nice-figures

nice-figures

A Claude Code plugin that teaches Claude to generate matplotlib figures in a soft-pastel, research-blog visual register — the kind of plot you see in modern ML/alignment write-ups. Bold sans-serif display titles, scatter overlaid with smoothed trends and shaded confidence bands, signature rounded bars, minimal axes, and ↓better badges. White background by default, so the output is conference- and paper-ready.

It ships as a single skill (nice-figures) plus a dependency-light style helper and 16 ready-to-adapt chart recipes. Claude invokes it automatically when you ask for plots in this style, or you can call it explicitly.

Install

This repo is a Claude Code plugin marketplace. Add it, then install the plugin:

/plugin marketplace add Mapika/nice-figures
/plugin install nice-figures@nice-figures

To update later:

/plugin marketplace update nice-figures

Usage

Once installed, just describe the figure you want. Claude triggers the skill on requests like:

• "Make a training-curve plot of these RL scores with a smoothed trend and a shaded band, soft research-blog style."
• "Grouped bar chart comparing three models across four eval scenarios, with the rounded bar tops and error bars."
• "Scaling-law scatter on log-log axes with a power-law fit for our poster — clean white background."
• "Match the figure you made last week with the coral/peach bars."

You can also invoke it directly:

/nice-figures:nice-figures

Bring your own data (CSV or arrays) and Claude maps it onto the nearest recipe; describe a figure with no data and it generates a clearly-marked synthetic placeholder.

What's inside

plugins/nice-figures/skills/nice-figures/
├── SKILL.md                     # the skill: when to use it, conventions, gotchas
├── scripts/soft_style.py        # matplotlib style helpers (numpy + matplotlib only)
└── references/chart_recipes.md  # full code for all 16 chart archetypes

The style

• Palettes — LINE_PALETTE (blue/mustard/sage/pink) for trend lines, BAR_PALETTE (coral/peach/gray/olive) for bars, MULTILINE_PALETTE for up to 5 categorical lines, plus sequential and diverging colormaps.
• Rounded bars — the signature look. rounded_bars() / rounded_hbars() give softly rounded top corners computed in display space so they stay circular at any aspect ratio.
• Smoothing + bands — smooth_curve() and rolling_band() for trend lines with tight shaded uncertainty bands.
• Typography & axes — bold display title, gray subtitles and labels (never pure black), only bottom/left spines, no grid.
• Export — save_figure() writes both PDF and PNG at 300 dpi.

The 16 archetypes

Trend-with-band, scatter + baseline, grouped bars, multi-line sweeps, heatmaps/confusion matrices, ROC/PR curves, distribution comparisons, box/violin, scaling-law fits, parity/calibration, 2D embeddings, ECDFs, forest plots, sorted horizontal bars, and Pareto fronts. Full copy-and-adapt code lives in references/chart_recipes.md.

Requirements

• matplotlib and numpy (the only hard dependencies of the style helper).
• The Inter font is preferred and picked up automatically if installed; otherwise it falls back to Helvetica → Arial → DejaVu Sans.

License

MIT