Legionkyomanacle

Legionkyomanacle / r10-wshobson-commands-datascience

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🤖 Data Science & AI/ML skill suite derived from wshobson/commands.

20
0
69% credibility
Found May 03, 2026 at 20 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 offering 10 commands and 5 workflows for data science and AI/ML tasks like profiling, modeling, and pipelines, designed for structured output in AI chat environments.

How It Works

1
🔍 Discover Data Tools

You find this helpful collection of data analysis and AI building tools while looking for ways to simplify your science projects.

2
📦 Add to AI Helper

You simply place these tools into your AI assistant's skill collection so it can use them right away.

3
💬 Start a Command

In your chat with the AI, you ask it to profile your data or build a model, and it jumps into action.

4
📈 See Progress Unfold

A clear panel shows each step advancing with checkmarks, colorful issue highlights, and smart suggestions popping up.

5
Choose Your Path
⚡
Quick Command

Finish a fast check like finding data problems or testing a model.

🔄
Full Workflow

Run an end-to-end journey like starting a whole machine learning project.

6
📋 Review Results

You get neat tables of findings, priority checklists, and ideas for what to do next.

🎉 Project Powers Up

Your data work or AI model is now analyzed, improved, and ready to deliver great results effortlessly.

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Star Growth

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AI-Generated Review

What is r10-wshobson-commands-datascience?

This GitHub repository delivers 10 slash commands and 5 multi-step workflows for data science and AI/ML tasks in Claude Code environments. It tackles repetitive chores like data profiling, feature engineering, model evaluation, and pipeline scaffolding with interactive, structured UIs featuring progress panels, findings tables, and action checklists. Users get consistent outputs for EDA reports, SHAP analysis, A/B test designs, and end-to-end ML projects, derived from a general commands suite.

Why is it gaining traction?

It stands out with domain-specific AI/ML commands like /llm-eval for hallucination checks and /anomaly-detect for time-series alerts, plus workflows for model retraining and analytics sprints—far beyond generic tools. The hook is the uniform interaction: scope confirmation, live progress, prioritized recommendations, and next-step suggestions, making data science jobs faster without switching contexts. Easy bash clone install into Claude skills keeps it lightweight.

Who should use this?

Data scientists and ML engineers handling daily pipelines, from data science bachelor projects to master-level MLOps in data science studium or weiterbildung. Perfect for teams designing A/B tests (/ab-test-design), optimizing SQL (/sql-optimize), or running reporting pipelines, especially in data science und künstliche intelligenz roles. Suited for github data repository audits or github data table workflows.

Verdict

At 17 stars and 0.7% credibility score, it's immature with just a solid README—no tests or broad adoption yet—but the parent repo's foundation makes it worth a quick Claude test for AI/ML commands. Grab it if you need structured DS tools now; otherwise, monitor for data science institute validation.

(198 words)

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