figures

Use this skill when the user asks to "generate an AI figure", "make an AI-generated figure", "make a poster figure", "make a presentation figure", "generate a figure with AI", "generate figure panels with Codex", "make a multi-panel AI figure", "AI background for a figure", or wants a pictorial figure or panel (brain scene, apparatus, anatomy, pipeline illustration) rendered by gpt-image-2 through the Codex CLI with large verbatim titles and panel letters, then QA'd and iterated until it passes. Expects a theme from figures:figure-bible. Data plots, axes, numerals, and equations go to figures:plot-styling or figures:svg-primitives instead.

Use this skill when the user asks to "make a figure bible", "set up a style bible", "create a figure theme", "make a theme for my figures", "set up a figure style", "palette for my paper figures", or wants a consistent figure style. Scaffolds and validates figures/theme.json; run before any figures skill generates images.

Use this skill to QA a scientific figure for journal compliance, alignment, palette correctness, and text legibility. Triggers on "QA this figure", "check this figure", "review my figure", "is this figure paper-ready", "validate figure", "check the text in this figure", or when invoked after a figures skill produces output (unless the caller passes no-qa). Dispatches on input type (SVG, raster PNG/JPG/TIFF, Python plot script, or composed-figure directory), runs the programmatic checks including OCR of expected strings and palette compliance against the project theme, adds a vision rubric pass, and returns a report with a machine-readable JSON verdict that drives the generate, QA, fix loop.

This skill should be used when the user asks to "plot data", "make a plot for a paper", "make a publication plot", "create a journal-quality plot", "matplotlib for a figure", "use seaborn", "use plotnine", "use ggplot in Python", "improve plot quality", "style my plot", "clean up my figure", "my matplotlib plot looks ugly", "apply journal style to existing script", "fix matplotlib defaults", "use SciencePlots", "make a Nature-style plot", "make an IEEE-style plot", "make a paper-ready chart", "which plotting library should I use", or wants guidance on choosing between matplotlib / seaborn / plotnine / plotly / pyvista and applying journal-quality defaults. Produces SVG-output Python scripts whose panels are consumed by the scientific-figure composer.

This skill should be used when the user asks to "create a figure", "make a scientific figure", "create a paper figure", "make a figure for my paper", "make a figure for my manuscript", "compose figure panels", "assemble figure panels", "combine figure panels", "make a multi-panel figure", "figure composition", "export a figure", "generate a PDF figure", "create a Nature-style figure", "make a journal figure", "make a publication figure", or "create a figure for my grant". The skill composes multi-panel figures at exact journal dimensions (Nature 89/183 mm, Science 55/120 mm, Cell 85/174 mm, PNAS 87/180 mm) using svgutils, validates font sizes against journal minima before export, and writes PDF/PNG via Inkscape when available or cairosvg otherwise.

This skill should be used when the user asks to "create an SVG figure", "make a schematic", "draw a diagram", "create a schematic diagram", "draw a flowchart", "draw a process flow", "draw a workflow", "draw a workflow diagram", "make an SVG schematic", "create a process diagram", "create a pipeline diagram", "create a block diagram", "draw a system diagram", "system architecture diagram", "make this SVG editable in Illustrator", "prepare an SVG for Affinity Designer", "editor handoff", "Illustrator-friendly SVG", "hand off a figure to a designer", or wants a hand-authored or programmatic SVG with shapes, arrows, and labels that the figure-qa agent can verify. **For new Python-driven figures, route to `figures:svg-primitives` instead**, this skill is the conventions and hand-authoring reference. Outputs are SVG files that can be loaded as panel sources by the scientific-figure composer; `scripts/editor_prep.py` rewrites any finished SVG into the editor-safe dialect for Illustrator/Affinity handoff.

Use when the user asks to build a programmatic SVG schematic in Python: flowcharts, boxes and arrows, auto-fit SVG text, mm-precise diagrams, edge-snapped arrows, tangent-correct curve arrowheads, orthogonal or Manhattan routing, deterministic z-order layers, labeled groups, bracket groupings, leader-line annotations, or strict Canvas.save validation for text overflow. Use figure-qa instead for after-the-fact validation of arbitrary hand-authored or exported SVGs. Built on drawsvg, svgpathtools, and fontTools; output SVGs can be composed by scientific-figure.

Use this skill when the user asks to "make an icon", "generate an icon", "create a scientific icon", "make a transparent icon", "make a minimal icon", "icon for a figure", "icon for a paper", "generate an icon set", "batch icons", or wants flat scientific icons (brain, neuron, DNA, EEG cap, microscope) with transparent backgrounds in a journal style. Generates PNG icons with gpt-image-2 through the Codex CLI (or the OpenAI Images API), or Atlas Cloud with explicit opt-in, keeps the model's native alpha, and reads palette and model settings from the project theme made by figures:figure-bible.