Holddrespell

Holddrespell / r16-voltagent-awesome-agent-skills-datascience

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🤖 Data Science & AI/ML skill suite derived from VoltAgent/awesome-agent-skills.

26
0
69% credibility
Found May 03, 2026 at 26 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A curated set of 10 commands and 5 multi-step workflows for data science and AI/ML tasks, providing structured outputs and progress tracking for AI agent integration.

How It Works

1
🔍 Discover the toolkit

You stumble upon this handy collection of smart tools designed to make data science and AI projects easier with your AI assistant.

2
📥 Grab the skills

You download the toolkit and place it in the special folder where your AI helper keeps its extra abilities.

3
🗣️ Introduce it to your AI

In your chat with the AI, you simply tell it to read the new skills file so it learns these data tricks.

4
🚀 Try a command

You give a simple instruction like 'profile my data' and instantly see it dive into analysis with progress updates.

5
📊 Review the results

Beautiful panels appear showing what's good, what's broken, and easy fixes sorted by importance.

6
🔄 Run a full workflow

You start an end-to-end process like building a complete data project, watching each stage light up as it completes.

✅ Project success

Your data is cleaned, analyzed, modeled, and ready to use, feeling like you have a expert team at your fingertips.

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Star Growth

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AI-Generated Review

What is r16-voltagent-awesome-agent-skills-datascience?

This repo delivers a suite of 10 commands and 5 multi-step workflows tailored for data science and AI/ML tasks, like automated EDA with /data-profiling, feature engineering via /feature-engineer, and full ML project initialization through ml-project-init. Derived from VoltAgent's agent skills, it plugs into Claude Code via a simple bash clone and /read command, giving you structured outputs, progress panels, and action checklists for pipelines, model evaluation, MLOps, and reporting. It solves the drudgery of repetitive DS grunt work by providing consistent, UI-driven tools that track progress and prioritize fixes.

Why is it gaining traction?

Unlike generic agent skill packs, this zeroes in on DS-specific needs with visual progress bars, severity-sorted findings tables, and orchestrated workflows that handle end-to-end processes like data migration or model retraining. Developers notice the immediate wins: real-time dashboards for ROC curves, SHAP analysis, or anomaly detection without setup hassle. With 25 stars, it's pulling in users from data science jobs and github data repositories seeking agent-based AI/ML automation.

Who should use this?

Data scientists in data science studium or master programs building portfolios, ML engineers at data science institutes handling daily pipelines, or analysts in data science weiterbildung tackling A/B tests and SQL optimization. Ideal for teams worried about github data residency and protection in agent workflows, or those in data science und künstliche intelligenz roles needing quick LLM evals and dashboard specs.

Verdict

Try it if you're in early-stage DS experimentation—solid docs and MIT license make it low-risk, but with just 25 stars and a 0.699999988079071% credibility score, it's immature without tests or broad adoption. Pair with production tools once proven in your github data storage setup.

(178 words)

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