Skill UI

Transforms raw datasets into charts, plots, and graphs by analyzing structure, picking suitable visualization types, and rendering them clearly so users can request visualizations or explore patterns and relationships.

Transforms data into publication-ready figures with multi-panel layouts, precise annotations, colorblind-friendly palettes, and journal formatting using matplotlib, seaborn, and plotly plus export helpers for PDF/EPS/TIFF.

bdistill transforms AI interactions into a structured, queryable knowledge base. It extracts, structures, and quality-scores domain knowledge from both closed-source AI models (in-session) and local open-source models (via Ollama). Users can build comprehensive reference datasets, Q&A pairs, or generate structured training data for traditional machine learning, ensuring validated and exportable domain expertise.

Transforms data into publication-ready figures with multi-panel layouts, precise annotations, colorblind-friendly palettes, and journal formatting using matplotlib, seaborn, and plotly plus export helpers for PDF/EPS/TIFF.

This guide provides comprehensive technical instructions for optimizing Palantir Foundry costs. Learn to use incremental transforms to drastically reduce compute rebuilds, right-size Spark profiles based on data volume, and migrate from expensive API polling to efficient, event-driven webhooks, ensuring both cost efficiency and system stability.

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.