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Data Normalization Tool

Listed
jeremylongshore·vlatest·database·MIT

Standardizes data formats (dates, addresses, currencies) across sources for improved analysis and consistency.

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-normalization-tool -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.

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scanned

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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.
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About this skill

What it does

This skill, the data-normalization-tool, standardizes and cleans data for improved analysis and usability. It takes raw, unstructured data as input and transforms it into a consistent format, handling issues like inconsistent units, missing values, and incorrect data types. The tool aims to make datasets more reliable and easier to work with across different systems or applications.

When to use it

  • Combining data from multiple sources: When you need to merge datasets collected in different ways (e.g., spreadsheets, databases, APIs).
  • Preparing data for machine learning: Machine learning models require clean, consistent data; this tool can help with that preprocessing step.
  • Creating reports or dashboards: Ensure accurate and comparable data is displayed by normalizing values before visualization.
  • Data migration projects: Standardize data formats during the transfer of information between systems.

Key capabilities

  • Data standardization
  • Missing value handling
  • Data type conversion
  • Unit consistency enforcement

Example prompts

  • "Normalize this CSV file, ensuring all dates are in YYYY-MM-DD format."
  • "Clean and normalize the following JSON data, replacing missing values with 0."
  • "Convert these temperature readings from Celsius to Fahrenheit and standardize the units."

Tips & gotchas

The tool requires a clear understanding of the desired output format and potential data inconsistencies. Providing specific instructions about how to handle edge cases or unusual data entries will improve results.