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 agentmods add skills/tondevrel/scientific-agent-skills/matplotlib-pronpx skills add tondevrel/scientific-agent-skills --skill matplotlib-progit clone --depth 1 https://github.com/tondevrel/scientific-agent-skillsWrote 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/tondevrel/scientific-agent-skills/matplotlib-pro)<a href="https://agentmods.dev/skills/tondevrel/scientific-agent-skills/matplotlib-pro"><img src="https://agentmods.dev/badge/skills/tondevrel/scientific-agent-skills/matplotlib-pro.svg" alt="Measured on agentmods" 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 | $0.00041 | $0.01068 |
| Opus 5 | $0.00020 | $0.00534 |
| Sonnet 5 | $0.00008 | $0.00214 |
| Haiku 4.5 | $0.00004 | $0.00107 |
Grade A, and why
matplotlib-pro 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Matplotlib - Professional Viz & Animation
Beyond static plots, Matplotlib is a powerful engine for dynamic data visualization and scientific storytelling. This guide focuses on the "Pro" features: blitting for speed, Artist hierarchy for control, and LaTeX integration for papers.
When to Use
- Creating high-FPS animations for simulations (Fluid dynamics, N-body).
- Building custom interactive tools inside Jupyter or a GUI.
- Generating pixel-perfect figures for academic journals.
- Visualizing real-time data streams from sensors.
Core Principles
1. The Artist Hierarchy
Everything you see is an Artist. Figures contain Axes, Axes contain Lines, Text, Patches. Pro-level control means manipulating these objects directly instead of using high-level plt commands.
2. Blitting (The Secret to Speed)
Standard animation redraws the whole figure every frame (slow). Blitting only redraws the parts that changed (e.g., the moving line), while keeping the axes and labels cached as a background image.
3. Backend Mastery
- Agg: High-quality static PNGs.
- PDF/PGF: Vector-based for LaTeX.
- TkAgg/QtAgg: Interactive windows.
High-Performance Animation
Using FuncAnimation with Blitting
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
fig, ax = plt.subplots()
line, = ax.plot([], [], lw=2) # Returns the Line2D artist
def init():
ax.set_xlim(0, 2*np.pi)
ax.set_ylim(-1, 1)
return line, # Note the comma
def update(frame):
x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x + frame/10.0)
line.set_data(x, y)
return line,
# blit=True is critical for performance
ani = FuncAnimation(fig, update, frames=100, init_func=init, blit=True)
plt.show()
Publication Standards
1. LaTeX & PGF Backend (For Papers)
import matplotlib as mpl
mpl.use("pgf") # Use PGF for perfect LaTeX integration
mpl.rcParams.update({
"pgf.texsystem": "pdflatex",
"font.family": "serif",
"text.usetex": True,
"pgf.rcfonts": False,
})
fig.savefig("figure.pgf") # Import this directly into your LaTeX doc
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.
- 4d ago First seen · 130 lines · 41 tokens per session scan A 395c2ed37282
matplotlib-pro is a skill published in the GitHub repository tondevrel/scientific-agent-skills (19 stars, last pushed 7mo ago), licensed MIT. It adds 41 tokens to every session and 1,068 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…
auditing-subgroup-fairness
Audit an OpenMed NER or de-identification model for performance disparities across demographic subgroups (sex, age band, race/ethnicity when available) using openmed.eval.fairnessreport. Use when the user wants per-subgroup recall and leakage, wants to check whether de-identification under-protects a group, wants to…
mixed-precision
Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
tooluniverse-drug-research
Comprehensive drug profiling — mechanism, primary/secondary targets, drug interactions, clinical-trial status, adverse events (FAERS), pharmacogenomics, and approval history. Use for full drug investigation reports, 'tell me about drug X' queries, and assembling drug profiles for clinicians, researchers, or regulatory…