Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PKU-YuanGroup/OpenAI4S --skill bio-data-visualization-matplotlib-fundamentalsgit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-data-visualization-matplotlib-fundamentals)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-data-visualization-matplotlib-fundamentals"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-data-visualization-matplotlib-fundamentals/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-data-visualization-matplotlib-fundamentals"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-data-visualization-matplotlib-fundamentals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00101 | $0.03243 |
| Opus 5 | $0.00051 | $0.01622 |
| Sonnet 5 | $0.00020 | $0.00649 |
| Haiku 4.5 | $0.00010 | $0.00324 |
Grade A, and why
bio-data-visualization-matplotlib-fundamentals scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
92% identical to bio-data-visualization-matplotlib-fundamentals — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: matplotlib 3.8+, seaborn 0.13+, numpy 1.26+, pandas 2.2+.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
matplotlib Fundamentals
"Make a publication figure in Python" -> Build via the object-oriented Figure/Axes API (not pyplot state-machine), with constrained_layout for axes alignment, pdf.fonttype=42 for journal-compliant TrueType fonts, CVD-safe palettes, and rasterized point layers for large scatter. The pyplot interface is for notebook scratch; the Figure/Axes API is for reproducible figures.
- Python:
fig, ax = plt.subplots()->ax.scatter/ax.plot/ax.bar;seaborn.objects(new grammar API) for ggplot-like
The Three Modern Defaults
-
Object-oriented API —
fig, ax = plt.subplots(figsize=(4, 3))thenax.scatter(x, y),ax.set_xlabel(...). The pyplot state-machine (plt.scatter,plt.xlabel) hides which axes are being modified and breaks in multi-subplot figures. -
constrained_layout —
plt.subplots(constrained_layout=True)automatically prevents axis-label clipping and tight-packs subplots. Replaces the oldertight_layout()and is the default in matplotlib 3.6+. -
Type-42 (TrueType) font embedding —
plt.rcParams['pdf.fonttype']=42produces searchable/editable PDF text. Default Type-3 PostScript glyphs are not searchable and rejected by Nature, IEEE, ACM, and many other publishers.
Standard Setup for Publication
import matplotlib.pyplot as plt
import matplotlib as mpl
# rcParams for publication compliance
mpl.rcParams.update({
'pdf.fonttype': 42, # TrueType -- searchable PDFs
'ps.fonttype': 42, # TrueType in EPS
'font.family': 'sans-serif',
'font.sans-serif': ['Arial', 'Helvetica', 'DejaVu Sans'],
'font.size': 7, # Nature requires 5-7 pt body text
'axes.labelsize': 7,
'axes.titlesize': 8,
'xtick.labelsize': 6,
'ytick.labelsize': 6,
'legend.fontsize': 6,
'figure.dpi': 100, # display
'savefig.dpi': 300, # save
'savefig.bbox': 'tight',
'savefig.pad_inches': 0.05,
'axes.linewidth': 0.5,
'xtick.major.width': 0.5,
'ytick.major.width': 0.5,
'lines.linewidth': 1.0,
'patch.linewidth': 0.5,
})
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 307 lines · 101 tokens per session scan A e98a26b855d2
bio-data-visualization-matplotlib-fundamentals is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 101 tokens to every session and 3,243 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to bio-data-visualization-matplotlib-fundamentals, differing in 12 lines, and is treated as a copy.
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