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Senior Data Scientist

Checked
borghei·vlatest·data

Analyzes complex datasets, builds predictive models, and provides data-driven insights for strategic decision-making.

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 senior-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
engineering/senior-data-scientist/SKILL.md
Scanned
9 files, 893 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 allows an AI agent to perform advanced data analysis, build predictive models, and interpret complex datasets. It can generate actionable insights from raw data, identify trends, and communicate findings effectively using visualizations and clear explanations. The agent leverages statistical methods and machine learning techniques to solve business problems and inform strategic decisions.

When to use it

  • Analyzing customer churn data to identify key drivers and recommend retention strategies.
  • Building a predictive model for sales forecasting based on historical trends and market factors.
  • Performing A/B testing analysis to optimize website conversion rates.
  • Identifying fraudulent transactions by analyzing patterns in financial data.

Key capabilities

  • Statistical modeling
  • Machine learning
  • Data visualization
  • Predictive analytics
  • Insight generation

Example prompts

  • "Analyze this sales dataset and identify the top 3 factors contributing to declining revenue."
  • "Build a model to predict customer churn based on these demographic and behavioral data points."
  • "Create a dashboard visualizing key performance indicators for our marketing campaigns."

Tips & gotchas

The agent's effectiveness depends heavily on the quality and completeness of the input data. Ensure the dataset is clean, properly formatted, and contains relevant features for accurate analysis and modeling.