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data-science

Build and deploy machine learning pipelines with Python, Scikit-learn, TensorFlow, and PyTorch.

1|Updated Nov 18, 2025
Search Tags:#prompt engineering#data science#machine learning#MLOps#LLM#AI#GenAI
One-click install
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-software-architect --skill data-science
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-science
Source: https://github.com/pluginagentmarketplace/custom-plugin-software-architect/tree/main/skills/data-science
Command: npx skills add https://github.com/pluginagentmarketplace/custom-plugin-software-architect --skill data-science

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill streamlines the entire lifecycle of AI and machine learning projects, from data processing and model training to deployment and MLOps, empowering users to build intelligent systems and leverage generative AI without extensive manual research.

Core Features & Use Cases

  • End-to-End ML Pipelines: Design, build, and deploy machine learning models using Python, Scikit-learn, TensorFlow, and PyTorch.
  • Generative AI & LLM Mastery: Master prompt engineering, LLM fine-tuning, RAG systems, and multi-agent architectures.
  • Data Engineering & MLOps: Learn data processing with Pandas/Spark, model monitoring, and production deployment strategies.
  • Use Case: You need to build a chatbot that answers questions based on your company's internal documents. Use this skill to implement a Retrieval-Augmented Generation (RAG) system, fine-tuning an LLM with your specific data, and deploying it for immediate use.

Quick Start

Explain the concept of Retrieval-Augmented Generation (RAG) and provide a simple Python example using LangChain.