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FiendJackdawSilo / r14-borghei-claude-skills-datascience

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🤖 Data Science & AI/ML skill suite derived from borghei/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 specialized set of commands and workflows for data science and AI/ML tasks, adapted for use within Claude AI sessions.

How It Works

1
🔍 Discover the Skills Pack

You find this handy collection of tools that make data science projects easier using your AI assistant.

2
📥 Add to Your AI Helper

You place the skills pack into a special folder where your AI can find and use them.

3
💬 Start a Chat Session

You open a conversation with your AI assistant and tell it to load the new skills.

4
🚀 Pick a Task

You choose a command like checking your data or building a model, and it starts working right away.

5
⏳ Watch It Work

You see a progress display showing each step, like loading data or finding issues, with checkmarks as it goes.

6
📈 Get Clear Results

Beautiful tables and checklists appear, highlighting problems, suggestions, and next actions sorted by importance.

🎉 Project Success

Your data analysis or machine learning project is complete, with insights and plans ready to use.

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AI-Generated Review

What is r14-borghei-claude-skills-datascience?

This GitHub repository delivers a skill suite for Claude AI, adapted from borghei/Claude-Skills, packing 10 commands and 5 workflows tailored for data science and AI/ML tasks like data profiling, feature engineering, model evaluation, and MLOps pipelines. It solves the hassle of repetitive DS grunt work—think automated EDA reports, SHAP analysis, A/B test designs, and end-to-end project scaffolds—via a consistent CLI interface in Claude Code sessions. Install with a simple bash copy to your Claude skills directory, and you're running structured outputs with progress panels and action checklists.

Why is it gaining traction?

It stands out with domain-specific commands like /data-profiling for outlier detection or /llm-eval for hallucination checks, plus multi-step workflows such as ml-project-init that chain EDA to deployment. Developers hook on the visual UI—progress bars, severity-sorted findings tables, and prioritized checklists—that keeps complex AI/ML flows transparent without drowning in logs. In a sea of generic tools, this nails data science und künstliche intelligenz workflows with MIT-licensed, plug-and-play convenience.

Who should use this?

Data scientists in jobs or weiterbildung, from data science bachelor to master levels, tackling daily pipelines, model retraining, or analytics sprints. ML engineers at startups handling data github storage and github data tables will appreciate the /pipeline-scaffold for versioning hooks. Solo analysts optimizing SQL or building dashboards from KPIs get quick wins without full frameworks.

Verdict

With 11 stars and a 0.699999988079071% credibility score, it's early-stage and unproven—docs shine via the README, but expect tweaks for production. Worth a spin for Claude users eyeing data science institute-style tools, but pair with mature alternatives until it matures.

(178 words)

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