skills.rest
shekerkamma
shekerkammaCOMMUNITY
Explore all skills by this author →
GitHub

deep-profile

Automates dataset profiling to map schema structure, value distributions, and data quality gaps.

Updated May 22, 2026
Search Tags:#data validation#anomaly detection#schema analysis#temporal patterns#data profiling#dataset quality#analytics preprocessing
One-click install
npx skills add https://github.com/shekerkamma/peopletech-marketplace --skill deep-profile-shekerkamma
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-profile
Source: https://github.com/shekerkamma/peopletech-marketplace/tree/main/plugins/ai-analyst/skills/ai-analyst/deep-profile
Command: npx skills add https://github.com/shekerkamma/peopletech-marketplace --skill deep-profile-shekerkamma

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Users often lack visibility into new or updated datasets' structure, data quality issues, and underlying patterns before starting analysis, leading to flawed insights, wasted effort on unnecessary data cleaning, or missed critical gaps in data coverage.

Core Features & Use Cases

  • Full Schema Introspection: Automatically maps all tables, columns, data types, row counts, and sample values to provide a complete overview of dataset structure.
  • Deep Quality Analysis: Evaluates value distributions, temporal patterns, data completeness (nulls, zeros, empty strings), correlations between numeric columns, and anomalies to flag data quality issues.
  • Structured Profile Report: Generates a standardized, actionable report that serves as a baseline for analysis planning and helps teams prioritize data cleaning steps.
  • Use Case: For example, when onboarding a new e-commerce transaction dataset, this skill automatically identifies missing values in the revenue column, gaps in daily transaction records, and skewed order amount distributions to inform targeted cleaning work.

Quick Start

Use the deep-profile skill to generate a full data quality and structure report 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 prepare datasets for analysis?▼

Data profiling maps schema structure, evaluates value distributions, and detects temporal patterns to identify data quality gaps. It generates structured profile reports that serve as foundational documentation for analysis planning and targeted data cleaning.

How do I automate anomaly detection and completeness assessment for a new dataset?▼

You can automate anomaly detection and completeness assessment by running a deep-profile analysis. This evaluates nulls, zeros, empty strings, and skewed distributions to flag data quality issues and prioritize cleaning steps.

Can I analyze temporal patterns and correlations in my active dataset?▼

Yes, deep-profile analysis detects temporal patterns and calculates correlations between numeric columns in active datasets. This maps underlying data behaviors to inform analytics preprocessing and validate dataset quality.

What is the best way to structure schema analysis before starting data science workflows?▼

The best way to structure schema analysis is full schema introspection, which automatically maps tables, columns, data types, row counts, and sample values to provide a complete overview of dataset structure.

Does data profiling work for analytics preprocessing on updated datasets?▼

Yes, data profiling supports pre-analysis workflows for new or updated datasets. It evaluates value distributions and identifies missing values to prevent flawed insights and wasted effort on unnecessary data cleaning.

Related Skills

Explore More Data & Analytics Skills→
ai-analyst-labai-analyst-lab
21

deep-profile

Profile active datasets to generate schema and profiling insights.

Official
Advanced
letitbkletitbk
37

clean-survey-data

Clean survey data by managing missing values, labels, and category collapsing.

Community
Intermediate
Amar1404Amar1404
1

deep-profile

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

Community
Advanced
wachawowachawo

data-analyzer

Profile dataset structure, completeness, and statistical validity with pandas and numpy.

Community
Intermediate