make-figures

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Generate publication-ready figures and visual abstracts for medical research papers. Supports ROC curves, forest plots, CONSORT/STARD/PRISMA flow diagrams, calibration plots, Kaplan-Meier curves, Bland-Altman plots, confusion matrices, pipeline diagrams, and journal-specific visual/graphical abstracts (python-pptx template-based).

Data & Documents 292 stars 71 forks Updated 4 days ago MIT

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Skill Content

# 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-...

Details

Author
Aperivue
Repository
Aperivue/medsci-skills
Created
5 months ago
Last Updated
4 days ago
Language
Python
License
MIT

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