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Csv Data Wrangler

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404kidwiz·vlatest·data

Helps with CSV, data as part of writing, documentation, and content creation workflows.

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This skill no longer installs under the name csv-data-wrangler. 404kidwiz/claude-supercode-skills is deleted or private, so it cannot be installed from.

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Repository install instructions

What we know about this skill

Publisher

404kidwiz

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

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About this skill

What it does

The csv-data-wrangler skill enables AI agents to read, parse, and manipulate CSV files directly within the chat interface. It transforms raw tabular data into structured formats or generates new CSVs based on user instructions without requiring external file uploads.

When to use it

  • You need to analyze large datasets quickly without setting up a local Python environment.
  • You want to convert messy text logs into clean, comma-separated values for spreadsheet import.
  • You require an AI agent to filter, sort, or aggregate data points from a provided CSV string.
  • You are prototyping data pipelines where the AI must output results in machine-readable formats.

Key capabilities

  • Parses standard CSV input strings directly from user prompts.
  • Generates new CSV content dynamically based on logical instructions.
  • Handles basic data transformation tasks like filtering rows or renaming columns.
  • Integrates seamlessly with chat-based workflows for immediate data processing.

Example prompts

  • "Parse this CSV string and tell me the total count of entries where the status is 'active'."
  • "Convert the following list of names into a formatted CSV file with columns for Name, Email, and Role."
  • "Read this dataset and generate a new CSV containing only rows where the price exceeds $50."

Tips & gotchas

Ensure your input data follows standard CSV formatting (comma-separated values) to avoid parsing errors. For complex transformations involving multiple steps or custom logic, break your request into smaller, sequential prompts for better accuracy.