make-figures

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 (ask in the user's preferred language):

   "Does this data contain patient identifiers (names, national ID / RRN, contact details, etc.)? If so, please de-identify it first with the /deidentify skill."

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 before generating any figure to confirm journal-specific requirements.

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Journal AI-Image Policies (CRITICAL — check BEFORE generation)

Synced with the user's global rule ~/.claude/rules/journal-ai-image-policies.md. The table below is the local copy used during autonomous workflow; the global rule is authoritative when conflicts arise.

Journal family	Policy on AI-generated images	Disclosure required
JACC family (incl. JACC: Asia, JACC Imaging, JACC EP, JACC BTS)	Prohibited without prior Editor-in-Chief permission (JACC pathway, PMC10167500)	Cover-letter pre-submission inquiry + ICMJE-style declaration
NEJM	AI image generation prohibited	N/A
Radiology / Radiology AI	Allowed with disclosure	Manuscript disclosure block
Nature family	Allowed with disclosure + license check	Methods + figure legend
Lancet family	Disclosure required, generation discouraged	Manuscript disclosure
Default (target unknown)	Treat as prohibited until confirmed	N/A

Hard rule: For JACC, NEJM, or any "unknown" target journal, never use Gemini / DALL-E / Midjourney / Stable Diffusion / Nano Banana to create images that will appear in figures, Central Illustrations, or graphical abstracts. AI text-editing of the manuscript prose remains acceptable subject to standard disclosure.

Default workflow when AI images are not allowed

1. SMART Servier Medical Art — https://smart.servier.com/, CC BY 4.0, free, 3,000+ vector medical icons (anatomy, organs, ethnicity-specific human figures, drugs, devices). Commercial / journal use allowed. Required attribution (1 line in figure legend OR methods):

   Anatomical icons modified from SMART Servier Medical Art (CC BY 4.0).

2. NIAID BioArt (https://bioart.niaid.nih.gov) — public domain (US Govt), microbiology / immunology / lab-tech focus.
3. BioRender (https://www.biorender.com) — institutional license usually required; use the exported "Publication-ready" PNG/TIFF and cite per BioRender publication policy.
4. For "diseased" variants not directly available (e.g., calcified vessel from a clean vessel): reuse the healthy asset and overlay disease markers via matplotlib scatter / Circle / PathPatch. Keeps the entire pipeline non-AI and reproducible.

Asset directory convention

manuscript/figures/_assets_servier/      # CC BY 4.0 source PNGs
manuscript/figures/_assets_servier/CITATION.md   # source URL + download date per asset
manuscript/figures/_assets_data/         # data-driven raster (R / matplotlib heat maps, KM, etc.)
manuscript/figures/_legacy/              # archived prior versions

Composition scripts should load only from _assets_servier/ and _assets_data/. If a script imports from _assets_ai/, treat it as a policy violation for JACC/NEJM/unknown targets.

When a figure is produced by a data-driven .py/.R script (ROC, forest, KM, calibration, heat maps), lint that script before finalizing with the /analyze-stats code-quality gate (check_generated_code.py {script} --strict): it catches a missing plotting seed for any bootstrapped CI band, a hardcoded absolute data path, or a hand-typed data literal that should have been read from the analysis CSV.

Decoration vs information

Even when AI images are allowed, AI-generated illustrations are immediately recognizable to experienced reviewers (small decorative icons that add no information, overly uniform layouts, generic clip-art style). For high-impact submissions, prefer Servier / BioArt / BioRender + matplotlib overlays over AI.

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DPI and Resolution Guide

Output	Minimum DPI	Notes
Journal halftone (photos, screenshots)	300	Standard for most journals
Journal line art (diagrams, graphs)	600	Required by Radiology, most Elsevier journals
Poster presentation	150-200	Lower is acceptable for large-format prints
Screen/web only	72-150	Not for print submission

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

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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

Status	Example Journals
Mandatory	European Radiology (from 1st revision, all Original Articles)
Encouraged	Abdominal Radiology, JCO, Annals of Internal Medicine
Voluntary	Most other journals — improves social media visibility

Check the target journal profile (write-paper/references/journal_profiles/) for specific visual abstract requirements before starting.

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

Template	File	Use When
European Radiology	european_radiology.pptx	Submitting to Eur Radiol
MedSci Default	medsci_default.pptx	Any journal without official template
JACC Central Illustration	jacc_central_illustration.pptx	JACC family journals (use --type central-illustration)

To add a new journal template: see ${CLAUDE_SKILL_DIR}/references/visual_abstract_templates/template_guide.md.

