Skill UI

This guide provides a comprehensive workflow for building robust ETL data pipelines within Palantir Foundry. It teaches how to utilize Python transforms, including the @transform_df decorator, for dataset-to-dataset processing. Key topics covered include multi-input joins, implementing incremental updates for efficiency, low-level file I/O, and configuring Spark resources for heavy computations. Essential for data engineers working with complex data modeling in Foundry.

This guide teaches best practices for configuring robust, enterprise-grade Role-Based Access Control (RBAC) within Palantir Foundry. Learn how to define project roles (Viewer, Editor, Owner), manage organization-level groups to enforce least privilege, establish secure service user accounts for external integrations, and utilize marking-based data classification to ensure data governance and security across large datasets.

A comprehensive, step-by-step guide demonstrating how to interact with Palantir Foundry using the official Python SDK. This example covers listing ontologies, querying object types, retrieving single records by primary key, reading datasets, and applying business actions, making it ideal for developers starting a new Foundry integration or learning core API patterns.

This guide provides advanced techniques to optimize interactions with the SalesLoft REST API v2. Key strategies include implementing LRU caching for frequently accessed data, utilizing incremental sync with `updated_at` filtering to avoid costly deep pagination, configuring connection pooling for stability, and running parallel reads safely. Use this when experiencing high latency, managing large datasets, or minimizing API usage costs for bulk operations.

This guide covers essential security best practices for utilizing Together AI's OpenAI-compatible API for inference, fine-tuning, and model deployment. It provides code examples and architectural advice on secure API key management, robust webhook signature verification, input validation using schemas (like Zod), and implementing data redaction pipelines to protect sensitive information in logs and datasets. Essential for building secure AI applications.