Imported from M1n-n9/academic-figures-drawer (SKILL.md). Install upstream with npx skills add M1n-n9/academic-figures-drawer. Copyright stays with the author.

Academic Figures Drawer

Turn a paper or description into a figure that is easy to decode at two-column size. The editable .drawio XML is the source of truth; PNG/SVG/PDF are derived exports. Optimize for visual hierarchy and semantic clarity, not for enumerating every standard layer.

Operating contract

• Make the story explicit: input → transformation → contribution → output.
• Use a concise framework view for the global story and a separate module view only when internal mechanics matter.
• Every shape, color, icon, tensor label, and connector must have a named meaning. Delete decorative grids, token cards, or colored blocks that do not encode real data.
• Keep real-object imagery (sensor, device, body part, waveform, application scene) in input/data/context regions only. Represent model computation with editable vector primitives.
• Distinguish prior/standard components from the paper contribution with one restrained accent color and an explicit legend.
• Preserve the paper's terminology, tensor symbols, stage order, and training/inference distinction. Never invent results, dimensions, or module names.
• Apply the reusable quality rules in references/general-quality-contract.md; they encode paper-scale readability, compact composition, explicit connector semantics, rendered LaTeX, real-object asset boundaries, and export-driven review.

Fast decision tree

1. Framework overview — 5–8 major stages, one dominant left-to-right path, only key tensors and one or two branch relations.
2. Module detail — zoom into the novelty: operation order, input/output dimensions, Q/K/V or feature-interaction direction, residual/skip paths, and where parameters are learned. Do not expand routine Linear/Norm/Activation layers unless they explain the novelty.
3. Multi-panel figure — use (a) Overall Architecture, (b) Proposed Module, and optionally (c) Training/Downstream Tasks) when one canvas would otherwise be unreadable. Panels share the same grid and legend.
4. Reference replication — treat the image as a style/layout source, not as the scientific source. Follow references/reference-replication-protocol.md and create the required intermediate artifacts before writing XML.

Ask at most three focused questions only when the input cannot establish the diagram type, output format, or missing scientific semantics. Otherwise infer a safe default: landscape, editable draw.io plus PNG preview, English labels unless the source is Chinese.

End-to-end workflow

1. Intake and semantic brief

Read the paper section, abstract/method, prompt, code, and supplied images. Classify each source as content, structure, style, layout, or asset. Extract:

• raw input and its modality/shape;
• preprocessing or patching that is essential to the story;
• named stages and their data dependencies;
• the novel module(s), their operation sequence, and any tensor reshapes;
• training-only branches, losses, inference path, and final output;
• known/frozen components versus trainable/new components.

Create brief.md with goal, audience, must-communicate items, exclusions, terminology, and open assumptions. Use references/self-supervision-and-intake.md for the traceability table.

2. Style contract before drawing

If reference images are provided, read references/style-extraction.md and record exact or measured values in visual-spec.md: palette, font, type hierarchy, corner radius, stroke widths, dash pattern, margins, spacing rhythm, arrow grammar, icon language, density, and panel composition.

If no style reference is available, read references/topconf-paper-style.md and apply references/figure-contract.md.

Use this default semantic palette unless the extracted contract overrides it:

Use one font family throughout (Arial/Helvetica; Noto Sans CJK for Chinese text), 1.5–2 px normal strokes, 2–3 px contribution strokes, 10–14 px body labels, 16–24 px panel/stage headings, 8 px alignment grid, and 16–28 px outer margins. Tune these values to the actual canvas and render; never shrink important text below paper-scale legibility.

Relative-scale gate: treat typography and module area as a final design constraint, not an afterthought. Before handoff, inspect a canvas-only screenshot at the intended paper width and verify that panel titles are the largest text, contribution/module titles are visibly larger than annotations, and standard helper cells are not larger than the innovation block. As a practical starting point, use ≥20 px panel titles, ≥15 px key-module labels, ≥12 px tensor annotations on a 1600–2200 px canvas, and reserve at least 80–120 px width or 120–180 px height for a key module. If the figure is dense, enlarge the important module and remove redundant words before shrinking its font. Record any intentional deviations in visual-spec.md and the final screenshot review.

3. Optional image-2 concept pass

Use the image-generation capability only when it helps explore composition or supplies a real-world input/context asset. Prompt for a clean academic concept with no scientific text, no equations, and no tiny unlabeled blocks. Treat the result as a visual reference; keep the paper-derived semantic graph authoritative. Do not embed the generated bitmap as the model pipeline. If a real input asset is used, record its provenance and role in asset-ledger.md.

Recommended concept prompt shape: “wide camera-ready scientific figure, left-to-right input–core innovation–output story, muted blue/teal/lavender/ochre palette, one restrained coral highlight for the proposed module, consistent rounded vector cards and arrows, generous whitespace, no words or equations, no decorative grids.” After generation, inspect the bitmap, write the semantic/layout inventory, and redraw the shapes and connectors in XML. If editing a user image, inspect it first and pass its local path as the image-generation reference; never use a guessed or missing path.

4. Plan the composition

For a framework view, choose a wide landscape canvas (roughly 1600–2200 × 850–1200 px), align stages on a single baseline, and leave whitespace around the contribution. For a module view, use a large central container with 3–6 labeled operations and small tensor-shape annotations. Use dashed containers only for meaningful groups (encoder, training-only path, memory bank, optional branch). Put the legend near a corner, never in the main flow.

