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orangevoinULTRA / r02-alirezarezvani-claude-skills-datascience

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🤖 Data Science & AI/ML skill suite derived from alirezarezvani/claude-skills.

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69% credibility
Found May 02, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This project offers a suite of 10 commands and 5 workflows tailored for data science and AI/ML tasks within Claude AI sessions, featuring structured progress displays and action recommendations.

How It Works

1
🔍 Discover Data Skills

You stumble upon this collection of smart tools that make your AI assistant a data science expert.

2
📥 Add to Your AI

You easily add the skill set to your AI helper's toolbox by placing the files in its special folder.

3
💬 Start Chatting

You open a conversation with your AI friend Claude and introduce it to the new data skills.

4
✨ Try a Command

You type a simple instruction like 'profile my data' and instantly get helpful analysis and reports.

5
📊 Watch Progress

You see clear progress updates, issue lists sorted by importance, and easy action checklists.

6
🚀 Run Full Workflows

You launch bigger processes like full machine learning projects, following guided multi-step journeys.

🎉 Achieve Data Wins

You finish tough data tasks with expert insights, plans, and visuals, feeling like a pro without the hassle.

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AI-Generated Review

What is r02-alirezarezvani-claude-skills-datascience?

This repo delivers a specialized skill suite for Claude AI, adapted from alirezarezvani's claude-skills for data science and AI/ML workflows. It packs 10 commands like /data-profiling for automated EDA reports, /feature-engineer for SHAP analysis, and /model-evaluate for performance dashboards, plus 5 multi-step workflows such as ml-project-init for end-to-end ML projects. Install via bash clone into your Claude skills directory to get structured UI outputs with progress panels and action checklists, speeding up data pipelines, model training, and reporting right in AI chats.

Why is it gaining traction?

It stands out with consistent structured outputs—progress bars, severity-sorted findings tables, and prioritized action plans—that keep you oriented during complex DS tasks, unlike generic AI prompts. The domain-specific commands for A/B test design, SQL optimization, and LLM evaluation hook developers tired of piecing together ad-hoc analyses. Derived from alirezarezvani's broader github data repository, it adds visual tracking and workflow orchestration tailored for ai/ml pros chasing data science jobs or weiterbildung.

Who should use this?

Data scientists in data science master programs or studium running quick EDA and anomaly detection on datasets. ML engineers scaffolding pipelines or handling model retraining and MLOps in sprints. Analysts at data science institutes designing dashboards or data contracts, especially those eyeing data science gehalt boosts via efficient AI-assisted workflows.

Verdict

With just 10 stars and a 0.699999988079071% credibility score, it's early-stage and unproven, but MIT-licensed with crisp docs and user-facing polish. Grab it if you're deep in Claude for data science und künstliche intelligenz—test a command like /data-profiling to see if the UI clicks for your github data storage setups.

(178 words)

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