Skip to content
TrustedSkillsbeta
Back to all skills

Data Quality Frameworks

Checked
rmyndharis·vlatest·data·MIT

Analyzes data against rmyndharis' quality frameworks, identifying inconsistencies, errors, and areas for improvement across datasets.

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 rmyndharis/antigravity-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

rmyndharis

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.

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 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

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned rmyndharis/antigravity-skills at 3eff4af253.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
3eff4af253
Manifest
skills/data-quality-frameworks/SKILL.md
Scanned
2 files, 591 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill provides access to data quality frameworks. It allows AI agents to apply established methodologies for assessing and improving the reliability, accuracy, and completeness of datasets. The skill enables automated checks against defined rules and facilitates remediation efforts based on framework recommendations.

When to use it

  • Data validation: Before training a machine learning model, ensure your data meets quality standards using pre-defined frameworks.
  • ETL pipeline monitoring: Integrate the skill into ETL processes to continuously monitor data quality and flag anomalies.
  • Data governance compliance: Automate checks required for regulatory compliance related to data accuracy and integrity.
  • Identifying Data Issues: Quickly identify and categorize data issues within a dataset, such as missing values or inconsistencies.

Key capabilities

  • Access to established data quality frameworks
  • Automated data quality assessment
  • Rule-based validation checks
  • Remediation recommendations

Example prompts

  • "Apply the [Framework Name] framework to this CSV file and report any issues."
  • "Check this dataset for missing values using a standard data quality framework."
  • “What are the recommended remediation steps based on the [Framework Name] results?”

Tips & gotchas

The skill's effectiveness depends on having clear definitions of data quality rules within the chosen framework. Familiarity with common data quality frameworks will help you select the appropriate one for your use case.