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mikeparcewski
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data

Profile tabular datasets and validate them against JSON schemas to generate quality reports.

8|2|Updated Feb 9, 2026
Search Tags:#validation#schema#quality#profiling#dataset#data-quality
One-click install
npx skills add https://github.com/mikeparcewski/wicked-garden --skill data-mikeparcewski
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data
Source: https://github.com/mikeparcewski/wicked-garden/tree/main/skills/data/data
Command: npx skills add https://github.com/mikeparcewski/wicked-garden --skill data-mikeparcewski

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Profiling, validating, and improving quality of datasets in data pipelines by providing structured profiling, schema validation, and quality reports.

Core Features & Use Cases

  • Profile datasets to infer schema, row counts, types, null rates, and sample records for quick data understanding and quality scoring.
  • Validate data against a JSON schema to enforce constraints and detect violations before loading into downstream systems.
  • Generate comprehensive quality reports covering completeness, uniqueness, and correctness to guide remediation and governance.

Quick Start

Use the data skill to profile a dataset, validate against a schema, and generate a quality report in one workflow.

Frequently Asked Questions about data

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

FAQPage Schema
How do I profile a dataset to infer schema and calculate null rates?▼

Dataset profiling parses tabular files to infer schema, count rows, identify column types, calculate null rates, and extract sample records. It generates structured profiling results to score data quality within data pipelines.

Can I validate tabular data against a JSON schema before loading it downstream?▼

Yes, schema validation enforces constraints and detects violations before loading data into downstream systems. It parses tabular datasets against a JSON schema to ensure correctness and prevent invalid records from entering data pipelines.

What is data-quality profiling and when do I need it in a data pipeline?▼

Data-quality profiling assesses dataset completeness, uniqueness, and correctness to guide remediation and governance. You need it when processing small to large tabular datasets in pipelines to detect anomalies and assure quality before downstream consumption.

Do I need Python to run data profiling and schema validation scripts?▼

Yes, the workflow requires Python scripts like data_profiler.py and schema_validator.py to parse, profile, and validate data. These scripts generate the structured profiling results and quality metrics required for pipeline automation.

What's the best way to generate a data-quality report covering completeness and uniqueness?▼

The best way is running a combined profiling and validation workflow that generates comprehensive quality reports. This process evaluates completeness, uniqueness, and correctness across tabular datasets to guide data remediation and governance efforts.

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