Skill UI

Provides automated regression analysis guidance, best practices, code snippets, and validation for SQL, BI, visualization, and statistical workflows in data analytics projects.

Automates schema validation within data pipelines, providing guidance on best practices, workflow orchestration, and streaming ETL scenarios while producing production-ready code and configurations.

Automates spark job creation within data pipelines by guiding etl, transformation, orchestration, and streaming workflows; includes industry best practices, code templates, and validation to deliver production-ready jobs.

Automates Spark SQL optimizer tasks in data pipelines by guiding best practices, producing production-ready code and configurations, and validating ETL, transformation, orchestration, and streaming workflows.

Automates SQL query optimization guidance for data analytics workflows, offering best practices, pattern advice, and production-ready code for queries, BI, and visualization tasks whenever the user invokes SQL query optimizer support.

Automated guidance for SQL transform helper tasks within data pipelines, covering ETL, data transformation, workflow orchestration, and streaming, while following best practices, generating production-ready code, and validating results.

Auto-activating skill that guides tensorflow model trainer workflows, covering data preparation, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

Automates time series decomposition work in data analytics by guiding through patterns and best practices, generating production-ready code/configuration, and validating outputs for SQL, visualization, and BI workflows.

This comprehensive tool manages the complete lifecycle of ML experiments, providing robust deployment capabilities across diverse environments. It supports running training jobs on local GPUs, dedicated remote servers, Vast.ai, and serverless platforms like Modal. The workflow handles environment detection, code synchronization (rsync/git), dependency pinning, and integrates advanced logging tools (e.g., W&B), ensuring reliable, scalable, and reproducible ML training runs.

Astropy is a core Python library for astronomy and astrophysics workflows. It provides essential functionality for astronomical research and data analysis, including coordinate transformations, unit and quantity calculations, FITS file operations, cosmological calculations, precise time handling, tabular data manipulation, and astronomical image processing. Use it for implementing or debugging astronomical data analysis code.

This comprehensive guide demonstrates how to scrape live webpages using Bright Data's Web Unlocker proxy. It details how to bypass advanced anti-scraping mechanisms, such as CAPTCHAs and fingerprinting, automatically. Users are provided with full code examples in both Node.js and Python, showing methods for scraping via the proxy endpoint and through the dedicated REST API. Ideal for starting new scraping projects or integrating Bright Data into existing workflows.

A structured guide for setting up a robust local development environment for Palantir Foundry data pipelines. This workflow enables users to run Python transforms and unit tests using PySpark against sample data, and mock the Foundry API client, ensuring fast, isolated iteration cycles before deploying code to the official Foundry environment. Ideal for data engineers and developers building complex ETL logic.