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Data Science

Checked
eyadsibai·vlatest·data

Analyzes datasets, builds predictive models, and extracts actionable insights using statistical methods and machine learning 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 eyadsibai/ltk --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

eyadsibai

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 eyadsibai/ltk at f8e85697b9.

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

Commit
f8e85697b9
Manifest
plugins/ltk-data/skills/data-science/SKILL.md
Scanned
1 file, 169 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 enables AI agents to perform data science tasks, including statistical modeling, experimentation, and advanced analytics. It facilitates designing A/B tests, building predictive models, performing causal analysis, feature engineering, and conducting statistical hypothesis testing using various tools and techniques. The skill provides code snippets for calculating sample sizes for A/B tests and analyzing test results.

When to use it

  • Designing A/B tests and experiments
  • Building predictive models
  • Performing causal analysis
  • Feature engineering
  • Statistical hypothesis testing

Key capabilities

  • A/B Test Framework: Includes functions for calculating required sample sizes and analyzing test results.
  • Statistical Significance Analysis: Provides methods to determine statistical significance using z-tests.
  • Feature Engineering: Offers a pipeline for creating new features from data, including temporal features like hour of day and day of week.
  • Tool Support: Utilizes Python, SQL, R, NumPy, Pandas, SciPy, Scikit-learn, XGBoost, LightGBM, Matplotlib, Seaborn, Plotly, Statsmodels, PyMC, Jupyter Notebooks, and VS Code.

Example prompts

  • "Calculate the required sample size for an A/B test with a baseline conversion rate of 5% and a minimum detectable effect of 0.5%."
  • "Analyze the results of an A/B test where the control group had a conversion rate of 10% and the treatment group had a conversion rate of 12%."
  • "Create new features in this dataset, including hour of day and day of week from the timestamp column."

Tips & gotchas

  • This skill provides code snippets; you may need to adapt them to your specific data and context.
  • The provided functions assume a basic understanding of statistical concepts like p-values and effect sizes.
  • Familiarity with Python, Pandas, and related libraries will be helpful for effectively using this skill.