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Data Quality Checker

Checked
armanzeroeight·vlatest·data·MIT

Identifies data inconsistencies, errors, and missing values within datasets using advanced statistical analysis and pattern recognition.

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 armanzeroeight/fastagent-plugins --skill data-quality-checker -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

armanzeroeight

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 armanzeroeight/fastagent-plugins at 61b61a99e6.

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

Commit
61b61a99e6
Manifest
plugins/data-engineer/skills/data-quality-checker/SKILL.md
Scanned
1 file, 88 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 checks the quality of data provided to it. It can identify missing values, incorrect formats (e.g., dates in wrong format), and inconsistencies within a dataset. The tool aims to improve the reliability of downstream processes that rely on accurate data.

When to use it

  • Data Validation: Before feeding data into machine learning models or other analytical tools.
  • ETL Processes: As part of an Extract, Transform, Load (ETL) pipeline to ensure data integrity.
  • Report Generation: To verify the accuracy and completeness of data used in reports.
  • Data Migration: When moving data between systems to prevent errors during transfer.

Key capabilities

  • Missing value detection
  • Format validation (e.g., date formats)
  • Consistency checks within datasets

Example prompts

  • "Check the quality of this CSV file and report any issues."
  • "Validate that all dates in this dataset are in YYYY-MM-DD format."
  • "Identify missing values in the 'customer_email' column of this data."

Tips & gotchas

The skill requires a structured data input, such as a CSV file or JSON object. It is most effective when provided with clear expectations about the expected data formats and constraints.