargs / rcParams)
  • Composed-figure directory → all of the above, per panel

Programmatic checks own anything with ground truth (font minima, palette compliance, geometry); the VLM judgment pass is reserved for "does this look balanced": hierarchy, alignment, palette coherence, journal fit.

Skills

  • scientific-figure: the composer (the sink): svgutils-based, exact mm coordinates, validate_fonts.py before export, Inkscape/cairosvg backend
  • plot-styling: data plots via matplotlib, seaborn, plotnine, plotly, or PyVista, with SciencePlots recipes for Nature/IEEE/Science/Cell/PNAS/APS
  • svg-figure / svg-primitives: hand-authored or programmatic schematics: boxes, arrows, and labels in SVG, with svg-primitives preferred for new work (mm-precise, auto-fit text, tangent-correct arrows, in-process validation)
  • transparent-icons: flat scientific icons via the Codex CLI image_gen tool or the OpenAI Images API, with Pillow-threshold or opt-in rembg+BiRefNet transparency
  • ai-full-figure: an AI-generated pictorial substrate plus programmatic label/arrow/scale-bar overlay, so the model never hallucinates the labels themselves
  • figure-qa: the QA agent described above, run against every figure regardless of how it was built

Try it

"Create a Nature 2-column figure with 3 panels showing EEG spectrograms"
"QA this figure for Science submission requirements"
"Generate a transparent icon of a neuron for my poster"

Learn more

The Agentic Research Course week 8, "Scientific Figures," covers this plugin hands-on.