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Low-level Python plotting for scientific figures: publication-quality line, scatter, bar, heatmap, contour, 3D; multi-panel layouts; fine control of every element. PNG/PDF/SVG export. Use seaborn for quick stats, plotly for interactive.
Passed 22 of 22 test cases with the skill, and 18 of 22 without it.
and 3 more in the full report
4 fixed18 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
Write Python code to create a 2-panel horizontal subplot figure and set bold panel identifiers ('A' and 'B') positioned on the top-left corner of each subplot title area.
| Checked | Without skill | With skill |
|---|---|---|
| Subplot titles set panel labels using loc='left'. | Missed | Passed |
| Subplot panel titles set fontweight='bold'. | Missed | Passed |
The instructions your model reads when the skill loads.
Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots. It provides both a MATLAB-style pyplot interface and an object-oriented API for full control over figures, axes, and artists. Essential for generating publication-quality scientific figures.
seaborn insteadplotly insteadmatplotlib, numpypandas (for DataFrame plotting), seaborn (for style presets)%matplotlib inline), and GUI appspip install matplotlib numpy
import matplotlib.pyplot as plt
import numpy as np
# Publication-ready figure template: set size, plot, label, save as PDF
fig, ax = plt.subplots(figsize=(6, 4)) # single-column journal width ≈ 6 cm → set here in inches
x = np.linspace(0, 2 * np.pi, 200)
ax.plot(x, np.sin(x), color="steelblue", lw=1.5, label="sin(x)")
ax.plot(x, np.cos(x), color="coral", lw=1.5, label="cos(x)", linestyle="--")
test cases passed, vs 18 / 22 without
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