Scienceplot Py

scienceplot-py — Matplotlib Script Generator (scienceplots / science+nature)

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

The canonical template lives at ~/Socialst/Templates/PyPlot_Template/pq_plot.py. Every variant in this skill is a structural extension of that file.

Mandatory style invariants

Every generated script MUST keep these load-bearing patterns intact. Do not "clean up" any of them, even if they look redundant or unused.

1. import scienceplots — required. It registers the science and

nature styles by import side-effect; the symbol is never referenced directly. Linters will flag it as unused — keep it anyway.

1. Style context block — all plotting code lives inside

with plt.style.context(["science", "nature"]):. Never substitute plt.style.use(...) or call any plotting outside this block.

1. 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.

1. ax.autoscale(tight=True) — called on every axis, before

ax.set(**pparam). For subplots, loop over all axes.

1. Raw-string LaTeX — every label, title, legend entry uses r'...'.

Non-raw '$x$' works for the literal $x$ but breaks the moment a backslash appears (\alpha, \sigma, \mathrm{...}). Always r-prefix.

1. savefig — fig.savefig(, dpi=300, bbox_inches='tight').

Default filename is plot.png. PDF/SVG output is fine if asked, but keep dpi=300 and bbox_inches='tight'. (Bump higher only if the user explicitly asks for it — the lab default is 300.)

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)

1. Pick the matching template under references/:

• single_line.py — one series, one ax (mirrors pq_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)

1. Swap in the requested data-loader block. See

references/data_loaders.md for parquet / CSV / .npy / .npz snippets.

1. Substitute column names, label strings, title, output filename. Preserve

every invariant from the section above.

1. Write the .py file with the Write tool. Do not execute it.
2. Print the output path and a one-line "run with uv run " 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

Each reference is a self-contained, runnable skeleton matching the mandatory style invariants. Read the file, adapt strings/columns in memory, then Write the result to the user's chosen path.

| 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 ).

• It does not install scienceplots / matplotlib / pandas / numpy.

Assume they are already available in the target environment.

• It does not produce methodology / architecture / pipeline diagrams. For

those, use the paperbanana skill or wide-slide-illustrator.

• It does not change the style invariants. If the user asks for a different

style (e.g., seaborn, ggplot, plain matplotlib), tell them this skill is specifically for the science+nature template and offer to write the alternative as a plain script outside the skill.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Axect
• Source: Axect/skills
• License: MIT

Install and usage instructions live in the source repository linked above.