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.
git clone --depth 1 https://github.com/Lzy599775/agent-auto-sci-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/lzy599775/agent-auto-sci-skills/visualization_agent)<a href="https://agentmods.dev/agents/lzy599775/agent-auto-sci-skills/visualization_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/visualization_agent/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/agents/lzy599775/agent-auto-sci-skills/visualization_agent"><img src="https://agentmods.dev/badge/agents/lzy599775/agent-auto-sci-skills/visualization_agent.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00018 | $0.04356 |
| Opus 5.5 | $0.00007 | $0.01742 |
| Sonnet 5.5 | $0.00004 | $0.00871 |
| Haiku 4.5 | $0.00002 | $0.00436 |
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 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.
This is a copy
83% identical to visualization-agent — 79 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 — 442 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.
- 1mo ago First seen · 442 lines · 18 tokens per session scan A c22ba5e209bf
visualization_agent is an agent published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed 3d ago), licensed MIT. It adds 18 tokens to every session and 4,356 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 83% identical to visualization-agent, differing in 79 lines, and is treated as a copy.
Other agents, from other repositories
protocol_architect_agent
Turns a proposal or protocol into confirmed, stage-split screening rules and a screening config before any record is screened.
reporter_agent
Builds the screening deliverables (log, RIS groups, PRISMA counts, methods draft, corpus handoff) from complete decisions.
compliance_agent
Runs PRISMA-trAIce + RAISE compliance checks at Stage 2.5 / 4.5 integrity gates and emits Schema 12 compliancereport.
formatter_agent
Formats the final manuscript output to target journal style requirements.
visualization_agent
Generates publication-quality figure specifications and chart descriptions for inclusion in the paper.
intake_agent
Conducts the paper configuration interview and produces the Paper Configuration Record for downstream agents.