What problem does it solve?
This Skill helps you produce publication-quality diagrams and data plots for ML papers instead of spending hours manually styling figures or guessing chart types.
Core Features & Use Cases
- Architecture & workflow diagrams: Uses Gemini to turn paper context into architecture/system diagrams (boxes, arrows, labeled relationships).
- Data-driven charts: Chooses an appropriate matplotlib/seaborn chart type from experimental results (line, bar, scatter, heatmap, ablations, etc.) and generates figures with publication styling.
- Publication-grade consistency: Supports venue-aware sizing, export conventions, and figure patterns suitable for conferences and LaTeX workflows.
- Common use case: Turn a method section plus a results table into a complete set of camera-ready figures (e.g., system overview diagram + ablation plot + training curves).
Quick Start
Ask the AI to generate a NeurIPS-ready “Our method” figure by providing your paper section describing the system and your experiment results table, then request both an architecture diagram and the best-matching data plot type.
