Make-Figures Skill

You are helping a medical researcher generate publication-ready figures for medical research manuscripts. Every figure must meet journal specifications for dimensions, resolution, fonts, and color accessibility. Produce clean, data-focused visuals with no chartjunk.

Credits

The Critic Loop (Step 4b) in this skill is inspired by PaperBanana (Zhu et al., Automating Academic Illustration for AI Scientists, arXiv:2601.23265, 2025) and by prior self-refinement research — Self-Refine (Madaan et al., 2023), Reflexion (Shinn et al., 2023), and Constitutional AI (Anthropic, 2022). This is a clean-room reconstruction specialized for medical publication figures (STARD / CONSORT / PRISMA, journal-specific specs, Wong colorblind palette). No code, prompts, or configurations are derived from PaperBanana's repository.

Communication Rules

• Communicate with the user in their preferred language.
• All figure text (labels, legends, annotations) must be in English.
• Medical terminology is always in English.

Data Privacy Check

Before reading any data file, check whether it might contain Protected Health Information (PHI):

1. If

   *_deidentified.*

   files exist in the working directory, use those preferentially.
2. If only raw CSV/Excel files exist (no

   *_deidentified.*

   counterpart), warn the user:

   "이 데이터에 환자 식별정보(이름, 주민번호, 연락처 등)가 포함되어 있습니까? 포함된 경우

   /deidentify

   스킬로 먼저 비식별화를 진행해주세요."

3. If the user confirms the data is already de-identified or contains no PHI, proceed.

Reference Files

• Figure specifications:

  ${CLAUDE_SKILL_DIR}/references/figure_specs.md

• Figure style:

  ${CLAUDE_SKILL_DIR}/../analyze-stats/references/style/figure_style.mplstyle

  (or project's CLAUDE.md if available)
• Project data: See CLAUDE.md for data locations under

  2_Data/

Read

figure_specs.md

AI-Generated Figure Warning

AI-generated illustrations (from Genspak, Canva AI, DALL-E, etc.) are immediately recognizable to experienced reviewers and audiences. Telltale signs include:

• Small decorative icons that add no information
• Overly uniform layouts with templated grid patterns
• Text too small to read at print resolution
• Generic medical clip-art style that does not match the study specifics

Rule: AI tools may assist with figure drafting, but the final figure must not look AI-generated. Always customize layouts, replace generic icons with study-specific visuals, adjust text sizing, and verify that every visual element serves a purpose.

For important presentations and journal submissions, an AI-looking figure gives the impression of low effort. When time permits, use Canva (for illustrations) or manual PPT drawing (for anatomical sketches) to produce figures that show intentional design.

DPI and Resolution Guide

Practical workflow for screen captures:

• Use HyperSnap or similar tool with DPI pre-set to the journal requirement
• Compose the figure in PPT at high zoom → capture at target DPI → save as TIFF/PNG
• Verify final file dimensions match journal column width requirements

Visual Abstract / Graphical Abstract

Many journals now require or strongly encourage visual abstracts. European Radiology made graphical abstracts mandatory for all Original Articles from first revision (Jan 2025). Submitting one voluntarily signals effort and can improve editorial impression.

Journal Requirements

Check the target journal profile (

write-paper/references/journal_profiles/

Workflow

1. Check journal template. Look for an official PPTX template in

   ${CLAUDE_SKILL_DIR}/references/visual_abstract_templates/{journal}.pptx

   . If no journal-specific template exists, use

   medsci_default.pptx

   .
2. Extract content from the manuscript:
   • Title: Full article title
   • Hypothesis/Question: Derived from Key Point 1 or study objective (max 1 sentence)
   • Methodology: Brief flowchart or ≤3 bullets, <6 words each
   • Visual element: Study's own figure (ROC curve, flow diagram, representative image)
   • Badges: Patient cohort (N=...) | Modality/organ | Single/Multi-center
   • Main finding: Derived from Key Point 3 (<20 words)
   • Citation: Journal (year) Authors; DOI
3. Select visual element (priority order — no API needed for top options):
   1. Study's own figures (ROC, flow diagram, representative image) — always preferred
   2. Free illustration from Servier Medical Art or NIAID BioArt (see

