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/echoleesong/claude-skills-plugin/visualization_agentgit clone --depth 1 https://github.com/echoleesong/claude-skills-pluginWhat 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 | $0.00018 | $0.03990 |
| Opus 5 | $0.00009 | $0.01995 |
| Sonnet 5 | $0.00004 | $0.00798 |
| Haiku 4.5 | $0.00002 | $0.00399 |
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 2d 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
100% identical to visualization_agent — 0 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 — 435 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.
- 2d ago First seen · 435 lines · 18 tokens per session scan A f5daa90d5aea
visualization_agent is an agent published in the GitHub repository echoleesong/claude-skills-plugin (4 stars, last pushed 2mo ago), licensed MIT. It adds 18 tokens to every session and 3,990 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to visualization_agent, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
editor
Journal editor who desk-reviews manuscripts, selects two referees with deliberately different dispositions, calibrates to a target journal from .claude/references/journal-profiles.md, and synthesizes an editorial decision (FATAL / ADDRESSABLE / TASTE). Used by /review-paper --peer [journal].
algorithm-expert
RL algorithm expert. Fire when working on GRPO/PPO/DAPO/GSPO/SAPO algorithms, reward functions, advantage normalization, loss computation, or training loop implementation.
by-epitope
Deep epitope analysis agent. Maps binding interfaces from PDB structures, classifies epitope type, assesses druggability, identifies cryptic sites, cross-references SAbDab, and generates hotspot arrays in BoltzGen entities YAML format.
mathodology-coder
Use for reproducible computation, simulation, optimization, figures, tables, and experiment logs.
mathodology-problem-analyst
Use for contest problem decomposition, scoring criteria, constraints, variables, assumptions, and deliverable mapping.
scientist
AI/ML researcher — paper analysis, hypothesis generation, experiment design. ONLY for named research paper/hypothesis/experiment. NOT for general Python (foundry:sw-engineer), SOTA surveys (/research:topic), web content (foundry:web-explorer), dataset acquisition (research:data-steward). TRIGGER: implementing from…