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Data Analyst

Checked
borghei·vlatest·data

Analyzes complex datasets, identifies trends, and generates actionable insights with advanced statistical modeling and visualization.

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-analyst -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-analyst/SKILL.md
Scanned
4 files, 904 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 an AI agent to function as a senior data analyst, performing tasks such as writing production SQL queries, designing visualizations, running statistical tests (t-tests, chi-square), and translating findings into actionable business recommendations. It focuses on framing business questions as testable hypotheses, exploring datasets for anomalies, and delivering insights in a structured "What / So What / Now What" format. The agent will use CTEs to improve SQL readability and follows best practices like filtering early and aggregating late.

When to use it

  • Analyzing campaign performance to determine if specific campaigns impacted key metrics (e.g., 7-day retention).
  • Understanding customer behavior through cohort analysis or funnel analysis.
  • Comparing groups of data using statistical hypothesis testing.
  • Identifying relationships between variables using regression analysis.
  • Creating visualizations to communicate data insights effectively.

Key capabilities

  • SQL Query Generation: Writes production-ready SQL queries, including the use of Common Table Expressions (CTEs).
  • Data Exploration & Profiling: Calculates descriptive statistics and checks for nulls, duplicates, and outliers.
  • Statistical Analysis: Performs cohort analysis, funnel analysis, t-tests, chi-square tests, and regression.
  • Visualization Design: Selects appropriate chart types (line, bar, donut, histogram, scatter) and adheres to design rules (e.g., Y-axis at zero for bars).
  • Insight Delivery: Structures findings using the "What / So What / Now What" framework.

Example prompts

  • “Did campaign X increase 7-day retention by >= 5%? Please provide a visualization.”
  • “Perform a cohort analysis to understand customer retention trends over time.”
  • “Analyze order data and identify any correlations between order date and amount."

Tips & gotchas

  • The agent requires access to relevant datasets and the ability to execute SQL queries.
  • Be specific with your business questions to ensure the agent can formulate a testable hypothesis.
  • Familiarity with basic statistical concepts will help you interpret the agent’s findings.