Check Metadata Typos
CheckedThis skill automatically identifies and corrects common typos in metadata fields, ensuring data accuracy and consistency for improved searchability.
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check-metadata-typos. SKILL.md is marked internal, so the skills CLI hides it.We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-10-21. We don't show a command we know will fail.
What we know about this skill
Publisher
owid
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.
Automated safety checks
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.
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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.
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No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No credential access: passed
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Pinned to a commit: passed
Scanned owid/etl at 6173f8e9ff.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 6173f8e9ff
- Scanned
- 1 file, 245 lines
About this skill
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.