ChamberTeller

ChamberTeller / b02-skills-main-datascience

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🤖 Data Science Engineering Skills — TDD and planning skills for ML pipelines, data APIs and analytical tooling.

25
0
100% credibility
Found May 02, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A set of guided planning skills and workflows for structuring data science, machine learning pipelines, and model deployments using an AI assistant.

How It Works

1
🕵️ Discover helpful guides

You find a collection of smart planning tools for data science projects while looking for ways to organize your machine learning work.

2
📥 Add to your AI helper

You easily place these guides into your AI assistant's special folder so it's ready to use.

3
🎯 Pick your project challenge

You choose a guide like planning a data pipeline or preparing a model launch that matches what you're trying to build.

4
💬 Chat to set the focus

You describe your goal to the AI, and it confirms exactly what to tackle first, making you feel on the right track.

5
📊 Watch it work live

A progress bar fills up as the AI thinks step by step, showing reds turning green like a game you win.

6
📋 Get your action plan

You receive clear tables of findings sorted by importance, quick wins, and next steps to follow.

🚀 Projects planned perfectly

Your data science or model project now has a complete roadmap, helping you build confidently without guesswork.

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AI-Generated Review

What is b02-skills-main-datascience?

This repo delivers eight AI-driven skills and three workflows tailored for data science engineering, emphasizing TDD and planning in ML pipelines, data APIs, and analytical tooling. Load them into Claude Code sessions via a simple copy to your skills directory, and they guide you through tasks like ETL refactoring, model evaluation TDD, and hypothesis validation with live progress tracking and structured outputs. It's Markdown-based for Claude AI, solving the chaos of ad-hoc data science workflows by enforcing disciplined, testable steps—perfect for data science master projects or data science jobs involving künstliche intelligenz.

Why is it gaining traction?

Adapted from a proven general dev skills set, it stands out by specializing in data science deutsch-style precision for pipelines and APIs, with user-facing features like progress bars, impact-sorted findings, and chained workflows such as ml-sprint from hypothesis to deploy. Developers hook on the TDD-first approach that catches data quality issues early, unlike generic prompts, plus GitHub issues breakdown for analytical teams. Low stars (11) but appeals to those tired of vague AI outputs in data science weiterbildung or institute experiments.

Who should use this?

Data engineers refactoring ETL for data github_repository scale, ML ops leads planning model launches with canary rollouts, or analytical devs triaging data quality in dbt-linked pipelines. Ideal for data science bachelor grads entering data science gehalt-competitive jobs, or teams handling github data storage and protection agreements in production APIs.

Verdict

Skip unless you're deep in data science studium or und künstliche intelligenz pipelines—1.0% credibility score and 11 stars signal early immaturity with thin docs and no tests shown. Promising fork for TDD fans, but test it small before committing to workflows.

(178 words)

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