data-analysis-workflow

Name: data-analysis-workflow
Availability: InStock
Author: timlai666

Plan and execute a 12-step data analysis pipeline from cleaning to reporting.

Updated Mar 3, 2026

Search Tags:#modeling #data-analysis #eda #data-quality #data-cleaning #analytics-workflow

Ready to run

npx skills add https://github.com/TimLai666/skills --skill data-analysis-workflow-timlai666

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-analysis-workflow
Source: https://github.com/TimLai666/skills/tree/main/data-analysis-workflow
Command: npx skills add https://github.com/TimLai666/skills --skill data-analysis-workflow-timlai666

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Data Analysis Workflow helps teams standardize and automate end-to-end data analysis tasks, turning ad-hoc analyses into repeatable, auditable processes that consistently deliver insights.

Core Features & Use Cases

Standardized 12-step analytics pipeline from data ingestion to final report.
Supports schema inspection, data quality audit, cleaning, EDA, modeling, evaluation, and insights.
Use cases include planning a new data project, auditing existing data workflows, and producing structured analytics reports.

Quick Start

Plan and execute an end-to-end data analysis workflow on your dataset.

Frequently Asked Questions about data-analysis-workflow

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

FAQPage Schema

How do I build an end-to-end data analysis workflow from scratch?▼

An end-to-end data analysis workflow standardizes your process through a 12-step pipeline covering schema inspection, data quality audit, cleaning, EDA, modeling, evaluation, and structured reporting. This turns ad-hoc analyses into repeatable, auditable processes that consistently deliver insights.

What is the best way to standardize data cleaning and EDA across multiple projects?▼

Standardizing data cleaning and EDA across multiple projects requires implementing a fixed analytics pipeline that enforces consistent schema inspection, data quality audits, and feature engineering steps. This ensures every dataset undergoes identical preparation before modeling and evaluation.

What steps are included in a standardized data analytics pipeline?▼

A standardized data analytics pipeline includes 12 steps: schema inspection, data quality audit, cleaning, EDA, feature engineering, modeling, evaluation, and reporting. Each step produces structured deliverables to maintain auditability across multiple projects.

Can I audit my existing data workflows for quality and consistency?▼

Auditing existing data workflows involves running them through a standardized 12-step analytics pipeline to evaluate their schema inspection, data quality audit, and cleaning stages. This identifies gaps in your current process and produces structured analytics reports for review.

Does this data analysis pipeline require specific modeling frameworks or dependencies?▼

This data analysis pipeline operates without external dependencies, using a structured 12-step process to guide modeling and evaluation. It focuses on the analytics workflow itself rather than tying you to specific modeling frameworks or platforms.