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exploration

Profile dataset structure, quality, and schema patterns across tabular, event-log, and JSON sources.

Updated Apr 8, 2026
Search Tags:#eda#data-quality#anomaly-detection#correlation-analysis#data-profiling#schema-discovery#column-profiling
One-click install
npx skills add https://github.com/aimentor606/aether --skill exploration-aimentor606
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploration
Source: https://github.com/aimentor606/aether/tree/main/core/kortix-master/opencode/skills/GENERAL-KNOWLEDGE-WORKER/exploration
Command: npx skills add https://github.com/aimentor606/aether --skill exploration-aimentor606

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a systematic methodology to quickly understand dataset structure, assess data quality, and surface patterns and anomalies so analysts and engineers can trust downstream analysis and pipelines.

Core Features & Use Cases

  • Structural profiling: row/column counts, grain, primary key checks, and column classification (identifier, dimension, metric, temporal, text, boolean, structural).
  • Column-level statistics: null rates, distinct counts, top/bottom values, numeric percentiles and dispersion, string length and pattern checks, and timestamp range analysis.
  • Relationship & quality discovery: foreign key candidate detection, correlation matrices, completeness scoring, consistency and accuracy checks, timeliness assessment, and documentation templates for schema and lineage.
  • Use Case: Run this methodology to validate a staging dataset before promotion to a reporting mart, identify stale or anomalous records, and generate a schema document for the analytics team.

Quick Start

Run a profile on the target table to compute column null rates, top values, numeric percentiles, correlations, and generate a schema documentation template.

Frequently Asked Questions about exploration

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

FAQPage Schema
How do I run exploratory data analysis to check data quality before modeling?▼

Run exploratory data analysis to compute column null rates, distinct counts, numeric percentiles, and correlations. This profiles dataset structure, assesses data quality, and surfaces anomalies to validate staging datasets before downstream modeling or reporting.

What is data profiling and how does it uncover schema patterns in a dataset?▼

Data profiling analyzes datasets to discover schema and distributional patterns by classifying columns and computing statistics. It identifies primary keys, detects foreign key candidates, and generates a schema documentation template for analytics teams.

Can I use this to profile JSON-structured sources and event logs, or just tabular data?▼

You can profile tabular, event-log, and JSON-structured sources. The methodology applies structural profiling and column-level statistics across these formats to assess completeness, consistency, and accuracy for data validation.

How do I detect anomalies and correlations in my dataset columns?▼

Detect anomalies and correlations by computing correlation matrices, numeric percentiles, and dispersion metrics. The analysis assesses data quality by surfacing stale records, rare values, and null rates to identify issues in your dataset.

What is the best way to validate a staging dataset before promoting it to a reporting mart?▼

The best way to validate a staging dataset is to profile its structure, compute completeness scores, and perform consistency and accuracy checks. This identifies stale or anomalous records and generates schema documentation prior to promotion.

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