Paper Figure

# Paper Figure: Publication-Quality Plots from Experiment Data

Generate all figures and tables for a paper based on: **$ARGUMENTS**

## Scope: What This Skill Can and Cannot Do

| Category | Can auto-generate? | Examples |
|----------|-------------------|----------|
| **Data-driven plots** | ✅ Yes | Line plots (training curves), bar charts (method comparison), scatter plots, heatmaps, box/violin plots |
| **Comparison tables** | ✅ Yes | LaTeX tables comparing prior bounds, method features, ablation results |
| **Multi-panel figures** | ✅ Yes | Subfigure grids combining multiple plots (e.g., 3×3 dataset × method) |
| **Architecture/pipeline diagrams** | ❌ No — manual | Model architecture, data flow diagrams, system overviews. At best can generate a rough TikZ skeleton, but **expect to draw these yourself** using tools like draw.io, Figma, or TikZ |
| **Generated image grids** | ❌ No — manual | Grids of generated samples (e.g., GAN/diffusion outputs). These come from running your model, not from this skill |
| **Photographs / screenshots** | ❌ No — manual | Real-world images, UI screenshots, qualitative examples |

**In practice:** For a typical ML paper, this skill handles ~60% of figures (all data plots + tables). The remaining ~40% (hero figure, architecture diagram, qualitative results) need to be created manually and placed in `figures/` before running `/paper-write`. The skill will detect these as "existing figures" and preserve them.

## Constants

- **STYLE = `publication`** — Visual style preset. Options: `publication` (default, clean for print), `poster` (larger fonts), `slide` (bold colors)
- **DPI = 300** — Output resolution
- **FORMAT = `pdf`** — Output format. Options: `pdf` (vector, best for LaTeX), `png` (raster fallback)
- **COLOR_PALETTE = `tab10`** — Default matplotlib color cycle. Options: `tab10`, `Set2`, `colorblind` (deuteranopia-safe)
- **FONT_SIZE = 10** — Base font size (matches typical conference body text)
- **FIG_DIR = `figures/`** — Output directory for generated figures
- **REVIEWER_MODEL = `gpt-5.5`** — Model used via Codex MCP for figure quality review.

## Inputs

1. **PAPER_PLAN.md** — figure plan table (from `/paper-plan`)
2. **Experiment data** — JSON files, CSV files, or screen logs in `figures/` or project root
3. **Existing figures** — any manually created figures to preserve

If no PAPER_PLAN.md exists, scan for data files and ask the user which figures to generate.

## Workflow

### Step 1: Read Figure Plan

Parse the Figure Plan table from PAPER_PLAN.md:

```markdown
| ID | Type | Description | Data Source | Priority |
|----|------|-------------|-------------|----------|
| Fig 1 | Architecture | ... | manual | HIGH |
| Fig 2 | Line plot | ... | figures/exp.json | HIGH |
```

Identify:
- Which figures can be auto-generated from data