      ${CLAUDE_SKILL_DIR}/references/medical_illustration_sources.md

      )
   3. Manual drawing in PPT/Keynote/Figma
   4. AI generation via

      generate_image.py --style medical

      (only if GEMINI_API_KEY set)
4. Generate using the script:

   python ${CLAUDE_SKILL_DIR}/scripts/generate_visual_abstract.py \
     --template european_radiology \
     --title "Article Title" \
     --hypothesis "Research question" \
     --methods "Method 1|Method 2|Method 3" \
     --finding "Main finding statement" \
     --citation "Eur Radiol (2026) Author A et al; DOI:..." \
     --visual figures/fig1_roc_curve.png \
     --badges "N=450|CT chest|Multi-center" \
     --output figures/visual_abstract.pptx
   

5. Review with user. Open the PPTX to verify layout and content. Iterate.
6. Export. PPTX is the primary deliverable. For PNG: open in PowerPoint/Keynote → export, or use LibreOffice CLI (

   soffice --headless --convert-to png

   ).

Design Principles

• One page, landscape (16:9) or per journal template specification
• Three sections: Study question → Key method → Main result
• Use the study's actual figures rather than generic graphics
• Minimize text — let visuals carry the message
• Every visual element must serve a purpose (no decorative clip-art)

Available Templates

european_radiology.pptx

medsci_default.pptx

To add a new journal template: see

${CLAUDE_SKILL_DIR}/references/visual_abstract_templates/template_guide.md

Workflow

Step 1: Specify

Optional flags:

• --study-type <type>

  : One of:

  diagnostic-accuracy

  ,

  ai-validation

  ,

  meta-analysis

  ,

  dta-meta-analysis

  ,

  observational-cohort

  ,

  rct

  . When set, auto-generate the full figure set from the Study-Type Figure Sets table below without prompting for individual figure types.

• --data-dir <path>

  : Directory containing analysis outputs (CSVs,

  _analysis_outputs.md

  ). Default: current working directory.

Ask the user for:

1. Figure type (from the supported types below) — skipped when

   --study-type

   is provided
2. Data source (file path, DataFrame, or manual values)
3. Target journal (for dimension/font requirements)
4. Panel layout (single panel, multi-panel, or let you decide)
5. Any special requests (annotations, highlights, reference lines)
6. Study type (if not passed via

   --study-type

   ): determines the required figure set

If the user provides enough context, infer missing parameters and confirm before proceeding.

Step 2: Configure

1. Load the figure style file:

   import matplotlib.pyplot as plt
   import os
   style_path = os.path.join(os.environ.get('CLAUDE_SKILL_DIR', '.'), '../analyze-stats/references/style/figure_style.mplstyle')
   if os.path.exists(style_path):
       plt.style.use(style_path)
   

2. Look up journal-specific dimensions from

   ${CLAUDE_SKILL_DIR}/references/figure_specs.md

   .
3. Set the colorblind-safe palette (Wong palette by default).
4. Configure font sizes per element type (title, axis label, tick label, legend, annotation).

Step 3: Generate

Create the figure using Python (matplotlib/seaborn as primary, with specialized libraries as needed).

Script structure:

"""
Figure: {description}
Date: {YYYY-MM-DD}
Target: {journal}
Dimensions: {width} x {height} inches @ {DPI} DPI
"""
import numpy as np
import matplotlib.pyplot as plt
import os

style_path = os.path.join(os.environ.get('CLAUDE_SKILL_DIR', '.'), '../analyze-stats/references/style/figure_style.mplstyle')
if os.path.exists(style_path):
    plt.style.use(style_path)

# Wong colorblind-safe palette
WONG = ['#000000', '#E69F00', '#56B4E9', '#009E73',
        '#F0E442', '#0072B2', '#D55E00', '#CC79A7']

np.random.seed(42)

Step 4: Review

Present the figure to the user and ask:

• Does the layout work?
• Are labels and annotations correct?
• Any adjustments to colors, sizing, or emphasis?

Iterate until the user approves.

Step 4b: Critic Loop (self-critique before final export)

Before Step 5 Export, run the automated Critic Loop. This is two stages — deterministic quantitative checks via Python, then qualitative review by Claude itself — and the combined output tells us whether to re-render or hand off to the user.

