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Data Science

Checked
travisjneuman·vlatest·data·MIT

Analyzes data science concepts, tools, and techniques based on travisjneuman's expertise and published materials.

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 travisjneuman/.claude --skill data-science -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

travisjneuman

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 travisjneuman/.claude at 050878886e.

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

Commit
050878886e
Manifest
skills/data-science/SKILL.md
Scanned
4 files, 1,451 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 provides access to data science capabilities, allowing AI agents to perform tasks such as statistical analysis, model building, and data visualization. It enables users to leverage advanced analytical techniques without requiring extensive coding or domain expertise. The agent can process datasets, identify trends, and generate insights from complex information.

When to use it

  • Analyzing sales data to identify peak seasons and customer behavior patterns.
  • Building a predictive model to forecast future demand for a product or service.
  • Creating visualizations (charts, graphs) to communicate data-driven findings effectively.
  • Performing A/B testing analysis on website changes to optimize user experience.

Key capabilities

  • Statistical Analysis
  • Model Building
  • Data Visualization
  • Predictive Modeling

Example prompts

  • "Analyze this sales dataset and identify the top 5 performing products."
  • "Build a model to predict customer churn based on these demographic factors."
  • "Create a visualization showing the correlation between advertising spend and website traffic."

Tips & gotchas

The skill's effectiveness depends on the quality and completeness of the input data. Ensure datasets are clean, well-formatted, and representative of the population being analyzed for optimal results.