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Data Scientist

Listed
sickn33·vlatest·data·MIT

Analyzes datasets, builds predictive models, and generates 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 sickn33/agentic-awesome-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

sickn33

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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

    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 sickn33/antigravity-awesome-skills at 5a4cdd9df8.

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

Commit
5a4cdd9df8
Manifest
skills/data-scientist/SKILL.md
Scanned
0 files
Copies in repo
3 — all of them scanned
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

The data-scientist skill empowers AI agents to perform advanced statistical analysis, build predictive models, and extract actionable insights from complex datasets. It bridges the gap between raw data and decision-making by automating the entire analytical workflow within an agent's operational context.

When to use it

  • Analyzing large volumes of user behavior logs to identify conversion bottlenecks.
  • Generating time-series forecasts for inventory management or sales trends.
  • Segmenting customer bases based on purchasing patterns for targeted marketing campaigns.
  • Validating hypotheses by running A/B test results through statistical significance checks.

Key capabilities

  • Statistical analysis and hypothesis testing
  • Predictive modeling and regression analysis
  • Data cleaning and preprocessing automation
  • Visualization generation for trend identification
  • Clustering algorithms for pattern discovery

Example prompts

  • "Analyze the attached CSV of Q3 sales data and identify which product category had the highest growth rate."
  • "Build a linear regression model to predict next month's energy consumption based on historical weather and usage data."
  • "Cluster our customer database into three distinct segments based on purchase frequency and average order value."

Tips & gotchas

Ensure your dataset is clean and properly formatted before feeding it to the agent, as preprocessing steps are critical for accurate modeling. While the skill handles complex algorithms, you may need to provide specific domain context or constraints to guide the model's interpretation of results effectively.