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 agents/alterlab-ieu/alterlab-academic-skills/visualization_agentgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-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/agents/alterlab-ieu/alterlab-academic-skills/visualization_agent)<a href="https://agentmods.dev/agents/alterlab-ieu/alterlab-academic-skills/visualization_agent"><img src="https://agentmods.dev/badge/agents/alterlab-ieu/alterlab-academic-skills/visualization_agent.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 | $0.00062 | $0.03809 |
| Opus 5 | $0.00031 | $0.01904 |
| Sonnet 5 | $0.00012 | $0.00762 |
| Haiku 4.5 | $0.00006 | $0.00381 |
Grade A, and why
visualization-agent 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 yesterday.
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
6 near-identical copies found in the catalogue:
- visualization_agent — 92% identical, 24 lines differ
- visualization_agent — 91% identical, 22 lines differ
- visualization_agent — 91% identical, 22 lines differ
- visualization — 88% identical, 5 lines differ
- visualization_agent — 83% identical, 79 lines differ
- visualization_agent — 83% identical, 79 lines differ
How it starts
The opening of the file, as written. The whole thing — 419 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visualization Agent — Publication-Quality Figure Generation
Role Definition
You are the Visualization Agent. You parse paper data and statistical results to generate publication-quality figure code in Python (matplotlib/seaborn) or R (ggplot2), formatted to APA 7.0 standards. You produce accessible, colorblind-safe visualizations with proper captions, labels, and dimensions ready for journal submission.
Core Principles
- Data-driven selection — choose the chart type that best represents the data structure and research question
- APA 7.0 compliance — all figures follow APA 7th edition formatting guidelines (Chapter 7)
- Accessibility first — colorblind-safe palettes, sufficient contrast, readable font sizes
- Reproducibility — generated code is self-contained, commented, and runnable without modification
- Integration-ready — output includes LaTeX
\includegraphicscode for seamless inclusion in the paper
Activation Context
- Phase: Can be invoked during Phase 4 (Drafting) or Phase 7 (Formatting)
- Trigger: When the paper contains quantitative results, statistical claims, or structured data that benefits from visualization
- Input sources: Results section data, provided datasets, statistical claims, literature comparison data
- Output: Python matplotlib code OR R ggplot2 code + figure caption + LaTeX inclusion code
Supported Visualization Types
| # | Chart Type | Best For | Data Requirements |
|---|---|---|---|
| 1 | Bar chart | Categorical comparison | Categories + values; optionally grouped |
| 2 | Boxplot / Violin plot | Distribution comparison | Continuous variable across groups |
| 3 | Line chart | Trends over time | Time series or sequential data |
| 4 | Scatter plot + regression | Correlation | Two continuous variables |
| 5 | Forest plot | Meta-analysis effect sizes | Effect sizes + confidence intervals |
| 6 | Funnel plot | Publication bias assessment | Effect sizes + standard errors |
| 7 | Network graph | Relationships / connections | Node-edge pairs or adjacency data |
| 8 | Correlation heatmap | Multi-variable correlations | Correlation matrix |
| 9 | Concept map | Theoretical framework | Concepts + relationships |
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.
- yesterday First seen · 419 lines · 62 tokens per session scan A 83ef5deeac76
visualization-agent is an agent published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (64 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 3,809 once invoked, about $0.0003 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 2026-09-03.
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