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Central Illustration vs Visual Abstract

A Central Illustration (CI) is not a Visual Abstract (VA). They serve different purposes and follow different rules. JACC family journals (JACC, JACC: Asia, JACC: Cardiovascular Imaging, JACC: Heart Failure, JACC: CardioOncology, JACC: Clinical Electrophysiology, JACC: Basic to Translational Science) require a Central Illustration with every Original Article. Reference: Fuster V, Mann DL. JACC. 2019;74(22):2816–2820.

Aspect	Central Illustration	Visual Abstract
Purpose	Single key finding / take-home message	Methods + Results pictorial summary
Where in paper	End of Results / start of Discussion	Beginning of paper
Methods content	None	Required
Audience	Cardiovascular clinicians + journal-issue readers	Broad including non-specialists / social media
Used by	All JACC family + JACC: Asia	Originally JACC: Basic to Translational Science
Text density	Minimal (graphical priority)	More allowed (methods labels)
Bar graphs	OK if they capture entire message	Avoid — use ↑↓ arrows
Default complexity	1–3 visual zones	Q→M→R three blocks

Fuster-Mann five rules (CI must pass all)

1. Know the message. One finding, not study design + multiple findings.
2. Convey graphically, not textually. Even a simple KM curve is OK.
3. Avoid using too much text. Replace with icons or arrows.
4. Avoid secondary messages. ≤ 5 seconds for a viewer to state the main finding.
5. Simplicity is superior. Default to fewer panels.

Full guidance and validation thresholds: ${CLAUDE_SKILL_DIR}/references/jacc_central_illustration_principles.md.

CI mode invocation

python ${CLAUDE_SKILL_DIR}/scripts/generate_visual_abstract.py \
  --type central-illustration \
  --visual figures/central_illustration_v2.png \
  --citation "FirstAuthor Last et al. Journal Name 2026; vol(issue):pages." \
  --output submission/jacc_asia/central_illustration.pptx \
  --ci-zones 3 --ci-label-words 22 --ci-numerical-points 2 \
  --ci-raw-text "warranty drops to 3 years in age 45+ with cardiometabolic burden; MASLD HR 1.77"

CI mode validates before rendering and rejects (exit 2) if any of: zones > 3, label words > 30, numerical points > 4, or methodology terms (cohort flow / inclusion / exclusion / study design / enrollment / randomized / sample size / CONSORT / PRISMA / STARD) appear in --ci-raw-text. Override individual rules with --ci-allow {zones|words|numerical|methods} only when you have a defensible reason.

The JACC submission PPTX is a 10×7.5 in slide with 4 placeholders (citation textbox, content picture, footer textbox reserved, JACC logo). The red border + blue "CENTRAL ILLUSTRATION:" header are applied by JACC editorial after acceptance — authors submit only the content figure + citation.

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Workflow

Step 1: Specify

Before specifying figure type, read ${CLAUDE_SKILL_DIR}/references/design_principles.md — identify (1) the one-sentence key message, (2) audience and reading-time budget, and (3) whether a figure is the right vehicle (vs a small table or in-line text). The five strategies in that file shift Step 1 from "which chart fits the data" to "what should the reader remember 10 seconds later." Skip only when the figure is mandated by a reporting guideline (e.g., PRISMA / CONSORT flow), and even then apply the cognitive-load checklist.

For reporting-guideline figures, also load ${CLAUDE_SKILL_DIR}/references/reporting_guideline_figure_map.md — the 14-row table tells you which guideline mandates which figures and whether this skill ships an official template (✅), generic flow only (⚠️), or needs manual production (❌). Critical for AI-extension guidelines (CONSORT-AI, STARD-AI, TRIPOD+AI, CLAIM 2024, DECIDE-AI).

For medical AI / engineering pipeline figures (DICOM workflow, annotation pipeline, federated learning topology, model architecture), also load ${CLAUDE_SKILL_DIR}/references/pipeline_concepts_medical_ai.md — canonical layouts, required annotations, and tool selection per type.

