Academic Plotting
Turns paper sections and experiment results into conference-ready figures without the usual matplotlib hell. You paste in your methods paragraph or a results table, and it extracts what needs to be visualized. For architecture diagrams it generates them via Gemini in one of three visual styles (hand-drawn sketch, modern minimal, or icon-rich). For data it auto-picks chart types and renders with matplotlib/seaborn using proper academic styling. The style guide is opinionated, which is good because most ML paper figures look identical. Handles the tedious stuff like consistent colors, readable fonts, and making "your method" stand out in comparisons. Built for NeurIPS/ICML submissions where figure quality actually matters.
- skills.sh installs
- 1.3K
- GitHub stars
- 14.4K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add orchestra-research/ai-research-skills --skill academic-plotting --agent claude-codeInstalls into .claude/skills of the current project.
Files
Academic Plotting for ML Papers
Generate publication-quality figures for ML/AI conference papers. Two distinct workflows:
- Diagram figures (architecture, system design, workflows, pipelines) — AI image generation via Gemini
- Data figures (line charts, bar charts, scatter plots, heatmaps, ablations) — matplotlib/seaborn
When to Use Which Workflow
| Figure Type | Tool | Why |
|---|---|---|
| Architecture / system diagram | Gemini (Workflow 1) | Complex spatial layouts with boxes, arrows, labels |
| Workflow / pipeline / lifecycle | Gemini (Workflow 1) | Multi-step processes with connections |
| Bar chart, line plot, scatter | matplotlib (Workflow 2) | Precise numerical data, reproducible |
| Heatmap, confusion matrix | matplotlib/seaborn (Workflow 2) | Structured grid data |
| Ablation table as chart | matplotlib (Workflow 2) | Grouped bars or line comparisons |
| Pie / donut chart | matplotlib (Workflow 2) | Proportional data (use sparingly in ML papers) |
| Training curves | matplotlib (Workflow 2) | Loss/accuracy over steps/epochs |
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- Repository
- github.com/orchestra-research/ai-research-skills
- Categories
- Code Review & Quality, AI & Agent Building, Data Science & ML
- First seen
- Jun 3, 2026
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