Data Quality Frameworks

Checked

wshobson·vlatest·data·MIT

Evaluates data against established quality frameworks (e.g., DQF, DAMA-DMBOK) to identify deficiencies and suggest remediation strategies.

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Showing Claude Code. One command installs this skill into any of the agents below.

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terminal

$ npx skills add wshobson/agents --skill data-quality-frameworks -a claude-code

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What we know about this skill

Publisher

wshobson

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Live repository

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Automated safety checks

scanned 12 Oct 2026

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.
No undeclared network calls: passed
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.
No obfuscated payloads: passed
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Pinned to a commit: passed
Scanned wshobson/agents at 46891e7e60.
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Commit: 46891e7e60
Manifest: plugins/data-engineering/skills/data-quality-frameworks/SKILL.md
Scanned: 2 files, 568 lines

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About this skill

What it does

The data-quality-frameworks skill enables users to implement and manage frameworks that assess, monitor, and improve the quality of datasets. It supports tasks like identifying inconsistencies, validating data against predefined rules, and generating reports on data integrity.

When to use it

When preparing datasets for machine learning models to ensure accuracy and reliability.
During ETL (Extract, Transform, Load) processes to validate data before loading into a database.
When auditing existing datasets to identify missing values or formatting issues.
To automate quality checks in large-scale data pipelines.

Key capabilities

Integration with popular data validation libraries
Customizable rules for data integrity checks
Automated reporting on data quality metrics
Support for structured and semi-structured data formats

Example prompts

"Validate this CSV file against the schema provided."
"Generate a report on missing values in the customer dataset."
"Set up automated checks for new data entries in the sales database."

Tips & gotchas

Ensure that your datasets are properly formatted before applying validation rules.
Custom rules may require domain-specific knowledge to define effectively.