Stage 1: Quantitative checks (

critic_figure.py

python ${CLAUDE_SKILL_DIR}/scripts/critic_figure.py \
    figures/fig1_stard.png \
    --type stard \
    --spec-min-dpi 600 \
    --spec-width-in 7.0 \
    --source-text figures/fig1_stard.txt \   # optional: expected strings for OCR coverage
    --out figures/fig1_stard.critique.json

This produces a JSON report covering:

• DPI and physical width vs. journal spec
• Dominant-color breakdown and out-of-Wong-palette fraction
• OCR-detected word count, minimum text height, and (if a source-text file was provided) source-word coverage

Stage 2: Qualitative review (Claude session)

1. Use the Read tool to load the generated PNG.
2. Read the corresponding rubric file:
   • Flow diagrams:

     ${CLAUDE_SKILL_DIR}/references/critic_rubrics/flow_diagram.md

   • Data plots:

     ${CLAUDE_SKILL_DIR}/references/critic_rubrics/data_plot.md

3. If exemplars exist in

   ${CLAUDE_SKILL_DIR}/references/exemplar_diagrams/{type}/

   , Read 1–3 of them plus their

   _why.md

   notes.
4. Score every rubric item as PASS / PARTIAL / FAIL with a one-line note, using the format at the bottom of the rubric file.
5. Emit a "Required edits before next render" list of concrete source-code changes (D2 node renames, count corrections, matplotlib parameter tweaks).

Refinement loop

• If all items are PASS → proceed to Step 5 Export with

  critic_pass: yes

  .
• If any item is FAIL → apply the required edits to the source (D2 file or matplotlib script), re-render, and re-run Stage 1 + Stage 2. Default maximum is T=2 rounds; the user may request up to T=3.
• If after the max rounds some items remain PARTIAL, proceed with

  critic_pass: partial

  and record the residual items in the manifest's

  critic_notes

  field.

Record the final state in

_figure_manifest.md

/write-paper

Step 5: Export

Save final outputs:

• PDF (vector format, preferred for journal submission)
• PNG (300 DPI raster, for review and presentation)
• TIFF (if the journal requires it, 300 DPI LZW compression)

Name files descriptively:

fig1_roc_curve.pdf

fig2_consort_flow.pdf

Step 6: Design QC Checklist

Before delivering the final figure, verify all items:

• Font: Sans-serif (Arial/Helvetica), minimum 7pt, axis labels ≥ 9pt
• Color: Wong/Okabe-Ito colorblind-safe palette used
• Colorblind test: Would the figure work for deuteranopia? (no red-green only distinctions)
• Grayscale test: Information preserved when printed in black & white
• Alignment: All elements on a consistent grid; panels aligned
• Vector output: PDF/SVG saved (not just PNG)
• Resolution: ≥ 300 DPI for raster elements, ≥ 600 DPI for line art
• Journal specs: Dimensions, font, and format match target journal requirements
• No chartjunk: No 3D effects, unnecessary gridlines, gradient fills, or decorative elements
• Caption: Drafted with key finding, abbreviations, statistical details, and sample size

Study-Type Figure Sets

When the study type is known (from

/write-paper

After generating all figures, create a structured manifest file at

figures/_figure_manifest.md

# Figure Manifest
Generated: {YYYY-MM-DD}
Study type: {study type or "custom"}

| Figure | Path | Type | Tool | Critic | Rounds | Description |
|--------|------|------|------|--------|--------|-------------|
| Figure 1 | figures/fig1_stard_flow.svg | flow-diagram | D2 | yes | 2 | STARD participant flow diagram |
| Figure 2 | figures/fig2_roc.pdf | roc-curve | matplotlib | yes | 1 | ROC curves for Model A vs B |
| Figure 3 | figures/fig3_calibration.pdf | calibration | matplotlib | partial | 3 | Calibration plot; legend still crowded (see notes) |

## Critic notes
- Figure 3: after 3 rounds, legend placement remains crowded at the
  double-column width. Candidate remediations documented but not applied
  to avoid reducing data-point visibility.

