profiling-tables

Profile database tables with schema, size, and statistical summaries.

3|Updated Jul 22, 2025

Search Tags:#data quality #sql #duckdb #cardinality #data profiling #table statistics #schema discovery

Ready to run

npx skills add https://github.com/minyeamer/linkmerce --skill profiling-tables-minyeamer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: profiling-tables
Source: https://github.com/minyeamer/linkmerce/tree/main/.agents/skills/profiling-tables
Command: npx skills add https://github.com/minyeamer/linkmerce --skill profiling-tables-minyeamer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Profiling a table manually is slow and uncertain, especially when you need to quickly understand structure, data quality, and distribution before analysis or integration.

Core Features & Use Cases

Schema-first profiling: Collect column metadata (name, type, and comments) to document how the table is intended to be used.
Distribution and quality statistics: Compute row counts, nulls/empties, distinct counts, min/max/avg, and date ranges to reveal shape and anomalies.
Cardinality and sampling: Identify skewed categorical values and generate representative samples to speed up exploratory work.
Decision-ready output: Produce an interpretable summary including an overall quality scoring rubric and recommended follow-up queries.

Quick Start

Use the profiling-tables skill to generate a complete profile for your target table by providing the exact table name you want to analyze.

Frequently Asked Questions about profiling-tables

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

FAQPage Schema

How do I profile a SQL table to understand its schema and data quality?▼

Table profiling gathers schema, size, and statistical summaries to help you understand structure and content. It collects column metadata, row counts, null/empty counts, distinct counts, and representative samples to reveal data distribution and anomalies for analytics or ETL decisions.

What statistics are included when profiling data for cardinality and distribution?▼

Data profiling computes row counts, nulls, empties, distinct counts, min/max/avg for numeric columns, and date ranges to reveal shape and anomalies. It also identifies skewed categorical values through cardinality analysis and generates representative samples to speed up exploratory work.

Can I use DuckDB to profile tables and discover schema information?▼

Yes, DuckDB supports SQL-based metadata queries over INFORMATION_SCHEMA needed for table profiling. The skill applies these queries plus aggregate computations for numeric, string, and date/timestamp columns to gather schema, size, and statistical summaries for data discovery and pipeline debugging.

How do I assess data quality in a table before using it for analytics?▼

Assessing data quality requires computing null/empty counts, distinct counts, and cardinality to identify anomalies and skewed values. The skill produces an interpretable summary including an overall quality scoring rubric and recommended follow-up queries to guide your analytics or ETL decisions.

What's the best way to onboard to an unfamiliar database table for ETL integration?▼

The best way to onboard is running schema-first profiling that collects column names, types, and comments alongside distribution statistics. The skill generates a decision-ready profile with representative samples and recommended follow-up queries, helping you quickly understand structure and content for integration.