Skill UI

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 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.

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.

A comprehensive guide for executing major Palantir Foundry migrations. It covers data migration (bulk and incremental sync), API version upgrades, and re-platforming using advanced patterns like the Strangler Fig pattern. Use this when transitioning data from legacy systems into Foundry or upgrading existing platform integrations.

This guide provides advanced techniques for optimizing performance within Palantir Foundry. It covers efficient pagination, implementing client-side caching, using batch object retrieval (solving the N+1 problem), and tuning Spark transforms with memory configuration and incremental updates. Use this when experiencing slow API responses or inefficient data pipeline builds in Foundry.

This skill provides a robust solution for managing API rate limits, particularly crucial when integrating with enterprise platforms like Palantir Foundry. It implements advanced techniques including exponential backoff, jittered retries for handling 429/5xx errors, and a token bucket rate limiter for controlled batch throughput. Use this to ensure reliable data ingestion and bulk API operations.

This guide provides a comprehensive reference architecture for building production-ready, enterprise-grade applications on Palantir Foundry. It covers the entire data lifecycle, detailing best practices for the standard data pipeline (Raw > Clean > Model), Ontology design, external API integration patterns, and robust multi-layered security models. Use it when designing, planning, or optimizing complex data infrastructure within Foundry.

This guide provides production-ready patterns for integrating with the Palantir Foundry Platform SDK. It covers essential best practices like implementing thread-safe singletons, structured error handling, automatic pagination for large data sets, and robust retry mechanisms with exponential backoff. Ideal for refactoring existing Foundry integrations or establishing team coding standards.

This guide outlines comprehensive security best practices for integrating with Palantir Foundry. It covers secure credential storage (using secrets managers like AWS Secrets Manager or Vault), implementing the Principle of Least Privilege via OAuth2 scopes, and establishing robust credential rotation procedures. Use this when securing production environments, setting up new data pipelines, or auditing existing API access controls.

This comprehensive guide details the process of implementing a secure and robust system for handling Palantir Foundry webhook events. It covers key steps including API-based webhook registration, mandatory signature verification with replay protection, sophisticated event routing for various object lifecycles (create, update, delete), and ensuring idempotent processing using caching mechanisms like Redis. Use this when external microservices must react reliably and immediately to changes within the Foundry Ontology or data assets.

This skill integrates 10 major academic literature APIs (including PubMed, arXiv, OpenAlex, Crossref, etc.). It guides users on converting complex research intents into reproducible, verifiable lookups for papers, preprints, citations, and open-access full texts. It ensures results include verifiable provenance and follows a rigorous workflow for maximum accuracy in scholarly research.

This skill provides a structured guide for setting up CI/CD pipelines to test Persona identity verification integrations. It demonstrates how to use sandbox API credentials, simulate webhooks, and run comprehensive integration tests. It is essential for developers ensuring the reliability and security of KYC/identity services before deployment.