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Data Scientist

Checked
borghei·vlatest·data

Analyzes complex datasets, builds predictive models, and generates actionable insights using advanced statistical techniques.

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 borghei/claude-skills --skill data-scientist -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

borghei

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 borghei/claude-skills at 4a698e8d07.

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

Commit
4a698e8d07
Manifest
data-analytics/data-scientist/SKILL.md
Scanned
4 files, 1,048 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-scientist

What it does

This skill equips AI agents with specialized capabilities to perform data science tasks, enabling them to analyze datasets and generate insights. It allows agents to handle complex statistical operations and data manipulation workflows efficiently.

When to use it

  • Analyzing large datasets to identify trends, patterns, or anomalies without manual preprocessing.
  • Generating statistical summaries and visualizations to support data-driven decision-making.
  • Automating repetitive data cleaning tasks to prepare raw information for modeling.
  • Conducting exploratory data analysis (EDA) to formulate hypotheses before building predictive models.

Key capabilities

  • Data ingestion and preprocessing from various formats
  • Statistical analysis and descriptive metrics generation
  • Pattern recognition within structured and unstructured data
  • Insight synthesis based on quantitative evidence

Example prompts

  • "Analyze this CSV file containing sales data and summarize the top-performing regions over the last quarter."
  • "Run a correlation analysis between customer age and purchase frequency, then explain the findings."
  • "Identify any outliers in this dataset of server response times and suggest potential causes."

Tips & gotchas

Ensure your data is properly formatted and accessible before initiating analysis tasks to avoid parsing errors. For best results, provide clear context about the data source and specific goals when requesting insights.