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Data Science Expert

Checked
personamanagmentlayer·vlatest·data·Apache-2.0

Analyzes complex datasets, builds predictive models, and provides actionable data science insights to solve business challenges.

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 personamanagmentlayer/pcl --skill data-science-expert -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

personamanagmentlayer

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 personamanagmentlayer/pcl at 79ccaa9820.

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

Commit
79ccaa9820
Manifest
stdlib/ai/data-science-expert/SKILL.md
Scanned
1 file, 400 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

data-science-expert

What it does

This skill provides an AI agent with specialized expertise in data science methodologies, enabling it to analyze datasets and derive actionable insights. It allows the agent to apply statistical techniques and machine learning concepts to solve complex analytical problems effectively.

When to use it

  • Analyzing large datasets to identify trends, patterns, or anomalies for business intelligence.
  • Generating predictive models based on historical data to forecast future outcomes.
  • Explaining statistical significance and correlation within raw numerical information.
  • Assisting in the design of experimental frameworks for A/B testing or research studies.

Key capabilities

  • Statistical analysis and interpretation of quantitative data.
  • Application of machine learning algorithms for pattern recognition.
  • Deriving strategic insights from complex numerical inputs.
  • Methodological support for data-driven decision-making processes.

Example prompts

  • "Analyze this sales dataset to identify the top three factors driving revenue growth in Q4."
  • "Explain the correlation between customer age and product preference using the provided survey results."
  • "Design a basic predictive model framework to forecast next month's inventory needs based on historical trends."

Tips & gotchas

Ensure you provide clean, structured data when requesting analysis to maximize accuracy. This skill focuses on interpretation and methodology; it may not execute code directly without specific tool integrations.