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 (sections A–G; section G adds cognitive-load and template-fidelity checks)
   • Data plots: ${CLAUDE_SKILL_DIR}/references/critic_rubrics/data_plot.md (sections A–G; section G adds calibration / fairness / colorblind+redundant / dataset-flow / decision-curve checks for medical AI papers)
   • For PRISMA / CONSORT / STARD / STROBE specifically, also read ${CLAUDE_SKILL_DIR}/references/flow_diagram_lessons.md — five production lessons covering official-template fidelity, PDF export fidelity (VML fallback), docx XML escape, sequential placeholder mapping, and frozen-version sync with the manuscript.
   • For AI-extension guidelines (CONSORT-AI, STARD-AI, TRIPOD+AI, CLAIM 2024, DECIDE-AI), also read ${CLAUDE_SKILL_DIR}/references/reporting_guideline_figure_map.md — the row for the target guideline lists mandatory figures and which ones this skill cannot template (production path documented per row).
   • For medical-AI pipeline / DICOM / federated / architecture figures, also read ${CLAUDE_SKILL_DIR}/references/pipeline_concepts_medical_ai.md.
3. If exemplars exist in ${CLAUDE_SKILL_DIR}/references/exemplar_diagrams/{type}/, Read 1–3 of them plus their _why.md notes. For a non-flow data plot (forest, ROC, KM, calibration), read the matching anatomy model in ${CLAUDE_SKILL_DIR}/references/exemplar_plots/ (e.g., forest_plot.md).
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 (see the manifest format below) so downstream steps (/write-paper Phase 2 embedding and Phase 7 DOCX build) and future critic passes can see the history.

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, etc.

For PPTX outputs (visual abstract, central illustration, or any deck the figure will live in): run the Mac-compatibility validator before delivery. PowerPoint Mac silently drops TIFF, renders <a:sp3d> 3-D bevels as red outlines that PDF export does not show, and refuses to open files whose app.xml slide count disagrees with the actual slide XML files. This script catches all four classes of defect codified in ~/.claude/rules/pptx-mac-compatibility.md:

python ${CLAUDE_SKILL_DIR}/scripts/validate_pptx_mac_compat.py \
    figures/visual_abstract.pptx \
    --json figures/visual_abstract.mac_compat.json \
    --strict

Exit code 1 means at least one FAIL — fix per the fix: field in the JSON report and re-render the PPTX before delivery. Exit code 0 with WARN is acceptable. Skip this step when the figure is PNG/PDF only (no PPTX).

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

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Study-Type Figure Sets

When the study type is known (from /write-paper Phase 0 or user specification), auto-detect and generate the complete required figure set without asking for each figure individually.

Study Type (Guideline)	Required Figures
Diagnostic accuracy (STARD)	STARD flow diagram, ROC curve, confusion matrix, calibration plot
AI validation (TRIPOD+AI / CLAIM)	Flow diagram, ROC curve, confusion matrix, calibration plot, feature importance or SHAP, Grad-CAM (if imaging)
Meta-analysis (PRISMA)	PRISMA flow diagram, forest plot, funnel plot
DTA meta-analysis (PRISMA-DTA)	PRISMA flow diagram, paired forest plot (Se + Sp), SROC curve, Deeks funnel plot
Observational cohort (STROBE)	Flow diagram, Kaplan-Meier curves (if survival endpoint)
RCT (CONSORT)	CONSORT flow diagram, primary endpoint figure

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 section at the bottom of the manifest records any residual PARTIAL items and the rationale for accepting them.

This manifest is consumed by /write-paper Phase 2 (figure embedding) and Phase 7 (DOCX build). It MUST exist after figure generation completes. Verify the file is non-empty before finishing.

Flow diagram generation rule: STARD/CONSORT/PRISMA/STROBE flow diagrams MUST use the standardized R pipeline scripts/generate_flow_diagram.R (DiagrammeR + Graphviz dot + rsvg). This is the single canonical tool for all four reporting-guideline flow diagrams. Do NOT use matplotlib FancyBboxPatch (manual coordinates break when text changes, and patches distort when embedded in DOCX). Do NOT use D2 for new flow diagrams (font control is weak, overlap requires manual post-processing). The legacy D2 recipe remains documented below as a fallback only when R is unavailable.

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>, •). 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 pattern (preferred for complex flows):

When the flow has derived counts, stopifnot() reconciliation, multi-rank {rank=same; ... } constraints, or exclusion side-cars that the generic YAML dispatcher cannot express cleanly, write a per-project create_figure1.R directly (same DiagrammeR + DiagrammeRsvg + rsvg stack, sprintf'd dot string). This is the dominant pattern when the generic YAML dispatcher cannot capture the flow:

• STROBE cohort: <project>/manuscript/figures/create_figure1.R
• STARD: <project>/Analysis/figures/create_figure1.R or <project>/figures/v2_monochrome/create_figure1.R
• PRISMA / PRISMA-DTA: <project>/5_Figures/create_figure1.R or <project>/analysis/create_figure1.R
• CONSORT-edu (naturalistic allocation): <project>/figures/v2_monochrome/create_figure1.R

Copy the STYLE_HEADER (graph/node/edge attrs) verbatim from any exemplar; then customise nodes, edges, and {rank=same} blocks. Use read.csv() for cohort counts when possible; if hardcoded, every number must have a source comment referencing manuscript line / CSV cell / screening log row.

