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guardtrailclip / r03-anthropics-skills-datascience

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🤖 Data Science & AI/ML skill suite derived from anthropics/skills.

28
0
69% credibility
Found May 02, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A user-friendly adaptation of AI assistant skills tailored for data science and machine learning tasks like profiling data, engineering features, and running full project workflows.

How It Works

1
🔍 Discover the toolkit

You find this collection of helpful data science tools designed for your AI assistant while looking for ways to analyze data easily.

2
📦 Add to your AI

You place the toolkit into your AI assistant's special skills folder so it's ready to use.

3
💬 Start a chat session

You open a conversation with your AI helper and mention the new skills you added.

4
🚀 Run your first analysis

You ask your AI to check your data with a simple command like data profiling, and it springs into action with a clear progress display.

5
Choose your path
⚡
Quick command

Pick one tool like feature engineering to get fast insights and tips.

🔄
Full workflow

Launch a complete process like starting an ML project from scratch to end.

6
📈 Review results

Your AI shows organized findings, issue lists sorted by importance, and a prioritized plan of easy next actions.

🎉 Master your data projects

You now effortlessly handle data analysis, build models, and create reports, feeling like a pro data scientist with your AI sidekick.

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AI-Generated Review

What is r03-anthropics-skills-datascience?

This GitHub repository delivers a skill suite for Claude AI, adapted from Anthropic's official skills templates to tackle data science and AI/ML workflows. It equips you with 10 commands like /data-profiling for automated EDA reports and /model-evaluate for performance dashboards, plus 5 multi-step workflows such as ml-project-init for end-to-end ML projects. Users get structured UI outputs—progress panels, findings tables, and action checklists—streamlining data pipelines, model training, and reporting without traditional coding.

Why is it gaining traction?

It stands out by wrapping DS tasks in consistent, visual interfaces with real-time progress and prioritized recommendations, unlike scattered Jupyter notebooks or generic AI prompts. Developers hook on the domain-specific commands for feature engineering with SHAP or anomaly detection, plus workflows that orchestrate complex processes like model retraining. In a sea of data science weiterbildung resources and GitHub data packs, its focus on AI/ML saves hours on repetitive analysis.

Who should use this?

Data scientists building pipelines or running EDA on messy datasets, ML engineers handling evaluations and A/B tests, and analysts speccing dashboards from KPIs. Ideal for data science master students in data science studium or pros eyeing data science jobs und künstliche intelligenz, especially those in Claude Code sessions auditing GitHub data storage or optimizing SQL.

Verdict

With just 11 stars and a 0.699999988079071% credibility score, it's early-stage and unproven—docs are solid in the README but lack tests or examples beyond basics. Worth a quick install for Claude users in data science bachelor projects if you need fast scaffolding, but skip for production until it matures.

(178 words)

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