Manifest field definitions:

• Path: Relative path from project root
• Type: One of:

  flow-diagram

  ,

  roc-curve

  ,

  forest-plot

  ,

  funnel-plot

  ,

  calibration

  ,

  km-curve

  ,

  bland-altman

  ,

  confusion-matrix

  ,

  box-violin

  ,

  bar-chart

  ,

  heatmap

  ,

  pipeline

  ,

  visual-abstract

  ,

  sroc-curve

  ,

  other

• Tool: Tool used to generate (

  matplotlib

  ,

  D2

  ,

  python-pptx

  ,

  seaborn

  , etc.)
• Critic:

  yes

  (all rubric items PASS) /

  partial

  (some PARTIAL after max rounds) /

  no

  (never critiqued — avoid for submission figures) /

  skip

  (deliberately bypassed, e.g., panel figure assembled externally)
• Rounds: Number of Critic Loop rounds executed (0 if skipped)
• Description: One-line description suitable for figure legend context

A

## Critic notes

This manifest is consumed by

/write-paper

Flow diagram generation rule: STARD/CONSORT/PRISMA/STROBE flow diagrams MUST use the standardized R pipeline

scripts/generate_flow_diagram.R

FancyBboxPatch

R flow diagram recipe (mandatory for all flow diagrams):

The pipeline reads a YAML config describing nodes/edges and produces: a true vector PDF (journal submission), a 300 dpi PNG (review copy), and a 600 dpi PNG (RSNA/Eur Radiol line-art). Default style is single-color black outline with white fill in Arial, overriding D2's colored defaults and matplotlib's manual coordinates.

# 1. One-time system dependency:
brew install librsvg
Rscript -e 'install.packages(c("DiagrammeR","DiagrammeRsvg","rsvg","yaml"))'

# 2. Author a YAML config. Templates for each type live at
#    references/exemplar_diagrams/{strobe,consort,prisma,stard}/template_input.yaml
# 3. Render:
Rscript ${CLAUDE_SKILL_DIR}/scripts/generate_flow_diagram.R \
    --type   {strobe|consort|prisma|stard} \
    --config path/to/counts.yaml \
    --out    figures/figure1_flow
# Outputs: figure1_flow.pdf, figure1_flow.png (300 dpi), figure1_flow_600.png

YAML schema highlights:

• rankdir: TB

  (top-down, default) or

  LR

  (left-to-right).

• nodes:

  list with

  id

  ,

  label

  (use literal

  \n

  for line breaks, real Unicode

  –

  ,

  ≤

  ,

  −

  ,

  •

  ).
• Optional per-node:

  highlight: true

  (thicker border),

  shape: note

  (side boxes),

  rank_same_with: <other_id>

  (place on same horizontal rank).

• edges:

  list with

  from

  ,

  to

  , optional

  style: dashed

  ,

  arrow: false

  (no arrowhead),

  constraint: false

  (edge ignored by layout engine — use for exclusion side-links).
• Numbers in labels MUST be CSV-derived in an upstream R script that emits the YAML, or hand-written only when the value lives in a commit-tracked data artifact. Follow numerical-safety rules.

Style is fixed (do not override in the YAML):

• Monochrome: all boxes

  color=black, fillcolor=white, fontname="Arial"

  .
• Penwidth 1.2 default, 1.8 for highlighted cohort box.
• Arrow style: black solid, arrowsize 0.75. Dashed without arrowhead for exclusion side-links.
• Bullet alignment in multi-item labels: Graphviz

  \l

  (left-align), never

  \n

  (center). Each

  \l

  applies to text preceding it.
• No HTML-like labels (

  label=<...>

  with

  <B>

  ,

  <I>

  ,

  •

  ). Rejected 2026-04-20 in MA-01 RFA retrofit — "오히려 구조가 깨저버렸어. 그냥 이전버전으로 하자." Plain quoted labels with

  \l

  bullets produce tighter, more readable structure than HTML ragged wrapping. Do not reintroduce without explicit approval.
• To add one emphasis color (e.g., Wong blue

  #0072B2

  for a single highlighted box), edit

  scripts/generate_flow_diagram.R

  — do not inline hex colors in YAML.