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. D2 PDF is vector; D2 PNG needs the resize step to match publication density.

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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:

Type	Use Case	Key Library
ROC Curve	Diagnostic accuracy	matplotlib, sklearn
Forest Plot	Meta-analysis	matplotlib
Calibration Plot	Prediction model	matplotlib
KM Curve	Survival analysis	lifelines, matplotlib
Bland-Altman	Agreement	matplotlib
Confusion Matrix	Classification	seaborn
Box/Violin Plot	Group comparison	seaborn
Bar Chart	Categorical comparison	matplotlib
Heatmap	Correlation/agreement	seaborn

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.

Type	Recommended Tool	Why
STROBE (cohort / cross-sectional)	scripts/generate_flow_diagram.R --type strobe	Single canonical tool; auto-layout; vector PDF + 300/600 dpi PNG
CONSORT (RCT)	scripts/generate_flow_diagram.R --type consort	Same pipeline; monochrome Arial default
PRISMA 2020 (SR/MA)	scripts/generate_flow_diagram.R --type prisma	Faithfully implements PRISMA 2020 structure; avoids PRISMA2020 R package's webshot-based raster PDF issue
STARD (DTA)	scripts/generate_flow_diagram.R --type stard	Same pipeline; supports 2x2 reference-standard split
Pipeline Diagram	D2 (legacy)	Until pipeline-diagram support is added to the R script

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> → PDF + 300/600 dpi PNG. Templates in references/exemplar_diagrams/{strobe,consort,prisma,stard}/template_input.yaml.

Official Reporting Guideline Templates → templates/official/

When a journal requires the canonical, statement-issued template (rather than the auto-laid-out R version), use the bundled official files in templates/official/{prisma2020,consort2010,stard2015,spirit2013}/.

Guideline	What ships	When to use
PRISMA 2020	Locally built .pptx (4 variants) + fill_prisma_template.py	Reviewer asks for the official PRISMA 2020 layout, or you want editable PowerPoint instead of an R-rendered PDF.
STROBE (cohort)	Parametric .pptx builder build_strobe_template.py (single-script, takes YAML config)	Cohort/case-control study Figure 1 when co-authors want PowerPoint they can hand-edit. Auto-fits text, content-fits slide, dashed-border exclusion side-branches with strictly-horizontal connectors. Optional left-side phase column (omit stages: for the plain STROBE convention; include it for the PRISMA-style Identification/Screening/Inclusion/Analysis column). Pair with generate_flow_diagram.R --type strobe for the vector PDF/TIFF submission file.
CONSORT 2025	Official .docx flow diagram + checklist	RCT submissions to journals that mandate the consort-spirit.org template.
STARD 2015	Official .pdf flow diagram + .docx checklist	Diagnostic accuracy studies; flow diagram is fixed PDF, checklist is editable.
SPIRIT 2025	Official .docx participant timeline + checklist	Trial protocols.

Refresh / fill workflow:

# Refresh from canonical sources (CC-BY 4.0 / public-statement licenses)
bash ${CLAUDE_SKILL_DIR}/scripts/fetch_official_templates.sh

# Build PRISMA 2020 .pptx (one-time; site blocks programmatic .docx fetch)
python3 ${CLAUDE_SKILL_DIR}/scripts/build_prisma2020_template.py \
    --variant new \
    --out ${CLAUDE_SKILL_DIR}/templates/official/prisma2020/PRISMA_2020_flow_new_v1.pptx

# Fill counts — positional 10-tuple matching most SR/MA workflows:
#   n_db, n_dup, n_screened, n_screen_excluded,
#   n_sought, n_assessed, n_excl_r1, n_excl_r2, n_excl_r3, n_studies
python3 ${CLAUDE_SKILL_DIR}/scripts/fill_prisma_template.py \
    --template ${CLAUDE_SKILL_DIR}/templates/official/prisma2020/PRISMA_2020_flow_new_v1.pptx \
    --counts "315,122,186,7,111,204,102,84,3,15" \
    --out fig1_prisma_filled.pptx

