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deep-profile

Profile active datasets to generate schema and profiling insights.

21|11|Updated Mar 15, 2026
Search Tags:#reporting#data-quality#anomaly-detection#temporal-patterns#data-profiling#schema-discovery#correlations
One-click install
npx skills add https://github.com/ai-analyst-lab/ai-analyst-plugin --skill deep-profile
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-profile
Source: https://github.com/ai-analyst-lab/ai-analyst-plugin/tree/main/skills/deep-profile
Command: npx skills add https://github.com/ai-analyst-lab/ai-analyst-plugin --skill deep-profile

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Profiling active datasets to understand schema structure, value distributions, temporal patterns, correlations, completeness gaps, and anomalies helps analysts quickly assess data quality and readiness for analysis.

Core Features & Use Cases

  • Deep schema discovery across tables including column names, types, and nullability to map data assets.
  • Distribution and completeness profiling for numeric and categorical columns to detect quality issues and inform analysis planning.
  • Temporal pattern and correlation analysis to reveal trends, seasonality, and relationships between metrics.

Quick Start

Run the profiler to generate a full data profile for your active dataset.

Frequently Asked Questions about deep-profile

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

FAQPage Schema
What is data profiling and how does it help assess data quality?▼

Data profiling analyzes active datasets to map schema structure, value distributions, and completeness gaps. It helps analysts quickly detect data quality issues and determine readiness for further analysis.

How do I profile a dataset to discover schema and column types?▼

You can profile a dataset by running the profiler to perform deep schema discovery. This automatically extracts column names, data types, and nullability across tables to map your data assets.

Can I detect temporal patterns and correlations in numeric metrics?▼

Yes, data profiling applies temporal pattern and correlation analysis to date-enabled tables and numeric metrics. This reveals underlying trends, seasonality, and relationships between your metrics.

What's the best way to identify anomalies and completeness gaps in tables?▼

The best way to identify anomalies and completeness gaps is running deep profiling on your active dataset. It evaluates distribution and completeness for numeric and categorical columns to flag issues.

Does data profiling work with newly connected datasets and stale profiles?▼

Yes, data profiling prioritizes newly connected datasets and stale profiles. It automatically applies schema discovery, correlation analysis, and automated report generation to your active tables.

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