SkillHub ClubAnalyze Data & AIFull StackData / AI

moai-domain-data-science

Imported from https://github.com/kivo360/quickhooks.

Packaged view

This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.

Stars

Hot score

Updated

March 20, 2026

Overall rating

C2.6

Composite score

2.6

Best-practice grade

B74.4

Install command

npx @skill-hub/cli install kivo360-quickhooks-moai-domain-data-science

Repository

kivo360/quickhooks

Skill path: .claude/skills/moai-domain-data-science

Imported from https://github.com/kivo360/quickhooks.

Open repository

Best for

Primary workflow: Analyze Data & AI.

Technical facets: Full Stack, Data / AI.

Target audience: everyone.

License: Unknown.

Original source

Catalog source: SkillHub Club.

Repository owner: kivo360.

This is still a mirrored public skill entry. Review the repository before installing into production workflows.

What it helps with

Install moai-domain-data-science into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
Review https://github.com/kivo360/quickhooks before adding moai-domain-data-science to shared team environments
Use moai-domain-data-science for development workflows

Works across

Claude CodeCodex CLIGemini CLIOpenCode

Favorites: 0.

Sub-skills: 0.

Aggregator: No.

Original source / Raw SKILL.md

---
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']
allowed-tools:
  - Read
  - Bash
---

# 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