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moai-domain-data-science

Automate data science workflow governance and reproducible research processes.

1|Updated Jul 28, 2025
Search Tags:#analysis#data#visualization#statistics#jupyter
One-click install
npx skills add https://github.com/kivo360/quickhooks --skill moai-domain-data-science-kivo360
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: moai-domain-data-science
Source: https://github.com/kivo360/quickhooks/tree/main/.claude/skills/moai-domain-data-science
Command: npx skills add https://github.com/kivo360/quickhooks --skill moai-domain-data-science-kivo360

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

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

Core Features & Use Cases

  • Data Analysis: Pandas/NumPy workflows with reproducibility
  • Visualization: Integrated plotting patterns
  • Modeling: Statistical & ML modeling patterns
  • Reproducible Research: Notebooks, pipelines, and versioned data

Quick Start

Set up a reproducible data notebook and basic analysis pipeline.

Frequently Asked Questions about moai-domain-data-science

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

FAQPage Schema
How do I set up reproducible data science workflows with Pandas and Jupyter?▼

Reproducible data science workflows automate governance and best-practices enforcement across Pandas, NumPy, and Jupyter notebooks. This Skill applies automated code review, TRUST 5 compliance checks, and test-driven development support to ensure consistent, versioned analysis pipelines and research outputs.

Can I use this for data visualization and statistical modeling in Jupyter notebooks?▼

Yes. The Skill integrates visualization and statistical modeling patterns within Jupyter environments, supporting both Pandas/NumPy analysis and reproducible research workflows. It handles integrated plotting patterns and modeling processes with automatic compliance verification.

What's the best way to enforce data science best practices across a team's projects?▼

Domain data science workflow governance enforces best-practices through automated code reviews, feature design validation, and TRUST 5 compliance checks. It applies to Pandas and NumPy projects with both automatic and manual invocation, supporting language detection and project-file access.

How do I troubleshoot data analysis pipelines with built-in governance checks?▼

Troubleshooting support integrates SPEC implementations, code review automation, and compliance validation into your data workflow. The Skill detects issues during analysis, modeling, and visualization stages while maintaining reproducibility across versioned data and notebooks.

Does this Skill support test-driven development for data science projects?▼

Yes. The Skill satisfies test-driven development requirements for data science, combining reproducible research processes with automated validation. It supports dependency integration via moai-foundation-langs and moai-foundation-trust for comprehensive project governance.

Can I use this for both one-off analysis and production research pipelines?▼

The Skill handles both scenarios through flexible invocation—automatic checks run on code changes, while manual triggers support ad-hoc analysis. It governs Pandas/NumPy workflows, Jupyter notebooks, and multi-step pipelines with versioned data and reproducibility guarantees.

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