Check Metadata Typos

What it does

This skill scans metadata fields within datasets to identify and flag spelling errors, ensuring data integrity before processing. It specifically targets common typos in column names, variable labels, and descriptive attributes often found in ETL workflows.

When to use it

• Before loading raw data into a database or analytics pipeline to prevent downstream query failures.
• During the initial ingestion phase of an ETL job to clean up messy source files from external vendors.
• When collaborating on shared datasets where inconsistent naming conventions have caused confusion among team members.
• As a validation step prior to generating reports that rely on specific metadata keys for filtering or grouping.

Key capabilities

• Detects misspelled words and incorrect capitalization in metadata strings.
• Provides a list of flagged fields with suggestions for correction based on common patterns.
• Integrates seamlessly into existing data transformation pipelines without manual intervention.
• Supports multiple file formats including CSV, JSON, and Parquet headers.

Example prompts

"Run check-metadata-typos on my latest sales export to find any misspelled column names." "Scan the metadata of this research dataset for typos that might break my Python analysis script." "Identify spelling errors in the variable labels before I start aggregating the customer feedback data."

Tips & gotchas

Ensure your source files have consistent encoding (preferably UTF-8) to avoid false positives caused by character interpretation issues. This tool focuses on text-based metadata; it will not correct structural errors like missing commas or malformed JSON syntax.