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Data Quality Frameworks

Listed
sickn33·vlatest·data·MIT

Analyzes data quality using established frameworks (e.g., DAMA-DMBOK, TDQM) to identify issues and suggest remediation strategies.

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 sickn33/agentic-awesome-skills --skill data-quality-frameworks -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

sickn33

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Install target

Live repository

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No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

    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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  • No credential access: passed

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  • No pipe-to-shell installers: passed

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  • Pinned to a commit: passed

    Scanned sickn33/agentic-awesome-skills at c808e6e480.

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Commit
c808e6e480
Manifest
skills/data-quality-frameworks/SKILL.md
Scanned
0 files
Copies in repo
3 — all of them scanned
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About this skill

What it does

This skill enables AI agents to analyze data quality using established frameworks and tools like Great Expectations, dbt tests, and data contracts. It helps ensure reliable data pipelines by identifying potential issues and suggesting remediation strategies. The skill focuses on implementing data quality checks within existing pipelines and establishing clear agreements between teams regarding data standards. Detailed implementation patterns can be found in the included playbook.

When to use it

  • Implementing data quality checks as part of a data pipeline.
  • Setting up Great Expectations validation suites.
  • Building comprehensive dbt test suites for your data transformations.
  • Establishing and enforcing data contracts between different teams or systems.
  • Automating data validation processes within a CI/CD workflow.

Key capabilities

  • Utilizes established frameworks (e.g., DAMA-DMBOK, TDQM - not explicitly detailed but implied).
  • Integrates with Great Expectations for data validation.
  • Supports the creation and use of dbt tests.
  • Facilitates the definition and enforcement of data contracts.
  • Provides guidance on automating validation within CI/CD pipelines.

Example prompts

  • "Implement a Great Expectations check to validate the 'customer_id' column in my sales table."
  • "Create a dbt test suite for our product catalog transformation process."
  • "Define a data contract between the marketing and analytics teams regarding the format of lead generation data."

Tips & gotchas

  • This skill is specifically designed for tasks related to data quality checks, testing, and contracts. Ensure your task aligns with this scope before using it.
  • The output from this skill should not replace environment-specific validation or expert review.
  • Refer to the resources/implementation-playbook.md file for detailed frameworks, templates, and examples.