Per-project

create_figure1.R

When the flow has derived counts,

stopifnot()

{rank=same; ... }

create_figure1.R

dot

• STROBE cohort:

  1_Samsung_Changwon/11_CheckUP_DB/{05_Emphysema_COPD_Mortality,01_CAC_Warranty_Period}/manuscript/figures/create_figure1.R

• STARD:

  0_MI2RL/10_CXRscoliosis/Analysis/figures/create_figure1.R

  ,

  0_MI2RL/0_SkullFx/Paper2_Clinical/figures/create_figure1.R

  ,

  5_Personal_Research/MeducAI/7_Manuscript/Paper1/figures/v2_monochrome/create_figure1.R

• PRISMA / PRISMA-DTA:

  10_Meta_Analysis/{01_RFA_Adjunct/5_Figures/v3,02_CBCT_Biopsy/5_Figures,21_Aneurysm_AI_Validation_SR_Paper1_FD/analysis}/create_figure1.R

• CONSORT-edu (naturalistic allocation):

  5_Personal_Research/MeducAI/7_Manuscript/Paper3/figures/v2_monochrome/create_figure1.R

Copy the

STYLE_HEADER

{rank=same}

read.csv()

Legacy D2 fallback (only when R unavailable):

d2 --layout elk --theme 0 --pad 20 flow.d2 /tmp/raw.png --scale 2
# Resize + 85% vertical compression via Pillow; then render PDF:
d2 --layout elk --theme 0 --pad 20 flow.d2 figures/fig1_flow.pdf

Use

font-size: 20-24

stroke: black

fill: white

Tool Selection Guide

Choose the right tool for each figure type. Using matplotlib for flow diagrams leads to hard-coded coordinates that break when text changes — use auto-layout tools instead.

Data Visualization → matplotlib/seaborn (this skill)

Best for figures where data drives the layout. This skill handles these directly:

Flow Diagrams → Dedicated Tools (NOT matplotlib)

Flow diagrams require auto-layout engines. Do NOT use matplotlib patches with manual coordinates — this causes the "absolute coordinate hell" problem where changing one box breaks all downstream positions.

scripts/generate_flow_diagram.R --type strobe

scripts/generate_flow_diagram.R --type consort

scripts/generate_flow_diagram.R --type prisma

scripts/generate_flow_diagram.R --type stard

R workflow for flow diagrams: See the "R flow diagram recipe" above in the Flow diagram generation rule. Key points: YAML config →

Rscript scripts/generate_flow_diagram.R --type <t> --config <yaml> --out <prefix>

references/exemplar_diagrams/{strobe,consort,prisma,stard}/template_input.yaml

Visual / Graphical Abstracts → python-pptx Template Generator

generate_visual_abstract.py

${CLAUDE_SKILL_DIR}/references/medical_illustration_sources.md

See the Visual Abstract section above for the full workflow.

Hybrid Workflow (recommended for publication)

Data plots:    matplotlib/seaborn → PDF + PNG (this skill)
Flow diagrams: generate_flow_diagram.R (DiagrammeR + rsvg) → PDF + 300/600 dpi PNG
Final assembly: pandoc or python-docx (auto-embedded in DOCX)

Supported Figure Types (matplotlib/seaborn)

Figure Type Templates

ROC Curve

from sklearn.metrics import roc_curve, auc

fig, ax = plt.subplots(figsize=(3.5, 3.5))
fpr, tpr, _ = roc_curve(y_true, y_score)
roc_auc = auc(fpr, tpr)
ax.plot(fpr, tpr, color=WONG[5], lw=1.5,
        label=f'Model (AUC = {roc_auc:.3f})')
ax.plot([0, 1], [0, 1], 'k--', lw=0.8, alpha=0.5)
ax.set(xlabel='1 - Specificity', ylabel='Sensitivity',
       xlim=[-0.02, 1.02], ylim=[-0.02, 1.02])
ax.legend(loc='lower right', frameon=False)

• For multiple models: use distinct Wong palette colors, include AUC + 95% CI in legend.
• For comparison: report DeLong p-value in annotation.

Forest Plot

• Horizontal layout: effect sizes as squares (sized by weight), CIs as lines.
• Diamond at bottom for pooled estimate.
• Vertical dashed line at null effect (OR=1 or MD=0).
• Axis label: "Favours A | Favours B" or appropriate.
• Include heterogeneity stats (I-squared, p) below the diamond.

Flow Diagrams (STROBE / CONSORT / PRISMA / STARD)

Single canonical tool:

scripts/generate_flow_diagram.R

Layout invariants:

• Rectangular boxes with rounded corners for stages; notes (

  shape: note

  ) for exclusion side-boxes.
• Vertical top-down flow by default; horizontal only when the manuscript layout demands it.
• Every box label contains the count (e.g.,

  "Assessed for eligibility\n(n = 450)"

  ).
• Numbers are CSV-derived (numerical-safety) — author the YAML from an R/Python script that reads the upstream data, or cite the source file in a comment when a literal value is unavoidable.
• Follow the official template layout from each guideline.
• Use relative positioning — never hard-code absolute y-coordinates. Calculate each box position from the previous box's bottom edge plus a consistent gap constant.
• Define gap constants at the top of the script (e.g.,

  GAP_SMALL = 1.5

  ,

  GAP_BRANCH = 2.2

  ).
• Avoid magic number padding in arrow endpoints — use named constants.

