Xkcd py

xkcd-py — Matplotlib Script Generator (xkcd / hand-drawn style)

Generate a Python plotting script that always follows the user's lab xkcd template. The skill writes a .py file and returns its path; it does not run the script. The user executes it themselves (preferred: uv run <path> per their global preferences).

The canonical template lives at ~/Socialst/Templates/PyPlot_Template/xkcd_plot.py. Every variant in this skill is a structural extension of that file, normalized to the same pparam + ax-object pattern as scienceplot-py.

Mandatory style invariants

Every generated script MUST keep these load-bearing patterns intact. Do not "clean up" any of them.

1. xkcd context block — all plotting (and savefig) lives inside with plt.xkcd():. Never substitute plt.rcParams.update(...) or call any plotting outside this block; once the with exits, rcParams are restored and a later savefig will lose the xkcd fonts.
2. pparam dict — axis configuration is a dict, applied with ax.set(**pparam). Do not inline ax.set_xlabel(...), set_ylabel(...), set_title(...) calls when pparam would do.
3. ax.autoscale(tight=True) — called on every axis, before ax.set(**pparam). For subplots, loop over all axes.
4. Raw-string LaTeX — every label, title, legend entry uses r'...'. xkcd uses matplotlib's mathtext for $...$ blocks (not real LaTeX); raw strings still matter the moment a backslash appears.
5. figsize (10, 6) — xkcd-style figures need a wider canvas than the matplotlib default for the hand-drawn font and stroke widths to read cleanly. Pass figsize=(10, 6) to plt.subplots(...). For subplots (rows × cols), scale proportionally.
6. savefig — fig.savefig(<path>, dpi=300, bbox_inches='tight'). Default filename is plot.png. DPI is 300 (not 600). xkcd plots are deliberately sketchy; 600 dpi adds no value and bloats the file.

Font note

xkcd uses Humor Sans / xkcd-Script / Comic Neue. If none are installed, matplotlib falls back to Bitstream Vera Sans and emits a findfont: Font family ['xkcd Script', ...] not found warning. The plot still renders — the warning is informational. Do not try to suppress it; do not install fonts as part of the skill.

Workflow

1. Gather what the user has not already given:
   • Data source: parquet / CSV / .npy / .npz, plus the file path
   • x / y column or array names (and y-error if errorbar)
   • Plot variant: single line, multi-line + legend, scatter / errorbar, or subplots (rows × cols)
   • Axis labels, title, legend labels (LaTeX OK — strings will be wrapped in raw-string form)
   • Output path (default: plot.png next to the data file or in cwd)
2. Pick the matching template under references/:
   • single_line.py — one series, one ax (mirrors xkcd_plot.py)
   • multi_line.py — multiple series, one ax, with legend
   • scatter_errorbar.py — ax.errorbar(...) (or ax.scatter(...))
   • subplots.py — multi-panel fig, axes = plt.subplots(rows, cols)
3. Swap in the requested data-loader block. See references/data_loaders.md for parquet / CSV / .npy / .npz snippets.
4. Substitute column names, label strings, title, output filename. Preserve every invariant from the section above.
5. Write the .py file with the Write tool. Do not execute it.
6. Print the output path and a one-line "run with uv run <path>" hint. Do not invoke uv / python from this skill.

Data sources

Source	Imports	Read call
Parquet	import pandas as pd	df = pd.read_parquet('data.parquet')
CSV	import pandas as pd	df = pd.read_csv('data.csv')
NumPy .npy	import numpy as np	data = np.load('data.npy') (then column-slice)
NumPy .npz	import numpy as np	data = np.load('data.npz'); x = data['x']

Full snippets in references/data_loaders.md.

Templates

Variant	File
Single line (base)	references/single_line.py
Multi-line + legend	references/multi_line.py
Scatter / errorbar	references/scatter_errorbar.py
Subplots (multi-panel)	references/subplots.py
Data loaders	references/data_loaders.md

What this skill does NOT do

• It does not run the generated script. The user runs it (preferred: uv run <path>).
• It does not install matplotlib / pandas / numpy or any xkcd font.
• It does not produce publication-quality science+nature plots — for those, use the scienceplot-py skill.
• It does not change the style invariants. If the user asks for publication-style or different DPI, suggest scienceplot-py instead of bending xkcd-py's invariants.