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Data Quality Checker

Listed
jeremylongshore·vlatest·data·MIT

Identifies inconsistencies, errors, and missing values in datasets to improve reliability and analysis accuracy.

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 jeremylongshore/tons-of-skills-marketplace --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

jeremylongshore

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.

Nobody has reviewed this code. TrustedSkills indexes and links skills; it does not audit, run or scan them. A skill runs with whatever access you give your agent — read the source before you install it.

Automated safety checks

scanned

We could not read this skill's source, so none of the checks below were run.

Not scanned. none of the 396 skills in the repository match this slug — it was most likely renamed or removed upstream after the registry indexed it Source recorded as jeremylongshore/claude-code-plugins-plus-skills.
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.
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About this skill

What it does

This skill assesses the quality of data provided to an AI agent. It can identify inconsistencies, missing values, and potential errors within datasets. The tool helps ensure that downstream processes relying on this data are accurate and reliable by flagging issues before they impact results.

When to use it

  • Before training a machine learning model: Validate the dataset for biases or inaccuracies.
  • When integrating new data sources: Quickly check incoming data against existing standards.
  • During data cleaning workflows: Automate initial quality checks as part of a larger process.
  • To debug unexpected results from AI models: Identify if poor data quality is contributing to errors.

Key capabilities

  • Data inconsistency detection
  • Missing value identification
  • Error flagging

Example prompts

  • "Check the data quality of this CSV file and report any inconsistencies."
  • "Analyze this dataset for missing values and suggest potential imputation strategies."
  • "Can you assess the accuracy of these customer records?"

Tips & gotchas

The skill's effectiveness depends on providing clear, structured data. Large or complex datasets may require more processing time.