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Winnershitram / r17-behisecc-awesome-claude-skills-datascience

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

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69% credibility
Found May 02, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A curated collection of 10 commands and 5 workflows for data science and AI/ML tasks, adapted for use with Claude AI to provide structured guidance and outputs.

How It Works

1
🔍 Discover the skills

You hear about a handy pack of data science tools that supercharge your AI assistant Claude for analyzing data and building models.

2
📦 Grab the pack

Download the skills collection and slip it into Claude's special toolbox folder so it's ready to use.

3
🚀 Wake it up

In your chat with Claude, tell it to load the new skills, and it welcomes them with open arms.

4
✨ Dive into analysis

Ask Claude to profile your data or engineer features, and it shows live progress, spots issues, and hands you clear reports with fix ideas.

5
📊 Tackle big projects

Run full workflows for things like complete machine learning setups or data migrations, following guided steps with checklists and summaries.

6
🎯 Get smart recommendations

Claude sorts findings by importance, gives action plans with time estimates, and suggests what to do next for perfect results.

🏆 Become a data pro

You effortlessly create pipelines, evaluate models, and generate insights, turning complex data work into simple, successful wins.

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AI-Generated Review

What is r17-behisecc-awesome-claude-skills-datascience?

This GitHub repository delivers a skill suite for Claude AI, adapted from an awesome list of Claude skills, zeroing in on data science and AI/ML workflows like data pipelines, model training, evaluation, and reporting. You get 10 commands such as /data-profiling for automated EDA reports or /model-evaluate for performance dashboards, plus 5 multi-step workflows like ml-project-init for end-to-end ML projects—all with structured UI elements like progress panels and action checklists. Language is unknown, but it integrates via bash cloning into Claude's skills directory for seamless use in Claude Code sessions, solving the hassle of repetitive DS tasks 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 processes, unlike generic AI prompts. Developers hook on the domain-specific commands for AI/ML, such as /feature-engineer with SHAP analysis or /llm-eval for hallucination checks, plus workflows that chain steps like drift detection to retraining. In a world of scattered data science tools on GitHub, this packs quick wins for data github_repository users tackling github data storage or github data table issues.

Who should use this?

Data scientists in data science jobs or data science master programs handling EDA, feature engineering, and MLOps pipelines. ML engineers designing A/B tests or anomaly detection in production. Analysts in data science studium or weiterbildung courses building reporting pipelines or data contracts, especially those blending data science und künstliche intelligenz with tools like SQL optimization.

Verdict

With 13 stars and a 0.699999988079071% credibility score, it's an early-stage experiment—docs are solid via README but lacks broad adoption or tests. Worth a spin for Claude users in data science deutsch scenes or github data packs exploration, but verify outputs before prime-time data science gehalt projects.

(198 words)

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