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ConsciousnessBrawler / r13-danielrosehill-claude-slash-commands-datascience

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🤖 Data Science & AI/ML skill suite derived from danielrosehill/Claude-Slash-Commands.

22
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69% credibility
Found May 03, 2026 at 22 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This repository offers a specialized set of slash commands and multi-step workflows tailored for data science and AI/ML tasks within Claude AI sessions, featuring structured progress displays and action plans.

How It Works

1
📰 Discover the Toolkit

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

2
📥 Bring It Home

You grab the toolkit and slip it into your AI helper's special folder so it's ready to use.

3
💬 Kick Off a Command

In your chat with the AI, you type a simple slash command like /data-profiling followed by your data details.

4
✨ Watch It Work

A colorful progress panel appears, showing each step of the analysis happening live with checkmarks and updates.

5
📊 Review Results

You get neat tables of findings sorted by importance, checklists of fixes, and smart next steps.

6
🔄 Chain into Workflows

For bigger jobs, start a multi-step workflow like ml-project-init to handle end-to-end projects smoothly.

🎉 Nail Data Projects

Your data science work flows effortlessly, delivering pro insights, models, and reports with clear guidance every time.

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AI-Generated Review

What is r13-danielrosehill-claude-slash-commands-datascience?

This GitHub repository delivers a suite of 10 slash commands and 5 multi-step workflows tailored for data science and AI/ML tasks in Claude AI sessions. It handles everything from automated EDA with /data-profiling to model evaluation via /model-evaluate and MLOps scaffolding with /pipeline-scaffold, all outputting structured progress panels, findings tables, and action checklists. Derived from a general Claude slash commands repo, it solves the pain of repetitive DS workflows by giving you a consistent UI for pipelines, anomaly detection, and LLM evals—no coding required, just copy to your Claude skills dir and /read it.

Why is it gaining traction?

It stands out with domain-specific commands for data science jobs like feature engineering with SHAP or A/B test design, plus visual progress bars and prioritized action plans that keep you oriented during analysis. Unlike generic prompt libraries, the workflows chain steps like ml-project-init for end-to-end ML projects, making complex tasks like data migration or model retraining feel guided. Devs grab it for the flat command listing and hooks that integrate seamlessly into Claude, boosting productivity on github data storage or ai/ml experiments.

Who should use this?

Data scientists in data science master programs or data science studium chasing data science gehalt jumps, handling EDA, pipelines, or anomaly detection daily. ML engineers at data science institutes building reporting pipelines or llm-eval harnesses for production. Teams worried about github data protection agreement in data science und künstliche intelligenz projects needing quick sql-optimize or dashboard-spec.

Verdict

With 18 stars and a 0.699999988079071% credibility score, it's early-stage—docs are solid in one README but lacks tests or examples beyond mocks, so treat as a prompt starter for Claude DS workflows. Worth forking for data science weiterbildung if you're deep in ai/ml with Claude; skip if you need battle-tested github data packs.

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