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Data Analyst

Checked
mindrally·vlatest·data·Apache-2.0

Analyzes datasets, identifies trends, and generates actionable insights to optimize business decisions quickly.

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 mindrally/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

mindrally

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 mindrally/skills at 7682ca7771.

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

Commit
7682ca7771
Manifest
data-analyst/SKILL.md
Scanned
1 file, 85 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

The Data Analyst skill empowers AI agents to process, analyze, and interpret complex datasets to uncover actionable insights. It enables automated generation of statistical summaries, trend identification, and data visualization without manual intervention.

When to use it

  • Transforming raw CSV or JSON logs into clear business intelligence reports.
  • Identifying anomalies or trends in time-series data for forecasting.
  • Performing exploratory data analysis (EDA) to understand dataset distributions before modeling.
  • Generating visual representations like charts and graphs from numerical inputs.

Key capabilities

  • Automated data cleaning and preprocessing pipelines.
  • Statistical analysis including mean, median, variance, and correlation calculations.
  • Trend detection and pattern recognition within large datasets.
  • Dynamic chart generation for visualizing key metrics.

Example prompts

  • "Analyze this sales CSV file to identify the top three performing regions and summarize the monthly growth trends."
  • "Run an exploratory data analysis on this customer dataset and highlight any significant outliers in spending behavior."
  • "Generate a correlation matrix and scatter plot visualization to understand the relationship between ad spend and conversion rates."

Tips & gotchas

Ensure your input data is clean and properly formatted (e.g., valid CSV or JSON) before processing to avoid parsing errors. For best results, specify the desired output format (such as a summary table or specific chart type) when requesting analysis.