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 pavelzw/skill-forge --skill matplotlib-data-visualizationgit clone --depth 1 https://github.com/pavelzw/skill-forgeWrote 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/pavelzw/skill-forge/matplotlib-data-visualization)<a href="https://agentmods.dev/skills/pavelzw/skill-forge/matplotlib-data-visualization"><img src="https://agentmods.dev/badge/skills/pavelzw/skill-forge/matplotlib-data-visualization/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/pavelzw/skill-forge/matplotlib-data-visualization"><img src="https://agentmods.dev/badge/skills/pavelzw/skill-forge/matplotlib-data-visualization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00055 | $0.04022 |
| Opus 5.5 | $0.00022 | $0.01609 |
| Sonnet 5.5 | $0.00011 | $0.00804 |
| Haiku 4.5 | $0.00006 | $0.00402 |
Grade A, and why
matplotlib-data-visualization 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 1mo 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 — 358 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Storytelling with Data: Chart Design Guidelines for Matplotlib
This skill focuses on visualization design principles for creating clear, compelling charts that communicate your data's message. It is not a matplotlib API reference — it is a guide for making charts that are easy to read, honest, and visually appealing.
Choose the Right Chart Type
Avoid pie charts for most use cases. Human perception is poor at comparing angles and areas. Use horizontal bar charts instead — they are the easiest chart type to read for categorical comparisons.
Avoid grouped bar charts. Side-by-side bars are hard to compare across groups. Use stacked bar charts if the message is about part-to-whole composition, or a slope chart if the message is about the change between two groups or time periods.
Use horizontal bar charts for categorical comparisons. They allow natural left-to-right reading and accommodate long category labels without rotation.
import matplotlib.pyplot as plt
categories = ["Customer Support", "Engineering", "Marketing", "Sales", "Operations"]
values = [82, 95, 67, 78, 71]
fig, ax = plt.subplots(figsize=(8, 4))
bars = ax.barh(categories, values, color="#cccccc")
# Highlight the key bar
bars[1].set_color("#e63946")
ax.set_xlim(0, 110)
ax.set_title("Engineering leads in satisfaction scores", loc="left", fontweight="bold")
ax.spines[["top", "right", "bottom"]].set_visible(False)
ax.tick_params(left=False)
ax.xaxis.set_visible(False)
# Label bars directly instead of using an x-axis
for bar, val in zip(bars, values):
ax.text(bar.get_width() + 1.5, bar.get_y() + bar.get_height() / 2,
str(val), va="center", fontsize=10)
plt.tight_layout()
Use vertical bar charts when the x-axis represents chronological progression. Time reads naturally left-to-right on a horizontal axis.
Use line charts to show continuous data over time. Lines clearly convey trends, rates of change, and patterns. Use them instead of bar charts when the focus is on the shape of change rather than individual values.
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.
- 1mo ago First seen · 358 lines · 55 tokens per session scan A f342ea68fbd3
matplotlib-data-visualization is a skill published in the GitHub repository pavelzw/skill-forge (25 stars, last pushed yesterday), licensed BSD-3-Clause. It adds 55 tokens to every session and 4,022 once invoked, about $0.0002 per session on Opus 5.5. 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.
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