skills.rest
yethikrishna
yethikrishnaCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

exploration

Profile datasets by inspecting structure, quality, and schema relationships.

Updated Apr 16, 2026
Search Tags:#analytics#schema#data#profiling#exploration#dataset#data-quality
One-click install
npx skills add https://github.com/yethikrishna/humble --skill exploration-yethikrishna
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploration
Source: https://github.com/yethikrishna/humble/tree/main/core/kortix-master/opencode/skills/GENERAL-KNOWLEDGE-WORKER/exploration
Command: npx skills add https://github.com/yethikrishna/humble --skill exploration-yethikrishna

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Systematic methodology for profiling datasets, assessing data quality, discovering patterns, and understanding schemas.

Core Features & Use Cases

  • Data Profiling: compute column-level statistics, null counts, distinct counts, and top values.
  • Quality & Consistency: identify data quality issues, patterns, and anomalies.
  • Use Case: for a new dataset, generate a profiling report including schema, key columns, and potential relationships.

Quick Start

Provide a dataset profile with structure, quality metrics, and recommended next steps.

Frequently Asked Questions about exploration

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

FAQPage Schema
How do I perform systematic data profiling on a new dataset?▼

Data profiling identifies quality issues and anomalies by systematically inspecting schema relationships and computing column-level statistics like null counts, distinct counts, and top values across the dataset.

What is the best way to discover cross-table schema relationships?▼

Cross-table relationship discovery uses robust validation and multi-column profiling to identify schema connections and lineage, providing clear outputs for data governance workflows.

Can I use this for data quality and anomaly detection in analytics workflows?▼

Data quality and anomaly detection apply to analytics and data science workflows by profiling datasets to flag consistency issues, detect patterns, and validate schema relationships.

How does multi-column profiling improve data governance?▼

Multi-column profiling strengthens data governance by inspecting schema relationships and applying robust validation to discover cross-table dependencies and maintain dataset quality.

What should a dataset profile report include for data science workflows?▼

Dataset profile reports provide structure, quality metrics, and recommended next steps, detailing schema, key columns, and potential relationships for data science workflows.

Related Skills

Explore More Data & Analytics Skills→
minicooheiminicoohei
345

csv-analyzer

Analyze CSV files to produce JSON summaries with metadata and statistics.

Community
Intermediate
datheredathere
3.7k

data-quality

Analyze and remediate data quality issues in tabular datasets.

Official
Intermediate
labs21-devlabs21-dev
28

data-exploration

Profile datasets to reveal structure, data quality, and relationships.

Official
Advanced
Amar1404Amar1404
1

deep-profile

Analyze dataset schema, value distributions, temporal patterns, correlations, completeness gaps, and anomalies.

Community
Advanced