moralespanitz/figure-agent

<SUBAGENT-STOP> If you were dispatched as a subagent to generate a specific figure from data or create a specific diagram, skip this skill. Do the task and return structured results immediately. </SUBAGENT-STOP> <HARD-GATE> NEVER fabricate figure data. Every figure must be backed by real experimental results or a traceable data source. If no data is provided, ask for it before generating anything. </HARD-GATE>

Figure Agent Skill

Decision-driven figure generation for conference papers. Analyzes the paper content to determine figure types, generates Matplotlib/Seaborn code for quantitative figures, and iterates on quality via critic feedback.

Figure Type Decision Agent

When generating figures for a paper, first analyze the content to determine the appropriate figure type:

Quantitative Figures (Code-generated)

Use when the figure shows actual experimental results, metrics, or data:

| Figure Type | Best For | Matplotlib Function | |-------------|----------|-------------------| | Bar chart | Comparing discrete conditions, ablations | plt.bar() | | Line plot | Convergence curves, training trajectories | plt.plot() | | Scatter plot | Correlation analysis, parameter sensitivity | plt.scatter() | | Heatmap | Ablation matrices, hyperparameter grids | plt.imshow() or sns.heatmap() | | Box plot / Violin | Distribution of results across seeds | plt.boxplot() or sns.violinplot() | | Histogram | Result distributions | plt.hist() | | Stacked bar | Comparison across multiple metrics | plt.bar(stacked=True) | | Grouped bar | Multiple methods across conditions | plt.bar() with offset positions |

Architecture/Conceptual Figures (Diagram)

Use when the figure shows the proposed method, system architecture, or conceptual framework. These should be drawn with Excalidraw or a similar diagramming tool. For code-generated alternatives, use Matplotlib patches.

Selection Criteria

Is the figure based on experimental data?
├── YES → Is it a comparison (method A vs B) or relationship (parameter vs metric)?
│   ├── Bar/Line/Scatter/Box → Generate Matplotlib code
│   └── Architecture/Flowchart → Generate diagram code
└── NO → It must be conceptual or illustrative
    └── Architecture/Flowchart → Generate diagram code

Matplotlib/Seaborn Code Generation

Template

import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import json

# === LOAD DATA ===
with open("results.json") as f:
    data = json.load(f)

# === STYLE SETUP ===
plt.rcParams.update({
    "figure.figsize": (5.0, 3.5),  # NeurIPS column width
    "font.family": "serif",
    "font.size": 10,
    "axes.titlesize": 11,
    "axes.labelsize": 10,
    "xtick.labelsize": 9,
    "ytick.labelsize": 9,
    "legend.fontsize": 9,
    "lines.linewidth": 1.5,
    "axes.linewidth": 0.8,
    "grid.alpha": 0.3,
    "savefig.dpi": 300,
    "savefig.bbox": "tight",
})

# === PLOT COLOURS (Color-blind friendly) ===
CB_COLORS = ["#0072B2", "#E69F00", "#009E73", "#CC79A7", "#56B4E9", "#F0E442"]

# === FIGURE ===
fig, ax = plt.subplots(1, 1)

# ... plotting code ...

# === STYLE ===
ax.set_xlabel("X Label")
ax.set_ylabel("Y Label")
ax.set_title("Title")
ax.legend()
ax.grid(True, alpha=0.3)

# === SAVE ===
plt.tight_layout()
plt.savefig("figure_1.pdf", dpi=300)
plt.savefig("figure_1.png", dpi=300)
plt.close()
print("FIGURE_SAVED: figure_1.pdf")

Conference Style Presets

NeurIPS (default)

plt.rcParams.update({
    "figure.figsize": (5.0, 3.5),   # Single column
    "font.family": "serif",
    "font.size": 10,
})

ICML

plt.rcParams.update({
    "figure.figsize": (5.5, 3.5),   # Slightly wider
    "font.family": "serif",
    "font.size": 9,
})

ICLR

plt.rcParams.update({
    "figure.figsize": (5.0, 3.5),
    "font.family": "serif",
    "font.size": 10,
})

Color Schemes

Use color-blind friendly palettes by default:

# Qualitative (for categories)
CB_COLORS = ["#0072B2", "#E69F00", "#009E73", "#CC79A7",
             "#56B4E9", "#F0E442", "#D55E00", "#000000"]

# Sequential (for heatmaps)
CB_SEQUENTIAL = ["#FFFFFF", "#F0E442", "#D55E00", "#000000"]

# Diverging (for difference plots)
CB_DIVERGING = ["#0072B2", "#FFFFFF", "#D55E00"]

Iterative Improvement Loop

For each figure, run a 3-step improvement loop:

Step 1 — Generate

Generate the initial figure code from the data and figure specification.

Step 2 — Critic

Analyze the generated figure:

• Is the data visualization accurate? No misleading axis scales?
• Is the figure readable at conference page width (~5 inches)?
• Are labels clear and font sizes appropriate?
• Is the color scheme accessible (color-blind friendly)?
• Is the caption informative (takeaway in one sentence)?
• Does the figure serve the paper's core claim?

Step 3 — Improve

Apply critic feedback. Iterate up to 3 times or until the critic judges the figure as "publishable."

Figure Specification Format

Each figure must be specified with:

figure:
  id: 1
  type: "bar"           # bar | line | scatter | heatmap | box | histogram
  caption: "Comparison of convergence rates across optimizers. SGD-Adam achieves 15% faster convergence than baselines."
  data_source: "results.json"  # or inline data
  x: "optimizer"        # column or variable name
  y: "convergence_time" # column or variable name
  hue: "dataset"        # optional grouping variable
  style: "NeurIPS"      # NeurIPS | ICML | ICLR
  width: 5.0            # inches
  height: 3.5
  color_scheme: "colorblind"

Output Structure

Figures are saved to sessions/<slug>/figures/:

figures/
├── figure_1.pdf      # Vector format for paper
├── figure_1.png      # Raster format for preview
├── figure_1_caption.md  # Caption text
├── figure_1_code.py  # Regeneratable code
├── figure_2.pdf
├── figure_2.png
├── figure_2_caption.md
└── figure_2_code.py

Anti-patterns

• Jupyter notebook output as a figure. Conference papers need vector graphics (.pdf). Never submit screenshots or Jupyter inline plots.
• Rainbow color schemes. Use color-blind friendly palettes. Never use jet/rainbow colormaps for quantitative data.
• 3D plots for 2D data. 3D plots rarely add value and often obscure the data. Use 2D alternatives unless 3D is essential.
• Figures without captions. Every figure must have a caption with a takeaway. If there is no takeaway, reconsider whether the figure belongs.
• Axis manipulation. Never truncate the y-axis to exaggerate differences. Always start at 0 for bar charts unless there is a principled reason.

Verification Checklist

• [ ] Figure type matches the data type (quantitative vs conceptual)
• [ ] All data values are real (not fabricated)
• [ ] Color-blind friendly palette is used
• [ ] Font sizes are readable at conference page width
• [ ] Caption includes a one-sentence takeaway
• [ ] Figure is saved as PDF (vector) and PNG (raster)
• [ ] Code is saved for regeneration
• [ ] Critic judged the figure as "publishable"
• [ ] Axis scales are not misleading