D2 approach (recommended):

d2 --layout elk --theme 0 flow.d2 output.svg
# Then: open SVG in Figma → grid-snap → font swap → export PDF

Calibration Plot

• 45-degree reference line (perfect calibration).
• Grouped observed vs predicted with error bars.
• Report Hosmer-Lemeshow statistic and Brier score in annotation.
• Optional: histogram of predicted probabilities at the bottom.

Kaplan-Meier Curve

• Step function with distinct colors per group.
• Censoring marks as small vertical ticks.
• Number-at-risk table below the plot (aligned with x-axis ticks).
• Log-rank p-value in annotation.
• Median survival with 95% CI if applicable.

Bland-Altman Plot

• X-axis: mean of two measurements.
• Y-axis: difference between measurements.
• Horizontal lines: mean difference (solid), +/-1.96 SD (dashed).
• Annotate the mean diff and limits of agreement values.
• Optional: proportional bias check (regression line through points).

Confusion Matrix

• Heatmap with both counts and percentages in each cell.
• Row-normalized percentages preferred (sensitivity per class).
• Clear axis labels: "Predicted" (x) and "Actual" (y).
• Use sequential colormap (Blues or Greens), not diverging.

Box/Violin Plot

• Show individual data points (jittered) overlaid on box or violin.
• Mark median and mean distinctly.
• Statistical annotation brackets with significance stars.
• Stars: * p<0.05, ** p<0.01, *** p<0.001, ns for non-significant.

Pipeline Diagram

• Horizontal or vertical flow of processing stages.
• Boxes: rounded rectangles with stage name and brief description.
• Arrows: labeled with data counts or transformation type.
• Color-code stages by category (data collection, processing, validation).
• Keep text minimal; use supplementary caption for details.

Bar Chart

• Error bars: 95% CI (preferred) or SD, stated in caption.
• Individual data points overlaid if n < 30.
• Horizontal orientation for many categories.
• Sort by value (descending) unless order is meaningful.

Heatmap

• Annotate cells with values.
• Use sequential colormap for correlation (coolwarm diverging if centered at zero).
• Mask diagonal for correlation matrices.
• Cluster rows/columns if appropriate.

Style Rules

Colors

Wong colorblind-safe palette (default):

WONG = ['#000000', '#E69F00', '#56B4E9', '#009E73',
        '#F0E442', '#0072B2', '#D55E00', '#CC79A7']

Sequential palettes (for heatmaps):

• Positive values:

  Blues

  or

  Greens

• Diverging (centered at 0):

  coolwarm

  or

  RdBu_r

• Agreement matrices:

  YlOrRd

Rules:

• Never use red-green only distinctions.
• Use line style (solid, dashed, dotted) in addition to color for line plots.
• Use marker shape in addition to color for scatter plots.

Typography

• Font family: Arial or Helvetica (sans-serif).
• Panel labels: uppercase bold letter, top-left of each panel.

Layout

• Minimize white space while maintaining readability.
• Align multi-panel figures on a grid.
• Consistent axis ranges across comparable panels.
• No figure titles in the plot itself (title goes in the caption below).

Statistical Annotations

• Significance stars: * p<0.05, ** p<0.01, *** p<0.001
• Place above comparison brackets.
• Report exact p-value in the figure legend or caption, not in the plot.
• For AUC, correlation, or agreement: display in the legend with 95% CI.

