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 skills/beita6969/scienceclaw/data-visualization-expertnpx skills add beita6969/ScienceClaw --skill data-visualization-expertgit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/data-visualization-expert)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/data-visualization-expert"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/data-visualization-expert.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 | $0.00010 | $0.00628 |
| Opus 5 | $0.00005 | $0.00314 |
| Sonnet 5 | $0.00002 | $0.00126 |
| Haiku 4.5 | $0.00001 | $0.00063 |
Grade A, and why
data-visualization-expert 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 5d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: data-visualization-expert description: Generate insightful, publication-quality visualizations from complex datasets. keywords:
- charts
- plots
- analysis
- pandas
- matplotlib
- seaborn measurable_outcome: Create 3 high-resolution (300dpi) statistical plots (volcano, heatmap, scatter) within 15 minutes. license: MIT metadata: author: AI Agentic Skills Team version: "2.0.0" compatibility:
- system: linux, macos allowed-tools:
- run_shell_command
- write_file
- read_file
Data Visualization Expert
A dedicated skill for transforming raw data (CSV, JSON, Excel) into compelling visual narratives. Specializes in statistical and scientific plotting.
When to Use
- Reports: Summarizing key metrics or KPIs.
- Exploration: Initial data analysis (EDA) to find trends/outliers.
- Publication: Generating figures for papers or presentations.
- Comparison: Comparing models, cohorts, or experimental groups.
Core Capabilities
- Code Generation: Creates Python scripts (Matplotlib, Seaborn, Plotly) or R code (ggplot2).
- Style Enforcement: Adheres to specific journal/company branding (fonts, colors).
- Data Cleaning: Preprocesses data (handle missing values, normalize) for plotting.
- Artifact Management: Saves plots as PNG/SVG/PDF files.
Workflow
- Load Data: Read input file (
pd.read_csv()) and inspect columns/types. - Clean & Transform: Filter, pivot, or aggregate data as needed.
- Generate Plot: Write plotting script with strict aesthetic controls.
- Save & Verify: Execute script, check output file existence/size.
Example Usage
# Agent prompt:
"Visualize the distribution of 'Age' vs 'Income' from customers.csv"
# Triggers generation of `plot_age_income.py` using Seaborn scatterplot.
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.
- 5d ago First seen · 77 lines · 10 tokens per session scan A 49bdcbfc6e20
data-visualization-expert is a skill published in the GitHub repository beita6969/ScienceClaw (892 stars, last pushed 2mo ago), licensed MIT. It adds 10 tokens to every session and 628 once invoked, about $0.0001 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 2026-08-30.
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