When should I use moai-domain-data-science?

moai-domain-data-science is useful in the following scenarios: • Automating data cleaning, preprocessing, and exploratory data analysis (EDA) using Pandas and NumPy. • Building and validating complex statistical models and generating high-quality visualizations for research or business insights. • Ensuring code quality and reproducibility in data science projects through TRUST 5 compliance checks and TDD (Test-Driven Development) workflows. • Migrating legacy data scripts to modern versions of data science libraries while maintaining high test coverage and documentation standards.

When & Why to Use This Skill

This Claude skill provides a comprehensive environment for data science and reproducible research, enabling advanced data analysis, statistical modeling, and professional visualization. It integrates industry-standard tools like Pandas and NumPy with the TRUST 5 quality principles to ensure that data workflows are not only efficient but also reliable, testable, and maintainable.

Use Cases

Automating data cleaning, preprocessing, and exploratory data analysis (EDA) using Pandas and NumPy.
Building and validating complex statistical models and generating high-quality visualizations for research or business insights.
Ensuring code quality and reproducibility in data science projects through TRUST 5 compliance checks and TDD (Test-Driven Development) workflows.
Migrating legacy data scripts to modern versions of data science libraries while maintaining high test coverage and documentation standards.

name	moai-domain-data-science
version	2.0.0
created	2025-10-22
updated	2025-10-22
status	active
description	Data analysis, visualization, statistical modeling, and reproducible research workflows.
keywords	['data', 'analysis', 'visualization', 'statistics', 'jupyter']

name

moai-domain-data-science

version

2.0.0

created

2025-10-22

updated

2025-10-22

status

active

description

Data analysis, visualization, statistical modeling, and reproducible research workflows.

keywords

['data', 'analysis', 'visualization', 'statistics', 'jupyter']

Domain Data Science Skill

Skill Metadata

Field	Value
Skill Name	moai-domain-data-science
Version	2.0.0 (2025-10-22)
Allowed tools	Read (read_file), Bash (terminal)
Auto-load	On demand when keywords detected
Tier	Domain

What It Does

Data analysis, visualization, statistical modeling, and reproducible research workflows.

Key capabilities:

✅ Best practices enforcement for domain domain
✅ TRUST 5 principles integration
✅ Latest tool versions (2025-10-22)
✅ TDD workflow support

When to Use

Automatic triggers:

Related code discussions and file patterns
SPEC implementation (/alfred:2-run)
Code review requests

Manual invocation:

Review code for TRUST 5 compliance
Design new features
Troubleshoot issues

Tool Version Matrix (2025-10-22)

Tool	Version	Purpose	Status
Pandas	2.2.0	Primary	✅ Current
NumPy	2.2.0	Primary	✅ Current
Jupyter	1.1.0	Primary	✅ Current

Inputs

Language-specific source directories
Configuration files
Test suites and sample data

Outputs

Test/lint execution plan
TRUST 5 review checkpoints
Migration guidance

Failure Modes

When required tools are not installed
When dependencies are missing
When test coverage falls below 85%

Dependencies

Access to project files via Read/Bash tools
Integration with moai-foundation-langs for language detection
Integration with moai-foundation-trust for quality gates

References (Latest Documentation)

Documentation links updated 2025-10-22

Changelog

v2.0.0 (2025-10-22): Major update with latest tool versions, comprehensive best practices, TRUST 5 integration
v1.0.0 (2025-03-29): Initial Skill release

Works Well With

moai-foundation-trust (quality gates)
moai-alfred-code-reviewer (code review)
moai-essentials-debug (debugging support)

Best Practices

✅ DO:

Follow domain best practices
Use latest stable tool versions
Maintain test coverage ≥85%
Document all public APIs

❌ DON'T:

Skip quality gates
Use deprecated tools
Ignore security warnings
Mix testing frameworks