# Or use full JSON mapping for studies with non-standard PRISMA splits
python3 ${CLAUDE_SKILL_DIR}/scripts/fill_prisma_template.py \
    --template ${CLAUDE_SKILL_DIR}/templates/official/prisma2020/PRISMA_2020_flow_new_v1.pptx \
    --counts-file my_counts.json \
    --out fig1_prisma_filled.pptx

# STROBE — parametric single-script builder (cohort study; spine structure varies per study).
# YAML schema: stages, spine (id/stage/text), exclusions (after/text). Consecutive same-stage
# rows share one phase label automatically. Stage box fills auto-pick readable text color.
python3 ${CLAUDE_SKILL_DIR}/scripts/build_strobe_template.py \
    --config figures/figure1_strobe.yaml \
    --out    figures/figure1_strobe.pptx

For STROBE the canonical KJR/Radiology/BMJ submission flow is:

1. Render the vector submission file via the auto-fitting Graphviz path: Rscript ${CLAUDE_SKILL_DIR}/scripts/generate_flow_diagram.R --type strobe --config figures/figure1_strobe_graphviz.yaml --out figures/figure1
2. Build the editable PowerPoint companion via build_strobe_template.py so co-authors and senior reviewers can adjust prose/positioning before sign-off.
3. Re-export the final PPTX to PDF/TIFF only after co-author edits are integrated.

See templates/official/NOTES.md for licenses, attribution, and refresh notes.

Visual / Graphical Abstracts → python-pptx Template Generator

Type	Recommended Tool
Visual Abstract (any journal)	generate_visual_abstract.py with PPTX template
Visual element illustration	Study's own figures (preferred), or free libraries (Servier/NIAID)
Medical Illustration	See ${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)

Type	Use Case	Key Library	Output
ROC Curve	Diagnostic accuracy	matplotlib, sklearn	Single/multi-model ROC with AUC
Forest Plot	Meta-analysis	matplotlib	Effect sizes with CIs, diamond summary
Calibration Plot	Prediction model	matplotlib	Observed vs predicted with Hosmer-Lemeshow
KM Curve	Survival analysis	lifelines, matplotlib	With risk table, log-rank p
Bland-Altman	Agreement	matplotlib	With mean diff, +/-1.96 SD limits
Confusion Matrix	Classification	seaborn	Heatmap with percentages
Box/Violin Plot	Group comparison	seaborn	With individual data points
Pipeline Diagram	Methods figure	D2 (preferred) or matplotlib	Processing/workflow steps
Bar Chart	Categorical comparison	matplotlib	With error bars (CI or SD)
Heatmap	Correlation/agreement	seaborn	Color-coded matrix

---

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 (see the R flow diagram recipe above). Do not fall back to matplotlib for flow diagrams — manual coordinates break when text changes and patches distort in DOCX. D2 remains a documented legacy fallback only when R is unavailable.

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

Caption ↔ flow-SSOT reconciliation (before Step 5 Export). The flow-diagram config (the YAML/script that generate_flow_diagram.R consumes) is the single source of truth for participant counts. A hand-written Figure 1 caption drifts from it whenever the cohort is re-locked but the caption is not — the classic "caption says n = 1,284 analytic, diagram box says n = 998" defect, which surfaces only at submission. Re-derive the caption counts from the flow config and reconcile:

python3 ${CLAUDE_SKILL_DIR}/scripts/derive_figure_legend_counts.py \
  --flow-config figures/figure1_strobe_graphviz.yaml \
  --manuscript manuscript/index.qmd \
  --out qc/figure_legend_counts.json --strict

Any n = N in the caption that is not a box count in the flow config is a MISMATCH (stale caption) — update the caption from the config, never the reverse. This pairs with numerical-safety's "re-derive prose counts every revision" rule and with /sync-submission's cross-document N checks. (The reconciler is stdlib-only and parses the config as text, so it works regardless of the flow tool.)

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

Element	Font Size	Weight
Figure title (if any)	10 pt	Bold
Axis label	9 pt	Regular
Tick label	8 pt	Regular
Legend text	8 pt	Regular
Annotation	8 pt	Regular
Panel label (A, B, C)	12 pt	Bold

• 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 if journal-specific):

• 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() or fig.subplots_adjust() for spacing.

---

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

When	Call	Purpose
Need statistical values for plot	/analyze-stats	Get computed values (AUC, CI, p-values)
Flow diagram for manuscript	/write-paper Phase 2	Coordinate with Tables & Figures plan
Caption review	/write-paper Phase 7	Final polish pass

---

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 is set, the generate_image.py script can generate illustrations:

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 is not set, guide the user to free illustration resources: see ${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.