Figures

Use this skill to QA a scientific figure for journal compliance, alignment, palette correctness, and label legibility. Triggers on "QA this figure", "check this figure", "review my figure", "is this figure paper-ready", "validate figure", or when invoked proactively after a figure-generation 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) and runs the right programmatic checks plus a VLM rubric judgment pass.

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 `[[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.

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 "generate an AI figure", "make a full AI-generated figure", "make a poster figure", "make a presentation figure", "generate a figure with AI", "create a substrate image", "AI background for a figure", "create a poster background", or wants a pictorial substrate (brain scene, microscope setup, anatomical scene, lab apparatus) generated by AI and then composited with labels and arrows in code. Defaults to the Codex CLI image_gen tool (preferred when `codex login` is configured) or the OpenAI Images API. Hard ceiling is any figure that needs data plots, axis numerals, equations, multi-arrow flowcharts, or more than 5 labeled elements should use scientific-figure or svg-figure instead.

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.

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 "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", "icon batch", "batch generate icons", or wants flat scientific icons (brain, neuron, DNA, EEG cap, etc.) in the Nature/Science journal style with transparent backgrounds. Generates PNG icons via the Codex CLI image_gen tool (preferred) or the OpenAI Images API (fallback), then applies transparency via a Pillow threshold by default or rembg + BiRefNet (opt-in, higher edge quality).