Skill UI

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 tool evaluates whether raw experimental results support predefined scientific claims or hypotheses. It collects data from multiple sources (W&B, logs, etc.) and uses an objective AI judgment (Codex) to determine the degree of support: fully supported, partially supported, or unsupported. It systematically identifies evidence gaps and suggests next steps, guiding the research workflow before drafting papers or making definitive conclusions.

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.