Define each edge before authoring it: source, target, direction, relation type (data/control/feedback/update/annotation), cardinality, label, and forbidden crossing zones. Prefer orthogonal or short straight routes; use waypoints and exitX/exitY/entryX/entryY when fan-in/out would stack or cross.

5. Author editable XML

Read references/xml-authoring.md before hand-authoring XML. Use explicit mxGeometry positions and stable ids (never reuse reserved ids 0 and 1). Use rounded rectangles, containers, arrows, and simple editable primitives. Use scripts/shapesearch.py for a specific draw.io library shape and scripts/aiicons.py only for a required AI brand mark. Use scripts/autolayout.py only as a first placement for large graphs; manually restore the paper composition afterward. Run scripts/edgeports.py when several edges leave the same side of a node.

Annotate dimensions only where they answer a reader question, using a compact second line such as X ∈ R^(B×T×D) or (B, C, H, W) → (B, T, D). For attention, show Q/K/V and arrow direction; for feature interaction, show the actual axes or branches being mixed. Add a legend mapping every used semantic color to a category and mark the contribution explicitly (e.g., Proposed / trainable).

6. Preflight and visual verification

Use references/general-quality-contract.md as the review contract. In particular, verify that the canvas is fitted to the composition, every arrow has an explicit semantic source and target, and formulas are rendered in the exported artifacts rather than shown as source delimiters.

Before any preview, run:

python <skill-dir>\scripts\validate_visual_quality.py <figure>.drawio
python <skill-dir>\scripts\validate_drawio.py <figure>.drawio

Zero FAIL items are required. Review every warning, especially text overflow, arrow-box collision, overlap, spacing variance, palette scatter, orphan labels, and edge-density hotspots. Read references/xml-preflight.md when a warning is ambiguous.

Run a proportion pass after static preflight: compare title/body/annotation scale, compare contribution versus standard-module area, and confirm that the smallest required label remains readable in the canvas-only screenshot. A figure is not ready if the XML is structurally valid but the key innovation or tensor dimensions disappear at paper scale.

Preferred preview on Windows or when URLs are long:

python <skill-dir>\scripts\serve_drawio_preview.py <figure>.drawio --port 8765

Open the local preview and capture a canvas-only screenshot (the diagram should fill most of the crop). If browser automation is unavailable, export a preview with draw.io CLI without -e; if the CLI is unavailable, deliver XML and a browser-fallback URL from scripts/encode_drawio_url.py.

For a camera-ready or user-critical figure, perform at least three screenshot → complete 9-zone defect inventory → fix all P0/P1 → re-render → verify cycles. Log them in defect-log.md; use references/self-supervision-and-intake.md for the inventory, red-team pass, and self-score. Compare at paper scale, not only at editor zoom.

7. Final export and handoff

Export the requested formats. For an editable PNG, use the double extension and repair the known draw.io IEND truncation:

drawio -x -f png -e -s 2 -o <figure>.drawio.png <figure>.drawio
python <skill-dir>\scripts\repair_png.py <figure>.drawio.png

SVG/PDF exports may also embed the XML. Report the .drawio source, latest preview/screenshot, exports, validation status, self-score, and remaining approximations. Never claim completion when a required component/edge is missing, text is clipped, semantics are ambiguous, or the latest evidence is partial.

Input-specific notes

• Paper/PDF: use the available PDF/document reader to extract method and notation; cite no fabricated numbers. Separate training, inference, and evaluation paths.
• Description only: state assumptions in brief.md and keep the first version abstract; ask only for information that changes the semantic graph.
• Reference screenshot: preserve layout rhythm and typography but redraw as vectors; use a raster only for an explicitly requested input/context image.
• Existing .drawio: patch labels, geometry, styles, or edges in place for local changes; regenerate only for a layout-wide change. Re-run both validators after every edit.

Bundled resources

• references/figure-contract.md — concise ICML/NeurIPS/ICLR visual and semantic contract.
• references/topconf-paper-style.md, style-extraction.md, reference-replication-protocol.md, self-supervision-and-intake.md — paper-figure intake, reference extraction, and evidence loop.
• references/xml-authoring.md, xml-preflight.md, primitive-icons.md — editable XML, layout, and icon recipes.
• references/diagram-types.md, shapes.md, troubleshooting.md — draw.io shape vocabulary and fallback guidance.
• references/THIRD_PARTY_NOTICES.md — license notices for included utility portions.
• scripts/make_drawio_preview.py, serve_drawio_preview.py, validate_drawio.py, validate_visual_quality.py, and validate_replication_artifacts.py — preview and quality gates.
• scripts/init_figure_workspace.py — create non-destructive brief.md, visual-spec.md, layout-grid.md, asset-ledger.md, and defect-log.md scaffolding.
• scripts/repair_png.py, encode_drawio_url.py, shapesearch.py, aiicons.py, autolayout.py, edgeports.py, and validate.py — export repair, browser fallback, shape lookup, optional layout, edge-port distribution, and structural lint.
• data/shape-index.json.gz and data/lobe-icons.json — local indexes used by shape and AI-icon lookup scripts.