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Scientific Visualization

Checked
braselog·vlatest·data

Generates compelling visualizations of datasets to reveal patterns and insights, making complex information easily understandable.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add braselog/researchassistant --skill scientific-visualization -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

braselog

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned braselog/researchassistant at 6e1e60a1d7.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
6e1e60a1d7
Manifest
.github/skills/scientific-visualization/SKILL.md
Scanned
1 file, 27 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill generates scientific visualizations from data. It can create plots and charts to represent complex datasets, aiding in understanding trends and patterns. The visualizations are designed for clarity and accuracy, suitable for research reports or presentations.

When to use it

  • Visualize experimental results: Generate graphs of sensor readings over time.
  • Create publication-ready figures: Produce high-quality plots for inclusion in scientific papers.
  • Explore datasets for insights: Quickly identify correlations and anomalies within a dataset.
  • Communicate findings effectively: Present complex data in an easily understandable visual format to colleagues or stakeholders.

Key capabilities

  • Data plotting
  • Chart generation
  • Scientific visualization
  • Publication-quality output

Example prompts

  • "Create a scatter plot of temperature vs. pressure from this dataset."
  • "Generate a line graph showing the trend of CO2 emissions over the past decade, using this data source."
  • "Visualize these experimental results as a bar chart, clearly labeling axes and including error bars."

Tips & gotchas

The skill requires a properly formatted dataset as input. Ensure your data is clean and structured for optimal visualization quality.