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 seb1n/awesome-ai-agent-skills --skill data-visualizationgit clone --depth 1 https://github.com/seb1n/awesome-ai-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/seb1n/awesome-ai-agent-skills/data-visualization)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/data-visualization"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/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/seb1n/awesome-ai-agent-skills/data-visualization"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/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 | $0.00040 | $0.01840 |
| Opus 5.5 | $0.00016 | $0.00736 |
| Sonnet 5.5 | $0.00008 | $0.00368 |
| Haiku 4.5 | $0.00004 | $0.00184 |
Grade A, and why
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- data-visualization — 98% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Visualization
This skill enables an AI agent to transform structured data into meaningful visual representations. The agent selects appropriate chart types based on the data and the question being asked, builds publication-quality static charts with matplotlib and seaborn, and creates interactive visualizations with plotly. It follows established data visualization principles to ensure clarity, accuracy, and visual appeal.
Workflow
-
Understand the data and the question. Examine the dataset's structure — how many variables, what types (numeric, categorical, temporal), and what relationship or comparison the user wants to highlight. The question drives chart selection more than the data alone.
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Select the appropriate chart type. Match the analytical goal to the right visual form. Use bar charts for categorical comparisons, line charts for trends over time, scatter plots for relationships between two continuous variables, histograms for distributions, box plots for spread and outliers, and heatmaps for correlation matrices or dense categorical grids.
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Prepare the data for plotting. Aggregate, pivot, or reshape the data as needed. Sort categorical axes by value for bar charts. Resample time-series to the right granularity. Ensure no NaN values leak into the plot that would create gaps or errors.
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Build the visualization with appropriate styling. Apply consistent color palettes, readable axis labels, descriptive titles, and proper legends. Remove chart junk — unnecessary gridlines, borders, and decorations. Use figure sizes that match the intended output medium (report, slide, dashboard).
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Add context and annotations. Highlight key data points with annotations, reference lines, or shaded regions. Add summary statistics directly on the chart where helpful (e.g., median line on a box plot, trend line on a scatter). Context turns a chart from decoration into analysis.
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Export or display. Save static charts as PNG or SVG for reports, or render interactive HTML for dashboards and exploration. Set DPI to 150+ for print-quality output.
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 · 153 lines · 40 tokens per session scan A f6936b924b56
data-visualization is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (231 stars, last pushed 2mo ago), licensed MIT. It adds 40 tokens to every session and 1,840 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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