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 a secondary Codex agent 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:

| 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
• Which need manual creation (architecture diagrams, etc.)
• Which are comparison tables (generate as LaTeX)

Step 2: Set Up Plotting Environment

Create a shared style configuration script:

# paper_plot_style.py — shared across all figure scripts
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams.update({
    'font.size': FONT_SIZE,
    'font.family': 'serif',
    'font.serif': ['Times New Roman', 'Times', 'DejaVu Serif'],
    'axes.labelsize': FONT_SIZE,
    'axes.titlesize': FONT_SIZE + 1,
    'xtick.labelsize': FONT_SIZE - 1,
    'ytick.labelsize': FONT_SIZE - 1,
    'legend.fontsize': FONT_SIZE - 1,
    'figure.dpi': DPI,
    'savefig.dpi': DPI,
    'savefig.bbox': 'tight',
    'savefig.pad_inches': 0.05,
    'axes.grid': False,
    'axes.spines.top': False,
    'axes.spines.right': False,
    'text.usetex': False,  # set True if LaTeX is available
    'mathtext.fontset': 'stix',
})

# Color palette
COLORS = plt.cm.tab10.colors  # or Set2, or colorblind-safe

def save_fig(fig, name, fmt=FORMAT):
    """Save figure to FIG_DIR with consistent naming."""
    fig.savefig(f'{FIG_DIR}/{name}.{fmt}')
    print(f'Saved: {FIG_DIR}/{name}.{fmt}')

Step 3: Auto-Select Figure Type

Use this decision tree for data-driven figures (inspired by Imbad0202/academic-research-skills):

| Data Pattern | Recommended Type | Size | |-------------|-----------------|------| | X=time/steps, Y=metric | Line plot | 0.48\textwidth | | Methods × 1 metric | Bar chart | 0.48\textwidth | | Methods × multiple metrics | Grouped bar / radar | 0.95\textwidth | | Two continuous variables | Scatter plot | 0.48\textwidth | | Matrix / grid values | Heatmap | 0.48\textwidth | | Distribution comparison | Box/violin plot | 0.48\textwidth | | Multi-dataset results | Multi-panel (subfigure) | 0.95\textwidth | | Prior work comparison | LaTeX table | — |

Step 4: Generate Each Figure

For each figure in the plan, create a standalone Python script:

Line plots (training curves, scaling):

# gen_fig2_training_curves.py
from paper_plot_style import *
import json

with open('figures/exp_results.json') as f:
    data = json.load(f)

fig, ax = plt.subplots(1, 1, figsize=(5, 3.5))
ax.plot(data['steps'], data['fac_loss'], label='Factorized', color=COLORS[0])
ax.plot(data['steps'], data['crf_loss'], label='CRF-LR', color=COLORS[1])
ax.set_xlabel('Training Steps')
ax.set_ylabel('Cross-Entropy Loss')
ax.legend(frameon=False)
save_fig(fig, 'fig2_training_curves')

Bar charts (comparison, ablation):

fig, ax = plt.subplots(1, 1, figsize=(5, 3))
methods = ['Baseline', 'Method A', 'Method B', 'Ours']
values = [82.3, 85.1, 86.7, 89.2]
bars = ax.bar(methods, values, color=[COLORS[i] for i in range(len(methods))])
ax.set_ylabel('Accuracy (%)')
# Add value labels on bars
for bar, val in zip(bars, values):
    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.3,
            f'{val:.1f}', ha='center', va='bottom', fontsize=FONT_SIZE-1)
save_fig(fig, 'fig3_comparison')

Comparison tables (LaTeX, for theory papers):

\begin{table}[t]
\centering
\caption{Comparison of estimation error bounds. $n$: sample size, $D$: ambient dim, $d$: latent dim, $K$: subspaces, $n_k$: modes.}
\label{tab:bounds}
\begin{tabular}{lccc}
\toprule
Method & Rate & Depends on $D$? & Multi-modal? \\
\midrule
\citet{MinimaxOkoAS23} & $n^{-s'/D}$ & Yes (curse) & No \\
\citet{ScoreMatchingdistributionrecovery} & $n^{-2/d}$ & No & No \\
\textbf{Ours} & $\sqrt{\sum n_k d_k / n}$ & No & Yes \\
\bottomrule
\end{tabular}
\end{table}

Architecture/pipeline diagrams (MANUAL — outside this skill's scope):

• These require manual creation using draw.io, Figma, Keynote, or TikZ
• This skill can generate a rough TikZ skeleton as a starting point, but do not expect publication-quality results
• If the figure already exists in figures/, preserve it and generate only the LaTeX \includegraphics snippet
• Flag as [MANUAL] in the figure plan and latex_includes.tex

Step 5: Run All Scripts

# Run all figure generation scripts
for script in gen_fig*.py; do
    python "$script"
done

Verify all output files exist and are non-empty.

Step 6: Generate LaTeX Include Snippets

For each figure, output the LaTeX code to include it:

% === Fig 2: Training Curves ===
\begin{figure}[t]
    \centering
    \includegraphics[width=0.48\textwidth]{figures/fig2_training_curves.pdf}
    \caption{Training curves comparing factorized and CRF-LR denoising.}
    \label{fig:training_curves}
\end{figure}

Save all snippets to figures/latex_includes.tex for easy copy-paste into the paper.

Step 7: Figure Quality Review with REVIEWER_MODEL

Send figure descriptions and captions to GPT-5.4 for review:

spawn_agent:
  model: gpt-5.5
  reasoning_effort: xhigh
  message: |
    Review these figure/table plans for a [VENUE] submission.

    For each figure:
    1. Is the caption informative and self-contained?
    2. Does the figure type match the data being shown?
    3. Is the comparison fair and clear?
    4. Any missing baselines or ablations?