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 PAPER_PLAN.md — figure plan table (from /paper-plan ) Experiment data — JSON files, CSV files, or screen logs in figures/ or project root 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: .1 f} ' , 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-6-Astra for review: mcp__codex__codex: model: gpt-6-astra 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