How to use AI skills—reusable packages of instructions and files—to automate repetitive data science workflows. By moving beyond simple prompting into structured skills, users can maintain shorter context windows while ensuring consistent, high-quality outputs for complex tasks like data visualization or metric investigation.
* A skill consists of a SKILL.md file with metadata and detailed instructions to guide an AI through specific recurring processes.
* Using skills helps keep the main LLM context lightweight by only loading detailed resources when they are relevant to the task.
* The author demonstrates this by automating a weekly visualization habit, reducing a one-hour manual process to less than ten minutes.
* Building effective skills requires iterative testing, incorporating personal domain knowledge, and researching external best practices.
* Combining skills with Model Context Protocol (MCP) allows AI to both follow specific procedural playbooks and access external data tools seamlessly.
* A skill consists of a SKILL.md file with metadata and detailed instructions to guide an AI through specific recurring processes.
* Using skills helps keep the main LLM context lightweight by only loading detailed resources when they are relevant to the task.
* The author demonstrates this by automating a weekly visualization habit, reducing a one-hour manual process to less than ten minutes.
* Building effective skills requires iterative testing, incorporating personal domain knowledge, and researching external best practices.
* Combining skills with Model Context Protocol (MCP) allows AI to both follow specific procedural playbooks and access external data tools seamlessly.
The New Stack encourages its readers to contribute to Towards Data Science, a leading platform for data science and AI. Recognizing the increasing convergence of cloud infrastructure, DevOps, and AI engineering, the article invites practitioners to share their experiences with building and deploying AI systems. Successful TDS submissions are technically detailed, timely, and specific. Authors can also benefit from editorial support, promotion, and potential payment opportunities, while building their reputation within the AI community.
This tutorial explores how to use LLM embeddings as features in time series forecasting models. It covers generating embeddings from time series descriptions, preparing data, and evaluating the performance of models with and without LLM embeddings.
This course takes you from Python fundamentals to AI Agent development, covering core Python, NumPy, Pandas, SQL, Flask, FastAPI, LLMs, and open-source models via HuggingFace.
This article details how to build a 100% local MCP (Model Context Protocol) client using LlamaIndex, Ollama, and LightningAI. It provides a code walkthrough and explanation of the process, including setting up an SQLite MCP server and a locally served LLM.
This article is a year-end recap from Towards Data Science (TDS) highlighting the most popular articles published in 2025. The year was heavily focused on AI Agents and their development, with significant interest in related frameworks like MCP and contextual engineering. Beyond agents, Python remained a crucial skill for data professionals, and there was a strong emphasis on career development within the field. The recap also touches on the evolution of RAG (Retrieval-Augmented Generation) into more sophisticated context-aware systems and the importance of optimizing LLM (Large Language Model) costs. TDS also celebrated its growth as an independent publication and its Author Payment
"Talk to your data. Instantly analyze, visualize, and transform."
Analyzia is a data analysis tool that allows users to talk to their data, analyze, visualize, and transform CSV files using AI-powered insights without coding. It features natural language queries, Google Gemini integration, professional visualizations, and interactive dashboards, with a conversational interface that remembers previous questions. The tool requires Python 3.11+, a Google API key, and uses Streamlit, LangChain, and various data visualization libraries
Analyzia is a data analysis tool that allows users to talk to their data, analyze, visualize, and transform CSV files using AI-powered insights without coding. It features natural language queries, Google Gemini integration, professional visualizations, and interactive dashboards, with a conversational interface that remembers previous questions. The tool requires Python 3.11+, a Google API key, and uses Streamlit, LangChain, and various data visualization libraries
This article explores how prompt engineering can be used to improve time-series analysis with Large Language Models (LLMs), covering core strategies, preprocessing, anomaly detection, and feature engineering. It provides practical prompts and examples for various tasks.
Extracting structured information effectively and accurately from long unstructured text with LangExtract and LLMs. This article explores Google’s LangExtract framework and its open-source LLM, Gemma 3, demonstrating how to parse an insurance policy to surface details like exclusions.
This article explores alternatives to NotebookLM, a Google assistant for synthesizing information from documents. It details NousWise, ElevenLabs, NoteGPT, Notion, Evernote, and Obsidian, outlining their key features, limitations, and considerations for choosing the right tool.
