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-5.6-sol — 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:

| 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. Then render-then-verify: re-open each RENDERED PDF/PNG (not the script) and self-check — no clipped labels, no legend covering data, every number/label readable at final print size. This self-check happens BEFORE the Step 7 review, so the reviewer's budget goes to substance, not to catching clipped axes.

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-5.6-Sol for review:

mcp__codex__codex:
  model: gpt-5.6-sol
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    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?
    5. Would a different visualization be more effective?

    [list all figures with captions and descriptions]

Step 8: Quality Checklist

The checklist is PARTITIONED (pattern from Anthropic's Claude Science figure-style skill, Apache-2.0): correctness rules always bind — they are about whether the figure tells the truth, have no aesthetic content, and no style choice may override them; guidance rules are defaults — they produce a clean result, but a deliberate, stated alternative may override them.

Correctness — always binds, verify against the DATA before the render:

• Excluded data never enters summaries — a row excluded/flagged in the source either disappears entirely or is drawn visibly distinct (open / hatched marker, named in the key); it never feeds a mean/CI plotted alongside included rows
• Captions and any claim-like title text are tested against EVERY plotted row — if one category contradicts the claim, qualify it ("on 3 of 4 benchmarks") or downgrade to a description; a figure that overclaims is wrong even if it renders beautifully
• Comparable conditions only — arms measured under different N / budget / protocol are not drawn as visual peers; separate them or mark the difference in the caption
• State n and what was held fixed — every panel with a summary mark says n and the unit of replication (panel or caption)
• Render-then-verify — the Step-5 self-check on the RENDERED PDF/PNG (not the script) actually happened: no clipped labels, no legend covering data, every number/label readable at final print size

Guidance — strong defaults (from pedrohcgs/claude-code-my-workflow), a deliberate stated alternative may override — EXCEPT items that Key Rules below make hard (vector-PDF output and no-titles-inside-figures are Key Rules: treat those two as binding, not overridable):

• Font size readable at printed paper size (not too small)
• Colors distinguishable in grayscale (print-friendly)
• No title inside figures — titles go only in LaTeX \caption{} (from pedrohcgs)
• Legend does not overlap data
• Axis labels have units where applicable
• Axis labels are publication-quality (not variable names like emp_rate)
• Figure width fits single column (0.48\textwidth) or full width (0.95\textwidth)
• PDF output is vector (not rasterized text)
• No matplotlib default title (remove plt.title for publications)
• Serif font matches paper body text (Times / Computer Modern)
• Colorblind-accessible (if using colorblind palette)

Output

figures/
├── paper_plot_style.py          # shared style config
├── gen_fig1_architecture.py     # per-figure scripts
├── gen_fig2_training_curves.py
├── gen_fig3_comparison.py
├── fig1_architecture.pdf        # generated figures
├── fig2_training_curves.pdf
├── fig3_comparison.pdf
├── latex_includes.tex           # LaTeX snippets for all figures
└── TABLE_*.tex                  # standalone table LaTeX files

Key Rules

• Every figure must be reproducible — save the generation script alongside the output
• Do NOT hardcode data — always read from JSON/CSV files
• Use vector format (PDF) for all plots — PNG only as fallback
• No decorative elements — no background colors, no 3D effects, no chart junk
• Consistent style across all figures — same fonts, colors, line widths
• Colorblind-safe — verify with https://davidmathlogic.com/colorblind/ if needed
• One script per figure — easy to re-run individual figures when data changes
• No titles inside figures — captions are in LaTeX only
• Comparison tables count as figures — generate them as standalone .tex files

Figure Type Reference

Type	When to Use	Typical Size
Line plot	Training curves, scaling trends	0.48\textwidth
Bar chart	Method comparison, ablation	0.48\textwidth
Grouped bar	Multi-metric comparison	0.95\textwidth
Scatter plot	Correlation analysis	0.48\textwidth
Heatmap	Attention, confusion matrix	0.48\textwidth
Box/violin	Distribution comparison	0.48\textwidth
Architecture	System overview	0.95\textwidth
Multi-panel	Combined results (subfigures)	0.95\textwidth
Comparison table	Prior bounds vs. ours (theory)	full width

Acknowledgements

Design pattern (type × style matrix) inspired by baoyu-skills. Publication style defaults and figure rules from pedrohcgs/claude-code-my-workflow. Visualization decision tree from Imbad0202/academic-research-skills.