data-validator

Name: data-validator
Availability: InStock
Author: JansenAnalytics

Infer schemas and validate data quality across CSV, JSON, SQLite, and APIs.

5|Updated Apr 8, 2026

Search Tags:#validation #integrity #drift #data-quality #data-pipeline #schema-inference

Ready to run

npx skills add https://github.com/JansenAnalytics/claudex --skill data-validator-jansenanalytics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-validator
Source: https://github.com/JansenAnalytics/claudex/tree/main/skills/data-validator
Command: npx skills add https://github.com/JansenAnalytics/claudex --skill data-validator-jansenanalytics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires better-sqlite3, and includes scripts (resource) components.

What problem does it solve?

Data quality is critical for reliable analytics and operations; this skill automatically infers schemas, validates data, and surfaces issues across CSV, JSON, SQLite, and API sources to prevent bad data from propagating.

Core Features & Use Cases

Schema inference and validation for tabular and API data sources (CSV, JSON, SQLite, APIs).
Data quality scoring across completeness, uniqueness, consistency, accuracy, drift, and referential integrity.
Cross-dataset comparison and drift detection to identify changes between versions of datasets.
Time-series-aware checks and integrity validations to ensure data reliability in pipelines.
Automated reporting to feed dashboards or downstream automation.

Quick Start

Run data-validator on a dataset to infer a schema and validate data quality.

Frequently Asked Questions about data-validator

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

FAQPage Schema

How do I automate data quality validation across CSV, JSON, and SQLite sources?▼

Automate data quality validation by inferring schemas and assessing completeness, uniqueness, consistency, and referential integrity across CSV, JSON, SQLite, and API datasets. The skill returns a structured report of quality metrics and drift findings suitable for downstream pipeline automation.

What is data drift detection and how does it work for dataset comparison?▼

Data drift detection identifies changes between versions of datasets by comparing them to surface structural and content shifts. It applies cross-dataset comparison and time-series-aware checks to ensure data reliability and prevent bad data from propagating through pipelines.

Can I use this tool to infer schemas for API data sources?▼

Yes, schema inference works for tabular and API data sources. It automatically infers schemas for CSV, JSON, SQLite, and API inputs, validating datasets against the inferred schema to assess accuracy, completeness, uniqueness, consistency, and referential integrity.

Does this data validator support referential integrity checks?▼

Yes, the data validator applies referential integrity checks alongside completeness, uniqueness, consistency, and accuracy validations. It infers schemas across multiple sources and returns a structured report of quality metrics and drift findings to ensure data reliability.

How do I prevent bad data from propagating through my data pipeline?▼

Prevent bad data propagation by running autonomous data quality validation to infer schemas and surface issues across CSV, JSON, SQLite, and API sources. It scores data quality, detects drift, and returns a structured report to feed downstream automation or dashboards.

Are there limitations when validating SQLite databases with this approach?▼

A known constraint is the dependency on better-sqlite3, which requires a compatible Node.js environment to validate SQLite databases. The skill processes SQLite, CSV, JSON, and API sources to generate structured reports of schema, quality metrics, and drift findings.