Skip to content
TrustedSkillsbeta
Back to all skills

Data Analyst

Checked
ailabs-393·vlatest·data·MIT

Analyzes datasets, identifies trends, and generates actionable insights for improved decision-making using advanced statistical methods.

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 ailabs-393/ai-labs-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

ailabs-393

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

    Only contacts known or declared hosts: 127.0.0.1.

    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.

    Hosts contacted

    127.0.0.1local

  • 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 ailabs-393/ai-labs-claude-skills at 1a12bc7aad.

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

Commit
1a12bc7aad
Manifest
packages/skills/data-analyst/SKILL.md
Scanned
8 files, 1,581 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 Data Analyst skill enables AI agents to perform comprehensive data analysis on CSV datasets. It automatically identifies and analyzes missing values, applies intelligent imputation techniques based on data characteristics, and generates interactive Plotly Dash dashboards for visualization. The skill supports a complete exploratory data analysis workflow, from initial assessment to actionable insights.

When to use it

  • When you need to understand the quality of a CSV dataset, particularly regarding missing values.
  • To automatically fill in missing data using appropriate statistical methods (mean, median, mode, KNN, forward fill, constant).
  • For creating interactive visualizations and dashboards to explore trends and patterns within your data.

Key capabilities

  • Missing Value Analysis: Detects missing values, identifies data types, calculates statistics, and suggests imputation strategies.
  • Intelligent Imputation: Applies various imputation methods (mean, median, mode, KNN, forward fill, constant) based on column characteristics.
  • Interactive Dashboard Creation: Generates Plotly Dash dashboards with multiple visualization types for data exploration.

Example prompts

  • "Analyze the missing values in this CSV file and suggest how to handle them."
  • "Impute the missing values in this dataset using the recommended methods from the analysis report."
  • "Create a dashboard visualizing the trends in this cleaned dataset."

Tips & gotchas

  • The skill requires Python 3.
  • Columns with over 70% missing data will be automatically dropped. Critical ID columns cannot have missing values and will cause rows to be dropped.
  • Review the output analysis report before imputing, as it provides valuable insights into the recommended imputation strategies.