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DataPython

Data Quality

by nimadorostkar

Data Quality is a Data skill for Claude Code, published by nimadorostkar in Claude-Skills-collection.

25 stars3 forkson nimadorostkar/Claude-Skills-collectionAdded 2026/08/12Repository updated 2026/07/26
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Install in seconds
Install Data Quality
Copy Data Quality into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/nimadorostkar/Claude-Skills-collection/tree/main/skills/data/data-quality ~/.claude/skills/data-quality

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/nimadorostkar/Claude-Skills-collection.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
skills/data/data-quality/SKILL.md in nimadorostkar/Claude-Skills-collection
Installs to
~/.claude/skills/data-quality
Collection
One of 50 skills cataloged from this repository
Category
Data — 668 skills

What Data Quality does

Data Quality catches bad data before it reaches a dashboard, model, or customer. It profiles datasets, validates schema and business rules, checks freshness and volume, and fails the pipeline with quarantine and alerts when checks break.

Data Quality is cataloged under Data on DirSkills. Data Quality comes from a repository tagged ai, claude, claude-skills and skills.

Documentation

README

Data Quality

Purpose

Catch bad data before it reaches a dashboard, a model, or a customer. A pipeline that silently propagates corrupt data is worse than one that fails, because the failure is discovered downstream, later, by someone who trusts the number.

When to Use

  • Ingesting data from a source you do not control.
  • Building quality gates into a pipeline.
  • Investigating a metric that looks wrong.
  • Auditing a dataset before it is used for analysis or training.

Capabilities

  • Profiling: distributions, cardinality, null rates, outliers.
  • Schema validation and type enforcement.
  • Constraint checks: uniqueness, referential integrity, ranges, formats.
  • Freshness, completeness, and volume anomaly detection.
  • Quarantine and alerting patterns.

Inputs

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Data Quality

  • What else does nimadorostkar publish alongside Data Quality?

    Data Quality is one of 50 skills that DirSkills catalogs from nimadorostkar/Claude-Skills-collection, the repository it ships in. Its siblings there include API Design, Agent Design and Agent Instructions. Each one is a separate skill with its own page in this directory, installs the same way Data Quality does, and is maintained by nimadorostkar in that same repository. The rest of the collection is listed on the nimadorostkar/Claude-Skills-collection page.

  • How does Data Quality compare to other Data skills?

    Data Quality ranks #645 by stars among the 668 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Data Quality against them. Open each page to compare what they document and how they install.

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