Journal Specifications

Default dimensions (override from

figure_specs.md

• Single column: 3.5 in (88 mm) width
• 1.5 column: 5.0 in (127 mm) width
• Double column: 7.0 in (178 mm) width
• Full page: 7.0 x 9.5 in (178 x 241 mm)
• DPI: 300 minimum for halftone, 600 for line art
• File formats: PDF (vector, preferred) + PNG (300 DPI)
• No chartjunk: no 3D effects, no unnecessary gridlines, no decorative elements, no gradient fills

Multi-Panel Figures

For composite figures with multiple panels:

fig, axes = plt.subplots(nrows, ncols, figsize=(width, height))

# Label each panel
for ax, label in zip(axes.flat, 'ABCDEFGH'):
    ax.text(-0.15, 1.05, label, transform=ax.transAxes,
            fontsize=12, fontweight='bold', va='top')

Common layouts:

• 2-panel horizontal:

  figsize=(7.0, 3.5)

  , 1 row x 2 cols
• 2-panel vertical:

  figsize=(3.5, 7.0)

  , 2 rows x 1 col
• 2x2 grid:

  figsize=(7.0, 7.0)

  , 2 rows x 2 cols
• 3-panel:

  figsize=(7.0, 3.0)

  , 1 row x 3 cols

Use

plt.tight_layout()

fig.subplots_adjust()

Caption Writing

After generating each figure, draft a caption following these rules:

1. First sentence: Describe what the figure shows (type + key finding).
2. Subsequent sentences: Define abbreviations, explain symbols, state sample sizes.
3. Statistical details: Note the test used and significance threshold.
4. Format: "Figure {N}. {Caption text}" -- no bold, no title case.

Example:

Figure 1. Receiver operating characteristic curves comparing the diagnostic performance of the multi-agent pipeline (blue) and single-agent baseline (orange) for identifying incorrect Anki flashcard content. The area under the curve was 0.92 (95% CI: 0.89-0.95) for the multi-agent pipeline and 0.84 (95% CI: 0.80-0.88) for the single-agent baseline (DeLong test, p = 0.003). The dashed diagonal line represents chance performance.

Skill Interactions

/analyze-stats

/write-paper

/write-paper

Error Handling

• If data is insufficient for the requested figure type, explain what is needed and ask the user.
• If a figure exceeds journal dimension limits, resize and report the adjustment.
• If text overlaps in the figure, try

  tight_layout()

  , reduce font size, or adjust spacing.
• Never fabricate data points. If sample data is needed for a template demo, explicitly label it as "example data."

CLI Tools Available

ImageMagick, Ghostscript, FFmpeg are installed and can be used for post-processing:

# Figure DPI/format conversion for journal submission
magick input.png -density 300 -units PixelsPerInch output.tiff
magick input.png -resize 1200x -quality 95 output.jpg

# CMYK conversion (some print journals require this)
magick input.png -colorspace CMYK output.tiff

# Multi-panel figure assembly (A/B/C/D panels)
magick montage panelA.png panelB.png panelC.png panelD.png \
  -tile 2x2 -geometry +10+10 -density 300 combined.png

# Animated figure (GIF from frame sequence)
ffmpeg -framerate 2 -i frame_%03d.png -vf "scale=800:-1" output.gif

# Video from figure sequence (for supplementary materials)
ffmpeg -framerate 1 -i slide_%03d.png -c:v libx264 -pix_fmt yuv420p supplementary_video.mp4

AI Image Generation (Optional)

AI illustration is a supplementary option, not a requirement. Visual abstracts and figures can be completed without any API key using study figures and free illustration libraries.

If

GEMINI_API_KEY

generate_image.py

python ${CLAUDE_SKILL_DIR}/scripts/generate_image.py \
  "Clean medical illustration of a CT-guided lung biopsy procedure, \
   flat vector style, white background, no text" \
  --output output.png --aspect 16:9

Use for: procedural schematics, anatomical illustrations, pipeline diagrams. Always review AI output against the AI-Generated Figure Warning section above.

If

GEMINI_API_KEY

${CLAUDE_SKILL_DIR}/references/medical_illustration_sources.md

Language

• Code and figure text: English
• Communication with user: Match user's preferred language
• Medical terms: English only

Anti-Hallucination

• Never fabricate references. All citations must be verified via

  /search-lit

  with confirmed DOI or PMID. Mark unverified references as

  [UNVERIFIED - NEEDS MANUAL CHECK]

  .
• Never invent clinical definitions, diagnostic criteria, or guideline recommendations. If uncertain, flag with

  [VERIFY]

  and ask the user.
• Never fabricate numerical results — compliance percentages, scores, effect sizes, or sample sizes must come from actual data or analysis output.
• If a reporting guideline item, journal policy, or clinical standard is uncertain, state the uncertainty rather than guessing.