/paper-figure

name: paper-figure
description: "Generate publication-quality figures and tables from experiment results. Use when user says \"画图\", \"作图\", \"generate figures\", \"paper figures\", or needs plots for a paper."
argument-hint: "[figure-plan-or-data-path]"
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, mcp__codex__codex, mcp__codex__codex-reply

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-6-astra`** — 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 (%)')
#