data-analysis

Profile datasets and translate statistics into business narratives.

Updated Apr 9, 2026

Search Tags:#storytelling #profiling #distributions #data-analysis #anomaly-detection #exploratory-data-analysis #correlations

Ready to run

npx skills add https://github.com/fabioc-aloha/tldr --skill data-analysis-fabioc-aloha

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/fabioc-aloha/tldr/tree/main/.github/skills/data-analysis
Command: npx skills add https://github.com/fabioc-aloha/tldr --skill data-analysis-fabioc-aloha

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data Analysis enables turning raw data into actionable insights by performing profiling, distribution exploration, correlation discovery, anomaly detection, and translating statistics into narrative business-value.

Core Features & Use Cases

Data Profiling: quickly profile a dataset to assess quality, structure, and readiness.
Descriptive Statistics & Distribution Analysis: compute key metrics and distributions to expose patterns and anomalies.
Insight Translation & Storytelling: convert statistical findings into business-language insights with recommended next steps and story intents for dashboards.
Use Case: analyze a marketing dataset to uncover customer segments and growth opportunities, then narrate implications for strategy.

Quick Start

Start by profiling your dataset, generating key statistics, and translating the findings into business-ready insights.

Frequently Asked Questions about data-analysis

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

FAQPage Schema

How do I perform exploratory data analysis on a raw dataset?▼

Exploratory data analysis profiles your dataset to assess quality and structure, computes descriptive statistics, explores distributions, discovers correlations, and detects anomalies to expose underlying patterns.

What is the best way to translate data distributions and statistics into business insights?▼

Translating statistics into business insights involves converting statistical findings into narrative business-value using DIKW-based storytelling, providing recommended next steps and story intents for dashboards.

Can I use data profiling for anomaly detection and segmentation across different domains?▼

Data profiling applies across domains and data sizes to assess dataset readiness, while modular capabilities support anomaly detection, relationship discovery, and segmentation for both small tabular datasets and larger corpora.

How does correlation discovery work during distribution analysis?▼

Correlation discovery during distribution analysis computes key metrics and explores distributions to identify relationships between variables, exposing patterns and anomalies for segmentation and time-series analysis.

What is included in an end-to-end EDA pipeline for datasets?▼

An end-to-end EDA pipeline includes modular capabilities for profiling, statistics, segmentation, time-series analysis, and DIKW-based insight translation, delivering configurable outputs for actionable data-driven insights.

Do I need a specific data format for anomaly detection and time-series analysis?▼

Anomaly detection and time-series analysis process tabular datasets and larger corpora without specific data format dependencies, applying profiling and statistics to uncover anomalies and relationships.