rockexecutivesee

rockexecutivesee / r12-vincenthopf-my-claude-code-datascience

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🤖 Data Science & AI/ML skill suite derived from vincenthopf/My-Claude-Code.

25
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69% credibility
Found May 02, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A skill suite providing specialized commands and multi-step workflows for data science, AI/ML, and analytics tasks within Claude AI sessions.

How It Works

1
🔍 Discover the Tool

You hear about a handy collection of data science helpers that work inside your Claude AI chats to make analyzing data and building models easier.

2
📥 Add It to Claude

You simply copy the skill folder into your Claude skills area and tell Claude to load it up during a chat.

3
💬 Start Using Commands

In your Claude conversation, you type a command like 'data profiling' on your dataset and watch it spring to life.

4
📊 See Live Progress

Claude shows a clear progress panel with checkmarks, bars, and tables highlighting issues in your data as it works.

5
✅ Get Smart Insights

You receive organized findings, severity colors, and a prioritized action plan with quick fixes and next steps.

6
🔄 Run Full Workflows

For bigger projects, start a multi-step workflow like initializing an ML project, and it guides you end-to-end.

🎉 Master Your Data Project

Your data analysis or model building is now faster and smarter, with beautiful reports and recommendations at your fingertips.

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AI-Generated Review

What is r12-vincenthopf-my-claude-code-datascience?

This GitHub repository from rockexecutivesee delivers a Claude Code skill suite tailored for data science and AI/ML workflows, packing 10 commands and 5 multi-step processes into a single install. It handles everything from automated EDA reports via /data-profiling to model evaluation dashboards with /model-evaluate, plus pipelines for MLOps and reporting—all with consistent structured UI like progress panels and action checklists. Derived from vincenthopf's original, it's language-agnostic but runs in Claude sessions, solving the pain of repetitive DS tasks like feature engineering or anomaly detection without leaving your terminal.

Why is it gaining traction?

Its hook is domain-specific commands that spit out prioritized findings tables and next-step suggestions, standing out from generic scripting by focusing on data science jobs like A/B test design or LLM eval harnesses. The visual progress displays and workflow orchestration (e.g., ml-project-init for end-to-end ML) make complex processes feel guided, unlike scattered Jupyter notebooks or verbose tools. For data github_repository explorers, it weaves in github data table handling and ai/ml claude integrations seamlessly.

Who should use this?

Data scientists in data science studium or master programs building data science und künstliche intelligenz pipelines, ML engineers scaffolding data science weiterbildung projects, or analysts optimizing sql-optimize for data science institute dashboards. Ideal for data science deutsch speakers eyeing data science gehalt boosts via quick /anomaly-detect or /pipeline-scaffold, but skip if you're deep into custom github data packs.

Verdict

With 10 stars and a 0.699999988079071% credibility score, it's early-stage—docs shine in the README but expect tweaks for production. Worth a quick clone for DS prototyping if you're in the Claude ecosystem; otherwise, monitor for maturity.

(198 words)

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