open-data-quality

Validate CSV files and CKAN/DCAT-AP datasets with severity-ranked compliance checks.

5|1|Updated Feb 14, 2026
One-click install
npx skills add https://github.com/ondata/skills --skill open-data-quality
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: open-data-quality
Source: https://github.com/ondata/skills/tree/main/skills/open-data-quality
Command: npx skills add https://github.com/ondata/skills --skill open-data-quality

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires uvx, python, duckdb, httpx, charset-normalizer, rich, typer, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you quickly determine whether a dataset is usable by validating both local CSV files and CKAN/open data portal datasets against open data quality and DCAT-AP-style expectations.

Core Features & Use Cases

  • CSV quality validation (local files): checks encoding, headers, structure, common CSV formatting problems, and data content issues.
  • CKAN portal compliance review: validates dataset metadata completeness, accessibility of distribution URLs, and DCAT-AP profile-specific requirements (including multiple national profiles).
  • Actionable, severity-ranked findings: produces blocker/major/minor issues with a plain-language summary and concrete fixes for both analysts and public administrations.

Quick Start

Ask your AI assistant to use the open-data-quality skill to validate the dataset quality for the provided CSV file or CKAN dataset URL, and return a severity-ranked report with recommended fixes.

Frequently Asked Questions about open-data-quality

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate CSV file quality for structural and content issues?

CSV quality validation checks local files for encoding, headers, structure, formatting problems, and data content issues to ensure datasets are usable. It returns severity-ranked findings with concrete fixes.

How do I check CKAN portal dataset metadata completeness and DCAT-AP compliance?

CKAN portal compliance review validates dataset metadata completeness, distribution URL accessibility, and DCAT-AP profile-specific requirements across multiple national profiles. It produces actionable findings for public administrations.

Can I automatically detect and validate open datasets from a URL?

Automated input-type detection identifies whether the input is a local CSV file or a CKAN portal URL. For portal datasets, it optionally downloads the CSV resources before running structural and content checks.

What is the best way to audit open data accessibility and distribution correctness?

Dataset auditing verifies resource accessibility and distribution correctness for different DCAT-AP profiles. It outputs a report with severity-ranked blocker, major, and minor issues suitable for technical and non-technical audiences.

Do I need Python and DuckDB to run open data quality checks?

The validation process requires Python, DuckDB, and uvx to execute structural, content, and metadata compliance checks. These dependencies enable automated CSV parsing and dataset auditing.

Why does my CSV validation report show blocker, major, and minor issue categories?

Severity-ranked findings categorize dataset issues into blocker, major, and minor levels to prioritize fixes. This ranking helps both analysts and public administrations address critical usability problems before minor formatting errors.